1
|
Gilmore N, Tseng CEJ, Maffei C, Tromly SL, Deary KB, McKinney IR, Kelemen JN, Healy BC, Hu CG, Ramos-Llordén G, Masood M, Cali RJ, Guo J, Belanger HG, Yao EF, Baxter T, Fischl B, Foulkes AS, Polimeni JR, Rosen BR, Perl DP, Hooker JM, Zürcher NR, Huang SY, Kimberly WT, Greve DN, Mac Donald CL, Dams-O'Connor K, Bodien YG, Edlow BL. Impact of repeated blast exposure on active-duty United States Special Operations Forces. Proc Natl Acad Sci U S A 2024; 121:e2313568121. [PMID: 38648470 DOI: 10.1073/pnas.2313568121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 03/22/2024] [Indexed: 04/25/2024] Open
Abstract
United States (US) Special Operations Forces (SOF) are frequently exposed to explosive blasts in training and combat, but the effects of repeated blast exposure (RBE) on SOF brain health are incompletely understood. Furthermore, there is no diagnostic test to detect brain injury from RBE. As a result, SOF personnel may experience cognitive, physical, and psychological symptoms for which the cause is never identified, and they may return to training or combat during a period of brain vulnerability. In 30 active-duty US SOF, we assessed the relationship between cumulative blast exposure and cognitive performance, psychological health, physical symptoms, blood proteomics, and neuroimaging measures (Connectome structural and diffusion MRI, 7 Tesla functional MRI, [11C]PBR28 translocator protein [TSPO] positron emission tomography [PET]-MRI, and [18F]MK6240 tau PET-MRI), adjusting for age, combat exposure, and blunt head trauma. Higher blast exposure was associated with increased cortical thickness in the left rostral anterior cingulate cortex (rACC), a finding that remained significant after multiple comparison correction. In uncorrected analyses, higher blast exposure was associated with worse health-related quality of life, decreased functional connectivity in the executive control network, decreased TSPO signal in the right rACC, and increased cortical thickness in the right rACC, right insula, and right medial orbitofrontal cortex-nodes of the executive control, salience, and default mode networks. These observations suggest that the rACC may be susceptible to blast overpressure and that a multimodal, network-based diagnostic approach has the potential to detect brain injury associated with RBE in active-duty SOF.
Collapse
Affiliation(s)
- Natalie Gilmore
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA 02114
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
| | - Chieh-En J Tseng
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129
| | - Chiara Maffei
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA 02114
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129
| | - Samantha L Tromly
- Institute of Applied Engineering, University of South Florida, Tampa, FL 33612
| | | | - Isabella R McKinney
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA 02114
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
| | - Jessica N Kelemen
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA 02114
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
| | - Brian C Healy
- Harvard T.H. Chan School of Public Health, Boston, MA 02115
| | - Collin G Hu
- United States Army Special Operations Aviation Command, Fort Liberty, NC 28307
- Department of Family Medicine, F. Edward Hebert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD 20814
| | - Gabriel Ramos-Llordén
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129
| | - Maryam Masood
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA 02114
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
| | - Ryan J Cali
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA 02114
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
| | - Jennifer Guo
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
| | - Heather G Belanger
- Department of Psychiatry and Behavioral Neurosciences, University of South Florida, Tampa, FL 33613
| | - Eveline F Yao
- Office of the Air Force Surgeon General, Falls Church, VA 22042
| | - Timothy Baxter
- Institute of Applied Engineering, University of South Florida, Tampa, FL 33612
| | - Bruce Fischl
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129
| | | | - Jonathan R Polimeni
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129
| | - Bruce R Rosen
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129
| | - Daniel P Perl
- Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD 20814
| | - Jacob M Hooker
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129
| | - Nicole R Zürcher
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129
| | - Susie Y Huang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129
| | - W Taylor Kimberly
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
| | - Douglas N Greve
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129
| | | | - Kristen Dams-O'Connor
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Yelena G Bodien
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA 02114
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA 02129
| | - Brian L Edlow
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA 02114
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129
| |
Collapse
|
2
|
Lee H, Fu JF, Gaudet K, Bryant AG, Price JC, Bennett RE, Johnson KA, Hyman BT, Hedden T, Salat DH, Yen YF, Huang SY. Aberrant vascular architecture in the hippocampus correlates with tau burden in mild cognitive impairment and Alzheimer's disease. J Cereb Blood Flow Metab 2024; 44:787-800. [PMID: 38000018 DOI: 10.1177/0271678x231216144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2023]
Abstract
Cerebrovascular dysfunction is a significant contributor to Alzheimer's disease (AD) progression. AD mouse models show altered capillary morphology, density, and diminished blood flow in areas of tau and beta-amyloid accumulation. The purpose of this study was to examine alterations in vascular structure and their contributions to perfusion deficits in the hippocampus in AD and mild cognitive impairment (MCI). Seven individuals with AD and MCI (1 AD/6 MCI), nine cognitively intact older healthy adults, and seven younger healthy adults underwent pseudo-continuous arterial spin labeling (PCASL) and gradient-echo/spin-echo (GESE) dynamic susceptibility contrast (DSC) MRI. Cerebral blood flow (CBF), cerebral blood volume, relative vessel size index (rVSI), and mean vessel density were calculated from model fitting. Lower CBF from PCASL and SE DSC MRI was observed in the hippocampus of AD/MCI group. rVSI in the hippocampus of the AD/MCI group was larger than that of the two healthy groups (FDR-P = 0.02). No difference in vessel density was detected between the groups. We also explored relationship of tau burden from 18F-flortaucipir positron emission tomography and vascular measures from MRI. Tau burden was associated with larger vessel size and lower CBF in the hippocampus. We postulate that larger vessel size may be associated with vascular alterations in AD/MCI.
Collapse
Affiliation(s)
- Hansol Lee
- Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Jessie Fanglu Fu
- Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Kyla Gaudet
- Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Annie G Bryant
- Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Julie C Price
- Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Rachel E Bennett
- Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Keith A Johnson
- Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
- Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Bradley T Hyman
- Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Trey Hedden
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David H Salat
- Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Yi-Fen Yen
- Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Susie Y Huang
- Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| |
Collapse
|
3
|
Li Z, Li Z, Bilgic B, Lee HH, Ying K, Huang SY, Liao H, Tian Q. DIMOND: DIffusion Model OptimizatioN with Deep Learning. Adv Sci (Weinh) 2024:e2307965. [PMID: 38634608 DOI: 10.1002/advs.202307965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 02/09/2024] [Indexed: 04/19/2024]
Abstract
Diffusion magnetic resonance imaging is an important tool for mapping tissue microstructure and structural connectivity non-invasively in the in vivo human brain. Numerous diffusion signal models are proposed to quantify microstructural properties. Nonetheless, accurate estimation of model parameters is computationally expensive and impeded by image noise. Supervised deep learning-based estimation approaches exhibit efficiency and superior performance but require additional training data and may be not generalizable. A new DIffusion Model OptimizatioN framework using physics-informed and self-supervised Deep learning entitled "DIMOND" is proposed to address this problem. DIMOND employs a neural network to map input image data to model parameters and optimizes the network by minimizing the difference between the input acquired data and synthetic data generated via the diffusion model parametrized by network outputs. DIMOND produces accurate diffusion tensor imaging results and is generalizable across subjects and datasets. Moreover, DIMOND outperforms conventional methods for fitting sophisticated microstructural models including the kurtosis and NODDI model. Importantly, DIMOND reduces NODDI model fitting time from hours to minutes, or seconds by leveraging transfer learning. In summary, the self-supervised manner, high efficacy, and efficiency of DIMOND increase the practical feasibility and adoption of microstructure and connectivity mapping in clinical and neuroscientific applications.
Collapse
Affiliation(s)
- Zihan Li
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, P. R. China
| | - Ziyu Li
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Harvard Medical School, Boston, MA, 02129, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Harvard Medical School, Boston, MA, 02129, USA
| | - Kui Ying
- Department of Engineering Physics, Tsinghua University, Beijing, 100084, P. R. China
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Harvard Medical School, Boston, MA, 02129, USA
| | - Hongen Liao
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, P. R. China
| | - Qiyuan Tian
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, P. R. China
| |
Collapse
|
4
|
Magdoom KN, Avram AV, Witzel TE, Huang SY, Basser PJ. Water Diffusion in the Live Human Brain is Gaussian at the Mesoscale. bioRxiv 2024:2024.04.10.588939. [PMID: 38645264 PMCID: PMC11030434 DOI: 10.1101/2024.04.10.588939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Imaging the live human brain at the mesoscopic scale is a desideratum in basic and clinical neurosciences. Despite the promise of diffusion MRI, the lack of an accurate model relating the measured signal and the associated microstructure has hampered its success. The widely used diffusion tensor MRI (DTI) model assumes an anisotropic Gaussian diffusion process in each voxel, but lacks the ability to capture intravoxel heterogeneity. This study explores the extension of the DTI model to mesoscopic length scales by use of the diffusion tensor distribution (DTD) model, which assumes a Gaussian diffusion process in each subvoxel. DTD MRI has shown promise in addressing some limitations of DTI, particularly in distinguishing among different types of brain cancers and elucidating multiple fiber populations within a voxel. However, its validity in live brain tissue has never been established. Here, multiple diffusion-encoded (MDE) data were acquired in the living human brain using a 3 Tesla MRI scanner with large diffusion weighting factors. Two different diffusion times (Δ = 37, 74 ms) were employed, with other scanning parameters fixed to assess signal decay differences. In vivo diffusion-weighted signals in gray and white matter were nearly identical at the two diffusion times. Fitting the signals to the DTD model yielded indistinguishable results, except in the cerebrospinal fluid (CSF)-filled voxels likely due to pulsatile flow. Overall, the study supports the time invariance of water diffusion at the mesoscopic scale in live brain parenchyma, extending the validity of the anisotropic Gaussian diffusion model in clinical brain imaging.
Collapse
|
5
|
Lang M, Clifford B, Lo WC, Applewhite BP, Tabari A, Filho ALMG, Hosseini Z, Longo MGF, Cauley SF, Setsompop K, Bilgic B, Feiweier T, Lev MH, Schaefer PW, Rapalino O, Huang SY, Conklin J. Clinical Evaluation of a 2-Minute Ultrafast Brain MR Protocol for Evaluation of Acute Pathology in the Emergency and Inpatient Settings. AJNR Am J Neuroradiol 2024; 45:379-385. [PMID: 38453413 DOI: 10.3174/ajnr.a8143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 12/07/2023] [Indexed: 03/09/2024]
Abstract
BACKGROUND AND PURPOSE The use of MR imaging in emergency settings has been limited by availability, long scan times, and sensitivity to motion. This study assessed the diagnostic performance of an ultrafast brain MR imaging protocol for evaluation of acute intracranial pathology in the emergency department and inpatient settings. MATERIALS AND METHODS Sixty-six adult patients who underwent brain MR imaging in the emergency department and inpatient settings were included in the study. All patients underwent both the reference and the ultrafast brain MR protocols. Both brain MR imaging protocols consisted of T1-weighted, T2/T2*-weighted, FLAIR, and DWI sequences. The ultrafast MR images were reconstructed by using a machine-learning assisted framework. All images were reviewed by 2 blinded neuroradiologists. RESULTS The average acquisition time was 2.1 minutes for the ultrafast brain MR protocol and 10 minutes for the reference brain MR protocol. There was 98.5% agreement on the main clinical diagnosis between the 2 protocols. In head-to-head comparison, the reference protocol was preferred in terms of image noise and geometric distortion (P < .05 for both). The ultrafast ms-EPI protocol was preferred over the reference protocol in terms of reduced motion artifacts (P < .01). Overall diagnostic quality was not significantly different between the 2 protocols (P > .05). CONCLUSIONS The ultrafast brain MR imaging protocol provides high accuracy for evaluating acute pathology while only requiring a fraction of the scan time. Although there was greater image noise and geometric distortion on the ultrafast brain MR protocol images, there was significant reduction in motion artifacts with similar overall diagnostic quality between the 2 protocols.
Collapse
Affiliation(s)
- Min Lang
- From the Department of Radiology (M.L., B.P.A., A.T., M.G.F.L., M.H.L., P.W.S., O.R., S.Y.H., J.C.), Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School (M.L., B.P.A., A.T., M.G.F.L., M.H.L., P.W.S., O.R., S.Y.H., J.C.), Boston, Massachusetts
| | - Bryan Clifford
- Siemens Medical Solutions (B.C., W.-C.L., Z.H., S.F.C.), Boston, Massachusetts
| | - Wei-Ching Lo
- Siemens Medical Solutions (B.C., W.-C.L., Z.H., S.F.C.), Boston, Massachusetts
| | - Brooks P Applewhite
- From the Department of Radiology (M.L., B.P.A., A.T., M.G.F.L., M.H.L., P.W.S., O.R., S.Y.H., J.C.), Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School (M.L., B.P.A., A.T., M.G.F.L., M.H.L., P.W.S., O.R., S.Y.H., J.C.), Boston, Massachusetts
| | - Azadeh Tabari
- From the Department of Radiology (M.L., B.P.A., A.T., M.G.F.L., M.H.L., P.W.S., O.R., S.Y.H., J.C.), Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School (M.L., B.P.A., A.T., M.G.F.L., M.H.L., P.W.S., O.R., S.Y.H., J.C.), Boston, Massachusetts
| | | | - Zahra Hosseini
- Siemens Medical Solutions (B.C., W.-C.L., Z.H., S.F.C.), Boston, Massachusetts
| | - Maria Gabriela Figueiro Longo
- From the Department of Radiology (M.L., B.P.A., A.T., M.G.F.L., M.H.L., P.W.S., O.R., S.Y.H., J.C.), Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School (M.L., B.P.A., A.T., M.G.F.L., M.H.L., P.W.S., O.R., S.Y.H., J.C.), Boston, Massachusetts
| | - Stephen F Cauley
- Siemens Medical Solutions (B.C., W.-C.L., Z.H., S.F.C.), Boston, Massachusetts
- Harvard-MIT Health Sciences and Technology (S.F.C., B.B., S.Y.H.), Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Kawin Setsompop
- Departments of Radiology and Electrical Engineering (K.S.), Stanford University, Stanford, California
| | - Berkin Bilgic
- Harvard-MIT Health Sciences and Technology (S.F.C., B.B., S.Y.H.), Massachusetts Institute of Technology, Cambridge, Massachusetts
| | | | - Michael H Lev
- From the Department of Radiology (M.L., B.P.A., A.T., M.G.F.L., M.H.L., P.W.S., O.R., S.Y.H., J.C.), Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School (M.L., B.P.A., A.T., M.G.F.L., M.H.L., P.W.S., O.R., S.Y.H., J.C.), Boston, Massachusetts
| | - Pamela W Schaefer
- From the Department of Radiology (M.L., B.P.A., A.T., M.G.F.L., M.H.L., P.W.S., O.R., S.Y.H., J.C.), Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School (M.L., B.P.A., A.T., M.G.F.L., M.H.L., P.W.S., O.R., S.Y.H., J.C.), Boston, Massachusetts
| | - Otto Rapalino
- From the Department of Radiology (M.L., B.P.A., A.T., M.G.F.L., M.H.L., P.W.S., O.R., S.Y.H., J.C.), Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School (M.L., B.P.A., A.T., M.G.F.L., M.H.L., P.W.S., O.R., S.Y.H., J.C.), Boston, Massachusetts
| | - Susie Y Huang
- From the Department of Radiology (M.L., B.P.A., A.T., M.G.F.L., M.H.L., P.W.S., O.R., S.Y.H., J.C.), Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School (M.L., B.P.A., A.T., M.G.F.L., M.H.L., P.W.S., O.R., S.Y.H., J.C.), Boston, Massachusetts
- Harvard-MIT Health Sciences and Technology (S.F.C., B.B., S.Y.H.), Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - John Conklin
- From the Department of Radiology (M.L., B.P.A., A.T., M.G.F.L., M.H.L., P.W.S., O.R., S.Y.H., J.C.), Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School (M.L., B.P.A., A.T., M.G.F.L., M.H.L., P.W.S., O.R., S.Y.H., J.C.), Boston, Massachusetts
| |
Collapse
|
6
|
Jang I, Li B, Rashid B, Jacoby J, Huang SY, Dickerson BC, Salat DH. Brain structural indicators of β-amyloid neuropathology. Neurobiol Aging 2024; 136:157-170. [PMID: 38382159 PMCID: PMC10938906 DOI: 10.1016/j.neurobiolaging.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 01/10/2024] [Accepted: 01/13/2024] [Indexed: 02/23/2024]
Abstract
Recent efforts demonstrated the efficacy of identifying early-stage neuropathology of Alzheimer's disease (AD) through lumbar puncture cerebrospinal fluid assessment and positron emission tomography (PET) radiotracer imaging. These methods are effective yet are invasive, expensive, and not widely accessible. We extend and improve the multiscale structural mapping (MSSM) procedure to develop structural indicators of β-amyloid neuropathology in preclinical AD, by capturing both macrostructural and microstructural properties throughout the cerebral cortex using a structural MRI. We find that the MSSM signal is regionally altered in clear positive and negative cases of preclinical amyloid pathology (N = 220) when cortical thickness alone or hippocampal volume is not. It exhibits widespread effects of amyloid positivity across the posterior temporal, parietal, and medial prefrontal cortex, surprisingly consistent with the typical pattern of amyloid deposition. The MSSM signal is significantly correlated with amyloid PET in almost half of the cortex, much of which overlaps with regions where beta-amyloid accumulates, suggesting it could provide a regional brain 'map' that is not available from systemic markers such as plasma markers.
Collapse
Affiliation(s)
- Ikbeom Jang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, South Korea.
| | - Binyin Li
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Barnaly Rashid
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - John Jacoby
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bradford C Dickerson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - David H Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, USA
| |
Collapse
|
7
|
Dong Z, Reese TG, Lee HH, Huang SY, Polimeni JR, Wald LL, Wang F. Romer-EPTI: rotating-view motion-robust super-resolution EPTI for SNR-efficient distortion-free in-vivo mesoscale dMRI and microstructure imaging. bioRxiv 2024:2024.01.26.577343. [PMID: 38352481 PMCID: PMC10862730 DOI: 10.1101/2024.01.26.577343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Purpose To overcome the major challenges in dMRI acquisition, including low SNR, distortion/blurring, and motion vulnerability. Methods A novel Romer-EPTI technique is developed to provide distortion-free dMRI with significant SNR gain, high motion-robustness, sharp spatial resolution, and simultaneous multi-TE imaging. It introduces a ROtating-view Motion-robust supEr-Resolution technique (Romer) combined with a distortion/blurring-free EPTI encoding. Romer enhances SNR by a simultaneous multi-thick-slice acquisition with rotating-view encoding, while providing high motion-robustness through a motion-aware super-resolution reconstruction, which also incorporates slice-profile and real-value diffusion, to resolve high-isotropic-resolution volumes. The in-plane encoding is performed using distortion/blurring-free EPTI, which further improves effective spatial resolution and motion robustness by preventing not only T2/T2*-blurring but also additional blurring resulting from combining encoded volumes with inconsistent geometries caused by dynamic distortions. Self-navigation was incorporated to enable efficient phase correction. Additional developments include strategies to address slab-boundary artifacts, achieve minimal TE for SNR gain at 7T, and achieve high robustness to strong phase variations at high b-values. Results Using Romer-EPTI, we demonstrate distortion-free whole-brain mesoscale in-vivo dMRI at both 3T (500-μm-iso) and 7T (485-μm-iso) for the first time, with high SNR efficiency (e.g., 25 × ), and high image quality free from distortion and slab-boundary artifacts with minimal blurring. Motion experiments demonstrate Romer-EPTI's high motion-robustness and ability to recover sharp images in the presence of motion. Romer-EPTI also demonstrates significant SNR gain and robustness in high b-value (b=5000s/mm2) and time-dependent dMRI. Conclusion Romer-EPTI significantly improves SNR, motion-robustness, and image quality, providing a highly efficient acquisition for high-resolution dMRI and microstructure imaging.
Collapse
Affiliation(s)
- Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Timothy G. Reese
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Jonathan R. Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Lawrence L. Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
8
|
Lee HH, Tian Q, Sheft M, Coronado-Leija R, Ramos-Llorden G, Abdollahzadeh A, Fieremans E, Novikov DS, Huang SY. The effects of axonal beading and undulation on axonal diameter estimation from diffusion MRI: Insights from simulations in human axons segmented from three-dimensional electron microscopy. NMR Biomed 2024; 37:e5087. [PMID: 38168082 PMCID: PMC10942763 DOI: 10.1002/nbm.5087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 11/19/2023] [Accepted: 11/21/2023] [Indexed: 01/05/2024]
Abstract
The increasing availability of high-performance gradient systems in human MRI scanners has generated great interest in diffusion microstructural imaging applications such as axonal diameter mapping. Practically, sensitivity to axon diameter in diffusion MRI is attained at strong diffusion weightings b , where the deviation from the expected 1 / b scaling in white matter yields a finite transverse diffusivity, which is then translated into an axon diameter estimate. While axons are usually modeled as perfectly straight, impermeable cylinders, local variations in diameter (caliber variation or beading) and direction (undulation) are known to influence axonal diameter estimates and have been observed in microscopy data of human axons. In this study, we performed Monte Carlo simulations of diffusion in axons reconstructed from three-dimensional electron microscopy of a human temporal lobe specimen using simulated sequence parameters matched to the maximal gradient strength of the next-generation Connectome 2.0 human MRI scanner ( ≲ 500 mT/m). We show that axon diameter estimation is accurate for nonbeaded, nonundulating fibers; however, in fibers with caliber variations and undulations, the axon diameter is heavily underestimated due to caliber variations, and this effect overshadows the known overestimation of the axon diameter due to undulations. This unexpected underestimation may originate from variations in the coarse-grained axial diffusivity due to caliber variations. Given that increased axonal beading and undulations have been observed in pathological tissues, such as traumatic brain injury and ischemia, the interpretation of axon diameter alterations in pathology may be significantly confounded.
Collapse
Affiliation(s)
- Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Maxina Sheft
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard–MIT Health Sciences and Technology, Cambridge, Massachusetts, USA
| | - Ricardo Coronado-Leija
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Gabriel Ramos-Llorden
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Ali Abdollahzadeh
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Dmitry S. Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
9
|
Hoebel KV, Bridge CP, Kim A, Gerstner ER, Ly IK, Deng F, DeSalvo MN, Dietrich J, Huang R, Huang SY, Pomerantz SR, Vagvala S, Rosen BR, Kalpathy-Cramer J. Not without Context-A Multiple Methods Study on Evaluation and Correction of Automated Brain Tumor Segmentations by Experts. Acad Radiol 2024; 31:1572-1582. [PMID: 37951777 DOI: 10.1016/j.acra.2023.10.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/06/2023] [Accepted: 10/07/2023] [Indexed: 11/14/2023]
Abstract
RATIONALE AND OBJECTIVES Brain tumor segmentations are integral to the clinical management of patients with glioblastoma, the deadliest primary brain tumor in adults. The manual delineation of tumors is time-consuming and highly provider-dependent. These two problems must be addressed by introducing automated, deep-learning-based segmentation tools. This study aimed to identify criteria experts use to evaluate the quality of automatically generated segmentations and their thought processes as they correct them. MATERIALS AND METHODS Multiple methods were used to develop a detailed understanding of the complex factors that shape experts' perception of segmentation quality and their thought processes in correcting proposed segmentations. Data from a questionnaire and semistructured interview with neuro-oncologists and neuroradiologists were collected between August and December 2021 and analyzed using a combined deductive and inductive approach. RESULTS Brain tumors are highly complex and ambiguous segmentation targets. Therefore, physicians rely heavily on the given context related to the patient and clinical context in evaluating the quality and need to correct brain tumor segmentation. Most importantly, the intended clinical application determines the segmentation quality criteria and editing decisions. Physicians' personal beliefs and preferences about the capabilities of AI algorithms and whether questionable areas should not be included are additional criteria influencing the perception of segmentation quality and appearance of an edited segmentation. CONCLUSION Our findings on experts' perceptions of segmentation quality will allow the design of improved frameworks for expert-centered evaluation of brain tumor segmentation models. In particular, the knowledge presented here can inspire the development of brain tumor-specific metrics for segmentation model training and evaluation.
Collapse
Affiliation(s)
- Katharina V Hoebel
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Harvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Christopher P Bridge
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Albert Kim
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Stephen E. and Catherine Pappas Center for Neuro-Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Elizabeth R Gerstner
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Stephen E. and Catherine Pappas Center for Neuro-Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Ina K Ly
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Stephen E. and Catherine Pappas Center for Neuro-Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Francis Deng
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Massachusetts
| | - Matthew N DeSalvo
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jorg Dietrich
- Department of Neurology, Division of Neuro-Oncology, Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, Massachusetts
| | - Raymond Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Susie Y Huang
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts
| | - Stuart R Pomerantz
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Saivenkat Vagvala
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Bruce R Rosen
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts
| | - Jayashree Kalpathy-Cramer
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Department of Ophthalmology, University of Colorado Anschutz Medical Campus, 1675 Aurora Court, Mail Stop F731, Aurora, CO.
| |
Collapse
|
10
|
Ma QM, Tang WB, Li XJ, Chang F, Yin X, Chen ZH, Wu GH, Xia CD, Li XL, Wang DY, Chu ZG, Zhang Y, Wang L, Wu CL, Tong YL, Cui P, Guo GH, Zhu ZH, Huang SY, Chang L, Liu R, Liu YJ, Wang YS, Liu XB, Shen T, Zhu F. [Multicenter retrospect analysis of early clinical features and analysis of risk factors on prognosis of elderly patients with severe burns]. Zhonghua Shao Shang Yu Chuang Mian Xiu Fu Za Zhi 2024; 40:249-257. [PMID: 38548395 DOI: 10.3760/cma.j.cn501225-20230808-00042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Objective: To investigate the early clinical characteristics of elderly patients with severe burns and the risk factors on prognosis. Methods: This study was a retrospective case series study. Clinical data of 124 elderly patients with severe burns who met the inclusion criteria and were admitted to the 12 hospitals from January 2015 to December 2020 were collected, including 4 patients from the Fourth People's Hospital of Dalian, 5 patients from Fujian Medical University Union Hospital, 22 patients from Guangzhou Red Cross Hospital of Jinan University, 5 patients from Heilongjiang Provincial Hospital, 27 patients from the First Affiliated Hospital of Naval Medical University, 9 patients from the First Affiliated Hospital of Nanchang University, 10 patients from Affiliated Hospital of Nantong University, 9 patients from Tongren Hospital of Wuhan University & Wuhan Third Hospital, 12 patients from the 924th Hospital of PLA, 6 patients from Zhangjiagang First People's Hospital, 4 patients from Taizhou Hospital of Zhejiang Province, and 11 patients from Zhengzhou First People's Hospital. The patients' overall clinical characteristics, such as gender, age, body mass index, total burn area, full-thickness burn area, inhalation injury, causative factors, whether combined with underlying medical diseases, and admission time after injury were recorded. According to the survival outcome within 28 days after injury, the patients were divided into survival group (89 cases) and death group (35 cases). The following data of patients were compared between the two groups, including the basic data and injuries (the same as the overall clinical characteristics ahead); the coagulation indexes within the first 24 hours of injury such as prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time, D-dimer, fibrinogen degradation product (FDP), international normalized ratio (INR), and fibrinogen; the blood routine indexes within the first 24 hours of injury such as white blood cell count, platelet count, neutrophil-to-lymphocyte ratio, monocyte count, red blood cell count, hemoglobin, and hematocrit; the organ function indexes within the first 24 hours of injury such as direct bilirubin, total bilirubin, urea, serum creatinine, aspartate aminotransferase, alanine aminotransferase, total protein, albumin, globulin, blood glucose, triglyceride, total cholesterol, alkaline phosphatase, creatine kinase, electrolyte indexes (potassium, sodium, chlorine, calcium, magnesium, and phosphorus in blood), uric acid, myoglobin, and brain natriuretic peptide; the infection and blood gas indexes within the first 24 hours of injury such as procalcitonin, C-reactive protein, pH value, oxygenation index, base excess, and lactate; treatment such as whether conducted with mechanical ventilation, whether conducted with continuous renal replacement therapy, whether conducted with anticoagulation therapy, whether applied with vasoactive drugs, and fluid resuscitation. The analysis was conducted to screen the independent risk factors for the mortality within 28 days after injury in elderly patients with severe burns. Results: Among 124 patients, there were 82 males and 42 females, aged 60-97 years, with body mass index of 23.44 (21.09, 25.95) kg/m2, total burn area of 54.00% (42.00%, 75.00%) total body surface area (TBSA), and full-thickness burn area of 25.00% (10.00%, 40.00%) TBSA. The patients were mainly combined with moderate to severe inhalation injury and caused by flame burns. There were 43 cases with underlying medical diseases. The majority of patients were admitted to the hospital within 8 hours after injury. There were statistically significant differences between patients in the 2 groups in terms of age, total burn area, full-thickness burn area, and inhalation injury, and PT, APTT, D-dimer, FDP, INR, white blood cell count, platelet count, urea, serum creatinine, blood glucose, blood sodium, uric acid, myoglobin, and urine volume within the first 24 hours of injury (with Z values of 2.37, 5.49, 5.26, 5.97, 2.18, 1.95, 2.68, 2.68, 2.51, 2.82, 2.14, 3.40, 5.31, 3.41, 2.35, 3.81, 2.16, and -3.82, respectively, P<0.05); there were statistically significant differences between two groups of patients in whether conducted with mechanical ventilation and whether applied with vasoactive drugs (with χ2 values of 9.44 and 28.50, respectively, P<0.05). Age, total burn area, full-thickness burn area, serum creatinine within the first 24 hours of injury, and APTT within the first 24 hours of injury were the independent risk factors for the mortality within 28 days after injury in elderly patients with severe burns (with odds ratios of 1.17, 1.10, 1.10, 1.09, and 1.27, 95% confidence intervals of 1.03-1.40, 1.04-1.21, 1.05-1.19, 1.05-1.17, and 1.07-1.69, respectively, P<0.05). Conclusions: The elderly patients with severe burns had the injuries mainly from flame burns, often accompanied by moderate to severe inhalation injury and enhanced inflammatory response, elevated blood glucose levels, activated fibrinolysis, and impaired organ function in the early stage, which are associated with their prognosis. Age, total burn area, full-thickness burn area, and serum creatinine and APTT within the first 24 hours of injury are the independent risk factors for death within 28 days after injury in this population.
Collapse
Affiliation(s)
- Q M Ma
- Department of Burn Surgery, the First Affiliated Hospital of Naval Medical University, Shanghai 200433, China Department of Critical Care Medicine, Shanghai East Hospital Affiliated to Tongji University, Shanghai 200120, China
| | - W B Tang
- Department of Burns and Plastic Surgery, Guangzhou Red Cross Hospital of Jinan University, Guangzhou 510220, China
| | - X J Li
- Department of Burns and Plastic Surgery, Guangzhou Red Cross Hospital of Jinan University, Guangzhou 510220, China
| | - F Chang
- Department of Burns and Plastic Surgery, Zhangjiagang First People's Hospital, Zhangjiagang 215600, China
| | - X Yin
- Department of Burns and Plastic Surgery, Zhangjiagang First People's Hospital, Zhangjiagang 215600, China
| | - Z H Chen
- Department of Burns, Fujian Medical University Union Hospital, Fuzhou 350001, China
| | - G H Wu
- Department of Burns, Fujian Medical University Union Hospital, Fuzhou 350001, China
| | - C D Xia
- Department of Burns, Zhengzhou First People's Hospital, Zhengzhou 450004, China
| | - X L Li
- Department of Burns, Zhengzhou First People's Hospital, Zhengzhou 450004, China
| | - D Y Wang
- Department of Burns, Tongren Hospital of Wuhan University & Wuhan Third Hospital, Wuhan 430060, China
| | - Z G Chu
- Department of Burns, Tongren Hospital of Wuhan University & Wuhan Third Hospital, Wuhan 430060, China
| | - Y Zhang
- Department of Burns and Plastic Surgery, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - L Wang
- Department of Burns and Plastic Surgery, Affiliated Hospital of Nantong University, Nantong 226001, China
| | - C L Wu
- Department of Burns, Taizhou Hospital of Zhejiang Province, Linhai 317000, China
| | - Y L Tong
- Department of Burns and Plastic Surgery, the 924th Hospital of PLA, Guilin 541002, China
| | - P Cui
- Department of Burns and Plastic Surgery, the 924th Hospital of PLA, Guilin 541002, China
| | - G H Guo
- Medical Center of Burn Plastic and Wound Repair, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - Z H Zhu
- Medical Center of Burn Plastic and Wound Repair, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - S Y Huang
- Medical Center of Burn Plastic and Wound Repair, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - L Chang
- Department of Burns and Plastic Surgery, the Fourth People's Hospital of Dalian, Dalian 116031, China
| | - R Liu
- Department of Burns, Heilongjiang Provincial Hospital, Harbin 150036, China
| | - Y J Liu
- Department of Burns, Heilongjiang Provincial Hospital, Harbin 150036, China
| | - Y S Wang
- Department of Burn Surgery, the First Affiliated Hospital of Naval Medical University, Shanghai 200433, China
| | - X B Liu
- Department of Critical Care Medicine, Shanghai East Hospital Affiliated to Tongji University, Shanghai 200120, China
| | - T Shen
- Department of Critical Care Medicine, Shanghai East Hospital Affiliated to Tongji University, Shanghai 200120, China
| | - F Zhu
- Department of Critical Care Medicine, Shanghai East Hospital Affiliated to Tongji University, Shanghai 200120, China
| |
Collapse
|
11
|
Ramos-Llordén G, Park DJ, Kirsch JE, Scholz A, Keil B, Maffei C, Lee HH, Bilgic B, Edlow BL, Mekkaoui C, Yendiki A, Witzel T, Huang SY. Eddy current-induced artifact correction in high b-value ex vivo human brain diffusion MRI with dynamic field monitoring. Magn Reson Med 2024; 91:541-557. [PMID: 37753621 PMCID: PMC10842131 DOI: 10.1002/mrm.29873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 08/30/2023] [Accepted: 09/02/2023] [Indexed: 09/28/2023]
Abstract
PURPOSE To investigate whether spatiotemporal magnetic field monitoring can correct pronounced eddy current-induced artifacts incurred by strong diffusion-sensitizing gradients up to 300 mT/m used in high b-value diffusion-weighted (DW) EPI. METHODS A dynamic field camera equipped with 16 1 H NMR field probes was first used to characterize field perturbations caused by residual eddy currents from diffusion gradients waveforms in a 3D multi-shot EPI sequence on a 3T Connectom scanner for different gradient strengths (up to 300 mT/m), diffusion directions, and shots. The efficacy of dynamic field monitoring-based image reconstruction was demonstrated on high-gradient strength, submillimeter resolution whole-brain ex vivo diffusion MRI. A 3D multi-shot image reconstruction framework was developed that incorporated the nonlinear phase evolution measured with the dynamic field camera. RESULTS Phase perturbations in the readout induced by residual eddy currents from strong diffusion gradients are highly nonlinear in space and time, vary among diffusion directions, and interfere significantly with the image encoding gradients, changing the k-space trajectory. During the readout, phase modulations between odd and even EPI echoes become non-static and diffusion encoding direction-dependent. Superior reduction of ghosting and geometric distortion was achieved with dynamic field monitoring compared to ghosting reduction approaches such as navigator- and structured low-rank-based methods or MUSE followed by image-based distortion correction with the FSL tool "eddy." CONCLUSION Strong eddy current artifacts characteristic of high-gradient strength DW-EPI can be well corrected with dynamic field monitoring-based image reconstruction.
Collapse
Affiliation(s)
- Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Daniel J Park
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - John E Kirsch
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Alina Scholz
- Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Sciences, Giessen, Germany
| | - Boris Keil
- Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Sciences, Giessen, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Marburg, Philipps University of Marburg, Marburg, Germany
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Brian L Edlow
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Choukri Mekkaoui
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | | | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| |
Collapse
|
12
|
Awan KM, Goncalves Filho ALM, Tabari A, Applewhite BP, Lang M, Lo WC, Sellers R, Kollasch P, Clifford B, Nickel D, Husseni J, Rapalino O, Schaefer P, Cauley S, Huang SY, Conklin J. Diagnostic evaluation of deep learning accelerated lumbar spine MRI. Neuroradiol J 2024:19714009231224428. [PMID: 38195418 DOI: 10.1177/19714009231224428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND AND PURPOSE Deep learning (DL) accelerated MR techniques have emerged as a promising approach to accelerate routine MR exams. While prior studies explored DL acceleration for specific lumbar MRI sequences, a gap remains in comprehending the impact of a fully DL-based MRI protocol on scan time and diagnostic quality for routine lumbar spine MRI. To address this, we assessed the image quality and diagnostic performance of a DL-accelerated lumbar spine MRI protocol in comparison to a conventional protocol. METHODS We prospectively evaluated 36 consecutive outpatients undergoing non-contrast enhanced lumbar spine MRIs. Both protocols included sagittal T1, T2, STIR, and axial T2-weighted images. Two blinded neuroradiologists independently reviewed images for foraminal stenosis, spinal canal stenosis, nerve root compression, and facet arthropathy. Grading comparison employed the Wilcoxon signed rank test. For the head-to-head comparison, a 5-point Likert scale to assess image quality, considering artifacts, signal-to-noise ratio (SNR), anatomical structure visualization, and overall diagnostic quality. We applied a 15% noninferiority margin to determine whether the DL-accelerated protocol was noninferior. RESULTS No significant differences existed between protocols when evaluating foraminal and spinal canal stenosis, nerve compression, or facet arthropathy (all p > .05). The DL-spine protocol was noninferior for overall diagnostic quality and visualization of the cord, CSF, intervertebral disc, and nerve roots. However, it exhibited reduced SNR and increased artifact perception. Interobserver reproducibility ranged from moderate to substantial (κ = 0.50-0.76). CONCLUSION Our study indicates that DL reconstruction in spine imaging effectively reduces acquisition times while maintaining comparable diagnostic quality to conventional MRI.
Collapse
Affiliation(s)
- Komal M Awan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard Medical School, USA
| | | | - Azadeh Tabari
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard Medical School, USA
| | - Brooks P Applewhite
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard Medical School, USA
| | - Min Lang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard Medical School, USA
| | | | | | | | | | | | - Jad Husseni
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard Medical School, USA
| | - Otto Rapalino
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard Medical School, USA
| | - Pamela Schaefer
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard Medical School, USA
| | | | - Susie Y Huang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard Medical School, USA
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, USA
| | - John Conklin
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard Medical School, USA
| |
Collapse
|
13
|
Edlow BL, Gilmore N, Tromly SL, Deary KB, McKinney IR, Hu CG, Kelemen JN, Maffei C, Tseng CEJ, Llorden GR, Healy BC, Masood M, Cali RJ, Baxter T, Yao EF, Belanger HG, Benjamini D, Basser PJ, Priemer DS, Kimberly WT, Polimeni JR, Rosen BR, Fischl B, Zurcher NR, Greve DN, Hooker JM, Huang SY, Caruso A, Smith GA, Szymanski TG, Perl DP, Dams-O'Connor K, Mac Donald CL, Bodien YG. Optimizing Brain Health of United States Special Operations Forces. J Spec Oper Med 2023; 23:47-56. [PMID: 37851859 DOI: 10.55460/99qw-k0hg] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/01/2023] [Indexed: 10/20/2023]
Abstract
United States Special Operations Forces (SOF) personnel are frequently exposed to explosive blasts in training and combat. However, the effects of repeated blast exposure on the human brain are incompletely understood. Moreover, there is currently no diagnostic test to detect repeated blast brain injury (rBBI). In this "Human Performance Optimization" article, we discuss how the development and implementation of a reliable diagnostic test for rBBI has the potential to promote SOF brain health, combat readiness, and quality of life.
Collapse
|
14
|
Chiang CC, Lee HC, Lin SC, Qu D, Chu MW, Chen CD, Chien CL, Huang SY. Unequivocal Identification of Spin-Triplet and Spin-Singlet Superconductors with Upper Critical Field and Flux Quantization. Phys Rev Lett 2023; 131:236003. [PMID: 38134800 DOI: 10.1103/physrevlett.131.236003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 11/13/2023] [Indexed: 12/24/2023]
Abstract
Spin-triplet superconductors play central roles in Majorana physics and quantum computing but are difficult to identify. We show the methods of kink-point upper critical field and flux quantization in superconducting rings can unequivocally identify spin-singlet, spin-triplet in centrosymmetric superconductors, and singlet-triplet admixture in noncentrosymmetric superconductors, as realized in γ-BiPd, β-Bi_{2}Pd, and α-BiPd, respectively. Our findings are essential for identifying triplet superconductors and exploring their quantum properties.
Collapse
Affiliation(s)
- C C Chiang
- Department of Physics, National Taiwan University, Taipei 10617, Taiwan
- Willian H. Miller III Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - H C Lee
- Department of Physics, National Taiwan University, Taipei 10617, Taiwan
| | - S C Lin
- Center for Condensed Matter Sciences, National Taiwan University, Taipei 10617, Taiwan
| | - D Qu
- Center for Condensed Matter Sciences, National Taiwan University, Taipei 10617, Taiwan
- Center of Atomic Initiatives for New Materials, National Taiwan University, Taipei 10617, Taiwan
| | - M W Chu
- Center for Condensed Matter Sciences, National Taiwan University, Taipei 10617, Taiwan
- Center of Atomic Initiatives for New Materials, National Taiwan University, Taipei 10617, Taiwan
| | - C D Chen
- Institute of Physics, Academia Sinica, Taipei 11529, Taiwan
| | - C L Chien
- Department of Physics, National Taiwan University, Taipei 10617, Taiwan
- Willian H. Miller III Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - S Y Huang
- Department of Physics, National Taiwan University, Taipei 10617, Taiwan
- Center of Atomic Initiatives for New Materials, National Taiwan University, Taipei 10617, Taiwan
| |
Collapse
|
15
|
Szvalb AD, Marten C, Cain K, Taylor JS, Huang SY, Jiang Y, Raad II, Viola GM. Percutaneous nephrostomy catheter-related infections in patients with gynaecological cancers: a multidisciplinary algorithmic approach. J Hosp Infect 2023; 141:99-106. [PMID: 37696471 DOI: 10.1016/j.jhin.2023.08.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 09/13/2023]
Abstract
BACKGROUND Percutaneous nephrostomy catheters (PCNs) are commonly utilized in patients with gynaecological cancers due to intrinsic or extrinsic urinary obstruction. Unfortunately, these foreign medical devices may be associated with several infectious complications, including: pyelonephritis, renal abscess, and bacteraemia, which may lead to further delay of life-saving cancer therapy. AIM To evaluate the performance of our multidisciplinary algorithm for diagnosis and treatment of PCN-related infections (PCNIs) and identify risk factors for recurrent urinary device-related infections. METHODS Patients with gynaecological cancers having PCNIs were prospectively evaluated at our institution from July 2019 to September 2021. All patients were managed by our standardized algorithm and followed-up until reinfection or routine PCN exchange. FINDINGS Of 100 consecutive patients with PCNIs, 74 had adequate follow-up, and were analysed in three groups according to clinical outcome: reinfection with the same organism (26%), reinfection with a different organism (23%), and no reinfection (51%). Their median age was 54 years, and the most common cancers were cervical (65%), and ovarian (19%) with 53% being metastatic. The most frequently recovered micro-organisms were Pseudomonas (32%), Enterococcus (27%), and Escherichia (24%) species. The main risk factors for recurrent PCNI with the same organism were pelvic radiation therapy (P=0.032), pelvic fistulas (P=0.014), and a PCNI with the same pathogen within the previous year (P = 0.012). CONCLUSIONS Our algorithm has allowed for accurate diagnosis, staging, and treatment of and identification of several key risk factors for recurrent PCNIs. These results may lead to further preventive measures for these infections.
Collapse
Affiliation(s)
- A D Szvalb
- Department of Infectious Diseases, Infection Control, and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - C Marten
- Department of Pharmacy Clinical Programs, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - K Cain
- Department of Pharmacy Clinical Programs, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - J S Taylor
- Department of Gynecologic Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - S Y Huang
- Department of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Y Jiang
- Department of Infectious Diseases, Infection Control, and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - I I Raad
- Department of Infectious Diseases, Infection Control, and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - G M Viola
- Department of Infectious Diseases, Infection Control, and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| |
Collapse
|
16
|
Huang SY, Salomon M, Eikermann-Haerter K. Advanced brain MRI may help understand the link between migraine and multiple sclerosis. J Headache Pain 2023; 24:113. [PMID: 37596546 PMCID: PMC10439604 DOI: 10.1186/s10194-023-01645-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 08/04/2023] [Indexed: 08/20/2023] Open
Abstract
BACKGROUND There is a clinical association between migraine and multiple sclerosis. MAIN BODY Migraine and MS patients share similar demographics, with the highest incidence among young, female and otherwise healthy patients. The same hormonal constellations/changes trigger disease exacerbation in both entities. Migraine prevalence is increased in MS patients, which is further enhanced by disease-modifying treatment. Clinical data show that onset of migraine typically starts years before the clinical diagnosis of MS, suggesting that there is either a unidirectional relationship with migraine predisposing to MS, and/or a "shared factor" underlying both conditions. Brain imaging studies show white matter lesions in both MS and migraine patients. Neuroinflammatory mechanisms likely play a key role, at least as a shared downstream pathway. In this review article, we provide an overview of the literature about 1) the clinical association between migraine and MS as well as 2) brain MRI studies that help us better understand the mechanistic relationship between both diseases with implications on their underlying pathophysiology. CONCLUSION Studies suggest a migraine history predisposes patients to develop MS. Advanced brain MR imaging may shed light on shared and distinct features, while helping us better understand mechanisms underlying both disease entities.
Collapse
Affiliation(s)
- Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Marc Salomon
- Department of Radiology, New York University Langone Medical Center, 660 First Ave, New York, NY, 10016, USA
| | - Katharina Eikermann-Haerter
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, New York University Langone Medical Center, 660 First Ave, New York, NY, 10016, USA.
| |
Collapse
|
17
|
Tabari A, Lang M, Awan K, Liu W, Clifford B, Lo WC, Splitthoff DN, Cauley S, Rapalino O, Schaefer P, Huang SY, Conklin J. Optimized flow compensation for contrast-enhanced T1-weighted Wave-CAIPI 3D MPRAGE imaging of the brain. Eur Radiol Exp 2023; 7:34. [PMID: 37394534 DOI: 10.1186/s41747-023-00351-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 04/25/2023] [Indexed: 07/04/2023] Open
Abstract
Flow-related artifacts have been observed in highly accelerated T1-weighted contrast-enhanced wave-controlled aliasing in parallel imaging (CAIPI) magnetization-prepared rapid gradient-echo (MPRAGE) imaging and can lead to diagnostic uncertainty. We developed an optimized flow-mitigated Wave-CAIPI MPRAGE acquisition protocol to reduce these artifacts through testing in a custom-built flow phantom. In the phantom experiment, maximal flow artifact reduction was achieved with the combination of flow compensation gradients and radial reordered k-space acquisition and was included in the optimized sequence. Clinical evaluation of the optimized MPRAGE sequence was performed in 64 adult patients, who all underwent contrast-enhanced Wave-CAIPI MPRAGE imaging without flow-compensation and with optimized flow-compensation parameters. All images were evaluated for the presence of flow-related artifacts, signal-to-noise ratio (SNR), gray-white matter contrast, enhancing lesion contrast, and image sharpness on a 3-point Likert scale. In the 64 cases, the optimized flow mitigation protocol reduced flow-related artifacts in 89% and 94% of the cases for raters 1 and 2, respectively. SNR, gray-white matter contrast, enhancing lesion contrast, and image sharpness were rated as equivalent for standard and flow-mitigated Wave-CAIPI MPRAGE in all subjects. The optimized flow mitigation protocol successfully reduced the presence of flow-related artifacts in the majority of cases.Relevance statementAs accelerated MRI using novel encoding schemes become increasingly adopted in clinical practice, our work highlights the need to recognize and develop strategies to minimize the presence of unexpected artifacts and reduction in image quality as potential compromises to achieving short scan times.Key points• Flow-mitigation technique led to an 89-94% decrease in flow-related artifacts.• Image quality, signal-to-noise ratio, enhancing lesion conspicuity, and image sharpness were preserved with the flow mitigation technique.• Flow mitigation reduced diagnostic uncertainty in cases where flow-related artifacts mimicked enhancing lesions.
Collapse
Affiliation(s)
- Azadeh Tabari
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 55 Fruit Street, Charlestown, Boston, MA, 02114, USA.
- Harvard Medical School, Boston, MA, USA.
| | - Min Lang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 55 Fruit Street, Charlestown, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, USA
| | - Komal Awan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 55 Fruit Street, Charlestown, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, USA
| | - Wei Liu
- Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | | | | | | | - Stephen Cauley
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 55 Fruit Street, Charlestown, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, USA
| | - Otto Rapalino
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 55 Fruit Street, Charlestown, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, USA
| | - Pamela Schaefer
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 55 Fruit Street, Charlestown, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, USA
| | - Susie Y Huang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 55 Fruit Street, Charlestown, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, USA
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - John Conklin
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 55 Fruit Street, Charlestown, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, USA
| |
Collapse
|
18
|
Krijnen EA, Russo AW, Salim Karam E, Lee H, Chiang FL, Schoonheim MM, Huang SY, Klawiter EC. Detection of grey matter microstructural substrates of neurodegeneration in multiple sclerosis. Brain Commun 2023; 5:fcad153. [PMID: 37274832 PMCID: PMC10233898 DOI: 10.1093/braincomms/fcad153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/16/2023] [Accepted: 05/22/2023] [Indexed: 06/07/2023] Open
Abstract
Multiple sclerosis features complex pathological changes in grey matter that begin early and eventually lead to diffuse atrophy. Novel approaches to image grey-matter microstructural alterations in vivo are highly sought after and would enable more sensitive monitoring of disease activity and progression. This cross-sectional study aimed to assess the sensitivity of high-gradient diffusion MRI for microstructural tissue damage in cortical and deep grey matter in people with multiple sclerosis and test the hypothesis that reduced cortical cell body density is associated with cortical and deep grey-matter volume loss. Forty-one people with multiple sclerosis (age 24-72, 14 females) and 37 age- and sex-matched healthy controls were scanned on a 3 T Connectom MRI scanner equipped with 300 mT/m gradients using a multi-shell diffusion MRI protocol. The soma and neurite density imaging model was fitted to high-gradient diffusion MRI data to obtain estimates of intra-neurite, intra-cellular and extra-cellular signal fractions and apparent soma radius. Cortical and deep grey-matter microstructural imaging metrics were compared between multiple sclerosis and healthy controls and correlated with grey-matter volume, clinical disability and cognitive outcomes. People with multiple sclerosis showed significant cortical and deep grey-matter volume loss compared with healthy controls. People with multiple sclerosis showed trends towards lower cortical intra-cellular signal fraction and significantly lower intra-cellular and higher extra-cellular signal fractions in deep grey matter, especially the thalamus and caudate, compared with healthy controls. Changes were most pronounced in progressive disease and correlated with the Expanded Disability Status Scale, but not the Symbol Digit Modalities Test. In multiple sclerosis, normalized thalamic volume was associated with thalamic microstructural imaging metrics. Whereas thalamic volume loss did not correlate with cortical volume loss, cortical microstructural imaging metrics were significantly associated with thalamic volume, and not with cortical volume. Compared with the short diffusion time (Δ = 19 ms) achievable on the Connectom scanner, at the longer diffusion time of Δ = 49 ms attainable on clinical scanners, multiple sclerosis-related changes in imaging metrics were generally less apparent with lower effect sizes in cortical and deep grey matter. Soma and neurite density imaging metrics obtained from high-gradient diffusion MRI data provide detailed grey-matter characterization beyond cortical and thalamic volumes and distinguish multiple sclerosis-related microstructural pathology from healthy controls. Cortical cell body density correlates with thalamic volume, appears sensitive to the microstructural substrate of neurodegeneration and reflects disability status in people with multiple sclerosis, becoming more pronounced as disability worsens.
Collapse
Affiliation(s)
- Eva A Krijnen
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- MS Center Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
| | - Andrew W Russo
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Elsa Salim Karam
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Hansol Lee
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Florence L Chiang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
| | - Susie Y Huang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Eric C Klawiter
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| |
Collapse
|
19
|
Li Z, Fan Q, Bilgic B, Wang G, Wu W, Polimeni JR, Miller KL, Huang SY, Tian Q. Diffusion MRI data analysis assisted by deep learning synthesized anatomical images (DeepAnat). Med Image Anal 2023; 86:102744. [PMID: 36867912 PMCID: PMC10517382 DOI: 10.1016/j.media.2023.102744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 12/25/2022] [Accepted: 01/05/2023] [Indexed: 01/20/2023]
Abstract
Diffusion MRI is a useful neuroimaging tool for non-invasive mapping of human brain microstructure and structural connections. The analysis of diffusion MRI data often requires brain segmentation, including volumetric segmentation and cerebral cortical surfaces, from additional high-resolution T1-weighted (T1w) anatomical MRI data, which may be unacquired, corrupted by subject motion or hardware failure, or cannot be accurately co-registered to the diffusion data that are not corrected for susceptibility-induced geometric distortion. To address these challenges, this study proposes to synthesize high-quality T1w anatomical images directly from diffusion data using convolutional neural networks (CNNs) (entitled "DeepAnat"), including a U-Net and a hybrid generative adversarial network (GAN), and perform brain segmentation on synthesized T1w images or assist the co-registration using synthesized T1w images. The quantitative and systematic evaluations using data of 60 young subjects provided by the Human Connectome Project (HCP) show that the synthesized T1w images and results for brain segmentation and comprehensive diffusion analysis tasks are highly similar to those from native T1w data. The brain segmentation accuracy is slightly higher for the U-Net than the GAN. The efficacy of DeepAnat is further validated on a larger dataset of 300 more elderly subjects provided by the UK Biobank. Moreover, the U-Nets trained and validated on the HCP and UK Biobank data are shown to be highly generalizable to the diffusion data from Massachusetts General Hospital Connectome Diffusion Microstructure Dataset (MGH CDMD) acquired with different hardware systems and imaging protocols and therefore can be used directly without retraining or with fine-tuning for further improved performance. Finally, it is quantitatively demonstrated that the alignment between native T1w images and diffusion images uncorrected for geometric distortion assisted by synthesized T1w images substantially improves upon that by directly co-registering the diffusion and T1w images using the data of 20 subjects from MGH CDMD. In summary, our study demonstrates the benefits and practical feasibility of DeepAnat for assisting various diffusion MRI data analyses and supports its use in neuroscientific applications.
Collapse
Affiliation(s)
- Ziyu Li
- Department of Biomedical Engineering, Tsinghua University, Beijing, China; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Guangzhi Wang
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Qiyuan Tian
- Department of Biomedical Engineering, Tsinghua University, Beijing, China; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States.
| |
Collapse
|
20
|
Polak D, Hossbach J, Splitthoff DN, Clifford B, Lo WC, Tabari A, Lang M, Huang SY, Conklin J, Wald LL, Cauley S. Motion guidance lines for robust data consistency-based retrospective motion correction in 2D and 3D MRI. Magn Reson Med 2023; 89:1777-1790. [PMID: 36744619 PMCID: PMC10518424 DOI: 10.1002/mrm.29534] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/06/2022] [Accepted: 10/31/2022] [Indexed: 02/07/2023]
Abstract
PURPOSE To develop a robust retrospective motion-correction technique based on repeating k-space guidance lines for improving motion correction in Cartesian 2D and 3D brain MRI. METHODS The motion guidance lines are inserted into the standard sequence orderings for 2D turbo spin echo and 3D MPRAGE to inform a data consistency-based motion estimation and reconstruction, which can be guided by a low-resolution scout. The extremely limited number of required guidance lines are repeated during each echo train and discarded in the final image reconstruction. Thus, integration within a standard k-space acquisition ordering ensures the expected image quality/contrast and motion sensitivity of that sequence. RESULTS Through simulation and in vivo 2D multislice and 3D motion experiments, we demonstrate that respectively 2 or 4 optimized motion guidance lines per shot enables accurate motion estimation and correction. Clinically acceptable reconstruction times are achieved through fully separable on-the-fly motion optimizations (˜1 s/shot) using standard scanner GPU hardware. CONCLUSION The addition of guidance lines to scout accelerated motion estimation facilitates robust retrospective motion correction that can be effectively introduced without perturbing standard clinical protocols and workflows.
Collapse
Affiliation(s)
- Daniel Polak
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Siemens Healthcare GmbH, Erlangen, Germany
| | | | | | | | | | - Azadeh Tabari
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Min Lang
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Susie Y. Huang
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - John Conklin
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Lawrence L. Wald
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Stephen Cauley
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
21
|
Huang SY, Zhu F, Guo GH. [Research advances on the role of complement system activation in post-burn immunity]. Zhonghua Shao Shang Yu Chuang Mian Xiu Fu Za Zhi 2023; 39:396-400. [PMID: 37805746 DOI: 10.3760/cma.j.cn501225-20220726-00313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 10/09/2023]
Abstract
Immune activation is one of the major factors of secondary injury post burn, and is the main organismal response in the anti-infection process. As an important part of the innate immune response, the complement system is able to induce the activation of immune cells after burns, promote inflammation and mediate the breakdown of the immune barrier, and even engage in complex cross-linking with the coagulation cascade. This article reviews the role of complement system activation in post-burn immunity and its possibility of clinical translation from the perspectives of innate immunity, acquired immunity, and cross-linking of the complement system with the coagulation cascade.
Collapse
Affiliation(s)
- S Y Huang
- Medical Center of Burn Plastic and Wound Repair, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - F Zhu
- Burn Department, the First Affiliated Hospital, Naval Medical University, Shanghai 200433, China
| | - G H Guo
- Medical Center of Burn Plastic and Wound Repair, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| |
Collapse
|
22
|
Lee HH, Tian Q, Sheft M, Coronado-Leija R, Ramos-Llorden G, Abdollahzadeh A, Fieremans E, Novikov DS, Huang SY. The influence of axonal beading and undulation on axonal diameter mapping. bioRxiv 2023:2023.04.19.537494. [PMID: 37131702 PMCID: PMC10153226 DOI: 10.1101/2023.04.19.537494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
We consider the effect of non-cylindrical axonal shape on axonal diameter mapping with diffusion MRI. Practical sensitivity to axon diameter is attained at strong diffusion weightings b , where the deviation from the 1 / b scaling yields the finite transverse diffusivity, which is then translated into axon diameter. While axons are usually modeled as perfectly straight, impermeable cylinders, the local variations in diameter (caliber variation or beading) and direction (undulation) have been observed in microscopy data of human axons. Here we quantify the influence of cellular-level features such as caliber variation and undulation on axon diameter estimation. For that, we simulate the diffusion MRI signal in realistic axons segmented from 3-dimensional electron microscopy of a human brain sample. We then create artificial fibers with the same features and tune the amplitude of their caliber variations and undulations. Numerical simulations of diffusion in fibers with such tunable features show that caliber variations and undulations result in under- and over-estimation of axon diameters, correspondingly; this bias can be as large as 100%. Given that increased axonal beading and undulations have been observed in pathological tissues, such as traumatic brain injury and ischemia, the interpretation of axon diameter alterations in pathology may be significantly confounded.
Collapse
Affiliation(s)
- Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129,USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129,USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Maxina Sheft
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129,USA
- Harvard-MIT Health Sciences and Technology, Cambridge, MA 02139, USA
| | - Ricardo Coronado-Leija
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY 10016, USA
| | - Gabriel Ramos-Llorden
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129,USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Ali Abdollahzadeh
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY 10016, USA
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY 10016, USA
| | - Dmitry S. Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY 10016, USA
| | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129,USA
- Harvard Medical School, Boston, MA 02115, USA
| |
Collapse
|
23
|
Goncalves Filho ALM, Awan KM, Conklin J, Ngamsombat C, Cauley SF, Setsompop K, Liu W, Splitthoff DN, Lo WC, Kirsch JE, Schaefer PW, Rapalino O, Huang SY. Validation of a highly accelerated post-contrast wave-controlled aliasing in parallel imaging (CAIPI) 3D-T1 MPRAGE compared to standard 3D-T1 MPRAGE for detection of intracranial enhancing lesions on 3-T MRI. Eur Radiol 2023; 33:2905-2915. [PMID: 36460923 PMCID: PMC9718459 DOI: 10.1007/s00330-022-09265-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 09/26/2022] [Accepted: 09/30/2022] [Indexed: 12/04/2022]
Abstract
OBJECTIVES High-resolution post-contrast T1-weighted imaging is a workhorse sequence in the evaluation of neurological disorders. The T1-MPRAGE sequence has been widely adopted for the visualization of enhancing pathology in the brain. However, this three-dimensional (3D) acquisition is lengthy and prone to motion artifact, which often compromises diagnostic quality. The goal of this study was to compare a highly accelerated wave-controlled aliasing in parallel imaging (CAIPI) post-contrast 3D T1-MPRAGE sequence (Wave-T1-MPRAGE) with the standard 3D T1-MPRAGE sequence for visualizing enhancing lesions in brain imaging at 3 T. METHODS This study included 80 patients undergoing contrast-enhanced brain MRI. The participants were scanned with a standard post-contrast T1-MPRAGE sequence (acceleration factor [R] = 2 using GRAPPA parallel imaging technique, acquisition time [TA] = 5 min 18 s) and a prototype post-contrast Wave-T1-MPRAGE sequence (R = 4, TA = 2 min 32 s). Two neuroradiologists performed a head-to-head evaluation of both sequences and rated the visualization of enhancement, sharpness, noise, motion artifacts, and overall diagnostic quality. A 15% noninferiority margin was used to test whether post-contrast Wave-T1-MPRAGE was noninferior to standard T1-MPRAGE. Inter-rater and intra-rater agreement were calculated. Quantitative assessment of CNR/SNR was performed. RESULTS Wave-T1-MPRAGE was noninferior to standard T1-MPRAGE for delineating enhancing lesions with unanimous agreement in all cases between raters. Wave-T1-MPRAGE was noninferior in the perception of noise (p < 0.001), motion artifact (p < 0.001), and overall diagnostic quality (p < 0.001). CONCLUSION High-accelerated post-contrast Wave-T1-MPRAGE enabled a two-fold reduction in acquisition time compared to the standard sequence with comparable performance for visualization of enhancing pathology and equivalent perception of noise, motion artifacts and overall diagnostic quality without loss of clinically important information. KEY POINTS • Post-contrast wave-controlled aliasing in parallel imaging (CAIPI) T1-MPRAGE accelerated the acquisition of three-dimensional (3D) high-resolution post-contrast images by more than two-fold. • Post-contrast Wave-T1-MPRAGE was noninferior to standard T1-MPRAGE with unanimous agreement between reviewers (100% in 80 cases) for the visualization of intracranial enhancing lesions. • Wave-T1-MPRAGE was equivalent to the standard sequence in the perception of noise in 94% (75 of 80) of cases and was preferred in 16% (13 of 80) of cases for decreased motion artifact.
Collapse
Affiliation(s)
- Augusto Lio M Goncalves Filho
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Komal Manzoor Awan
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - John Conklin
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Chanon Ngamsombat
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Nakhon Pathom, Thailand
| | - Stephen F Cauley
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Kawin Setsompop
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Wei Liu
- Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | | | | | - John E Kirsch
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Pamela W Schaefer
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Otto Rapalino
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Susie Y Huang
- Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA.
- Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital, 55 Fruit St, GRB-273A, Boston, MA, 02114, USA.
| |
Collapse
|
24
|
Chen Q, Wang XX, Jiang SW, Gao XT, Huang SY, Liang Y, Jia H, Zhu HF. MGF360-12L of ASFV-SY18 is an immune-evasion protein that inhibits host type I IFN, NF-κB, and JAK/STAT pathways. Pol J Vet Sci 2023; 26:119-130. [PMID: 36961276 DOI: 10.24425/pjvs.2023.145013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
African swine fever virus (ASFV) causes feverous and hemorrhagic disease of domestic pigs and European wild boars with high mortality, yet no commercial vaccine is currently available. Several ASFV strains with natural deletion or gene-targeted knockout of multiple MGF360 and MGF505 genes are attenuated in vitro and in vivo, and can offer full protection against homologous challenge. However, the mechanisms underlying the protection are not fully understood. This study aims to investigate the effects of MGF360-12L of ASFV-SY18 on the cGAS-STING signaling pathway and explore the potential mechanisms. We identified that ASFV-SY18 MGF360-12L could inhibit cGAS-STING, TBK1, or IRF3-5D-stimulated IFN-β expression and ISRE activation. Specifically, MGF360-12L inhibits both the activation of PRD(III-I) in a dose-dependent manner, and suppresses the exogenous expression of TBK1 and IRF3-5D. MGF360-12L could block NF-κB activation induced by overexpression of cGAS-STING, TBK1, IKKβ. Downstream of the IFN-β signaling, MGF360-12L blocks the ISRE promoter activation by reducing total protein level of IRF9. Moreover, MGF360-12L protein can inhibit IFN-β-mediated antiviral effects. In conclusion, our findings suggest that MGF360-12L is a multifunctional immune-evasion protein that inhibits both the expression and effect of IFN-β, which could partially explain the attenuation of relevant gene-deleted ASFV strains, and shed light on the development of efficient ASFV live attenuated vaccines in the future.
Collapse
Affiliation(s)
- Q Chen
- Key Laboratory of Northern Urban Agriculture of Ministry of Agriculture and Rural Affairs, College of Bioscience and Resource Environment, Beijing University of Agriculture, No. 7 Beinong Road, Changping District, 102206 Beijing, China
| | - X X Wang
- Department of Veterinary Medicine, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, No. 2 Yuanmingyuan West Road, Haidian District, 100193 Beijing, China
| | - S W Jiang
- Key Laboratory of Northern Urban Agriculture of Ministry of Agriculture and Rural Affairs, College of Bioscience and Resource Environment, Beijing University of Agriculture, No. 7 Beinong Road, Changping District, 102206 Beijing, China
| | - X T Gao
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, No. 12 Zhongguancun South Street, Haidian District, 100081 Beijing, China
| | - S Y Huang
- Key Laboratory of Northern Urban Agriculture of Ministry of Agriculture and Rural Affairs, College of Bioscience and Resource Environment, Beijing University of Agriculture, No. 7 Beinong Road, Changping District, 102206 Beijing, China
| | - Y Liang
- Key Laboratory of Northern Urban Agriculture of Ministry of Agriculture and Rural Affairs, College of Bioscience and Resource Environment, Beijing University of Agriculture, No. 7 Beinong Road, Changping District, 102206 Beijing, China
| | - H Jia
- Department of Veterinary Medicine, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, No. 2 Yuanmingyuan West Road, Haidian District, 100193 Beijing, China
| | - H F Zhu
- Department of Veterinary Medicine, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, No. 2 Yuanmingyuan West Road, Haidian District, 100193 Beijing, China
| |
Collapse
|
25
|
Wichtmann BD, Fan Q, Eskandarian L, Witzel T, Attenberger UI, Pieper CC, Schad L, Rosen BR, Wald LL, Huang SY, Nummenmaa A. Linear multi-scale modeling of diffusion MRI data: A framework for characterization of oriented structures across length scales. Hum Brain Mapp 2023; 44:1496-1514. [PMID: 36477997 PMCID: PMC9921225 DOI: 10.1002/hbm.26143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 10/07/2022] [Accepted: 10/23/2022] [Indexed: 12/12/2022] Open
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) has evolved to provide increasingly sophisticated investigations of the human brain's structural connectome in vivo. Restriction spectrum imaging (RSI) is a method that reconstructs the orientation distribution of diffusion within tissues over a range of length scales. In its original formulation, RSI represented the signal as consisting of a spectrum of Gaussian diffusion response functions. Recent technological advances have enabled the use of ultra-high b-values on human MRI scanners, providing higher sensitivity to intracellular water diffusion in the living human brain. To capture the complex diffusion time dependence of the signal within restricted water compartments, we expand upon the RSI approach to represent restricted water compartments with non-Gaussian response functions, in an extended analysis framework called linear multi-scale modeling (LMM). The LMM approach is designed to resolve length scale and orientation-specific information with greater specificity to tissue microstructure in the restricted and hindered compartments, while retaining the advantages of the RSI approach in its implementation as a linear inverse problem. Using multi-shell, multi-diffusion time DW-MRI data acquired with a state-of-the-art 3 T MRI scanner equipped with 300 mT/m gradients, we demonstrate the ability of the LMM approach to distinguish different anatomical structures in the human brain and the potential to advance mapping of the human connectome through joint estimation of the fiber orientation distributions and compartment size characteristics.
Collapse
Affiliation(s)
- Barbara D. Wichtmann
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General HospitalCharlestownMassachusettsUSA
- Department of Diagnostic and Interventional RadiologyUniversity Hospital BonnBonnGermany
| | - Qiuyun Fan
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General HospitalCharlestownMassachusettsUSA
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics EngineeringTianjin UniversityTianjinChina
| | - Laleh Eskandarian
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General HospitalCharlestownMassachusettsUSA
| | | | - Ulrike I. Attenberger
- Department of Diagnostic and Interventional RadiologyUniversity Hospital BonnBonnGermany
| | - Claus C. Pieper
- Department of Diagnostic and Interventional RadiologyUniversity Hospital BonnBonnGermany
| | - Lothar Schad
- Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty MannheimHeidelberg UniversityMannheimGermany
| | - Bruce R. Rosen
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General HospitalCharlestownMassachusettsUSA
| | - Lawrence L. Wald
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General HospitalCharlestownMassachusettsUSA
- Harvard‐MIT Division of Health Sciences and TechnologyMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Susie Y. Huang
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General HospitalCharlestownMassachusettsUSA
- Harvard‐MIT Division of Health Sciences and TechnologyMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Aapo Nummenmaa
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General HospitalCharlestownMassachusettsUSA
| |
Collapse
|
26
|
Ramos-Llordén G, Park D, Kirsch JE, Scholz A, Keil B, Maffei C, Lee HH, Bilgiç B, Edlow BL, Mekkaoui C, Yendiki A, Witzel T, Huang SY. Eddy current-induced artifacts correction in high gradient strength diffusion MRI with dynamic field monitoring: demonstration in ex vivo human brain imaging. bioRxiv 2023:2023.02.15.528684. [PMID: 36824894 PMCID: PMC9948962 DOI: 10.1101/2023.02.15.528684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Purpose To demonstrate the advantages of spatiotemporal magnetic field monitoring to correct eddy current-induced artifacts (ghosting and geometric distortions) in high gradient strength diffusion MRI (dMRI). Methods A dynamic field camera with 16 NMR field probes was used to characterize eddy current fields induced from diffusion gradients for different gradients strengths (up to 300 mT/m), diffusion directions, and shots in a 3D multi-shot EPI sequence on a 3T Connectom scanner. The efficacy of dynamic field monitoring-based image reconstruction was demonstrated on high-resolution whole brain ex vivo dMRI. A 3D multi-shot image reconstruction framework was informed with the actual nonlinear phase evolution measured with the dynamic field camera, thereby accounting for high-order eddy currents fields on top of the image encoding gradients in the image formation model. Results Eddy current fields from diffusion gradients at high gradient strength in a 3T Connectom scanner are highly nonlinear in space and time, inducing high-order spatial phase modulations between odd/even echoes and shots that are not static during the readout. Superior reduction of ghosting and geometric distortion was achieved with dynamic field monitoring compared to ghosting approaches such as navigator- and structured low-rank-based methods or MUSE, followed by image-based distortion correction with eddy. Improved dMRI analysis is demonstrated with diffusion tensor imaging and high-angular resolution diffusion imaging. Conclusion Strong eddy current artifacts characteristic of high gradient strength dMRI can be well corrected with dynamic field monitoring-based image reconstruction, unlike the two-step approach consisting of ghosting correction followed by geometric distortion reduction with eddy.
Collapse
|
27
|
Lang M, Tabari A, Polak D, Ford J, Clifford B, Lo WC, Manzoor K, Splitthoff DN, Wald LL, Rapalino O, Schaefer P, Conklin J, Cauley S, Huang SY. Clinical Evaluation of Scout Accelerated Motion Estimation and Reduction Technique for 3D MR Imaging in the Inpatient and Emergency Department Settings. AJNR Am J Neuroradiol 2023; 44:125-133. [PMID: 36702502 PMCID: PMC9891324 DOI: 10.3174/ajnr.a7777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 12/11/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND AND PURPOSE A scout accelerated motion estimation and reduction (SAMER) framework has been developed for efficient retrospective motion correction. The goal of this study was to perform an initial evaluation of SAMER in a series of clinical brain MR imaging examinations. MATERIALS AND METHODS Ninety-seven patients who underwent MR imaging in the inpatient and emergency department settings were included in the study. SAMER motion correction was retrospectively applied to an accelerated T1-weighted MPRAGE sequence that was included in brain MR imaging examinations performed with and without contrast. Two blinded neuroradiologists graded images with and without SAMER motion correction on a 5-tier motion severity scale (none = 1, minimal = 2, mild = 3, moderate = 4, severe = 5). RESULTS The median SAMER reconstruction time was 1 minute 47 seconds. SAMER motion correction significantly improved overall motion grades across all examinations (P < .005). Motion artifacts were reduced in 28% of cases, unchanged in 64% of cases, and increased in 8% of cases. SAMER improved motion grades in 100% of moderate motion cases and 75% of severe motion cases. Sixty-nine percent of nondiagnostic motion cases (grades 4 and 5) were considered diagnostic after SAMER motion correction. For cases with minimal or no motion, SAMER had negligible impact on the overall motion grade. For cases with mild, moderate, and severe motion, SAMER improved the motion grade by an average of 0.3 (SD, 0.5), 1.1 (SD, 0.3), and 1.1 (SD, 0.8) grades, respectively. CONCLUSIONS SAMER improved the diagnostic image quality of clinical brain MR imaging examinations with motion artifacts. The improvement was most pronounced for cases with moderate or severe motion.
Collapse
Affiliation(s)
- M Lang
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
| | - A Tabari
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
| | - D Polak
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Siemens Healthcare GmbH (D.P., D.N.S.), Erlangen, Germany
| | - J Ford
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
| | - B Clifford
- Siemens Medical Solutions (B.C., W.-C.L.), Boston, Massachusetts
| | - W-C Lo
- Siemens Medical Solutions (B.C., W.-C.L.), Boston, Massachusetts
| | - K Manzoor
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
| | - D N Splitthoff
- Siemens Healthcare GmbH (D.P., D.N.S.), Erlangen, Germany
| | - L L Wald
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
- Harvard-MIT Health Sciences and Technology (L.L.W.), Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - O Rapalino
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
| | - P Schaefer
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
| | - J Conklin
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
| | - S Cauley
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
| | - S Y Huang
- From the Department of Radiology (M.L., A.T., D.P., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Harvard Medical School (M.L., A.T., J.F., K.M., L.L.W., O.R., P.S., J.C., S.C., S.Y.H.), Boston, Massachusetts
| |
Collapse
|
28
|
Ramos-Llordén G, Lobos RA, Kim TH, Tian Q, Witzel T, Lee HH, Scholz A, Keil B, Yendiki A, Bilgiç B, Haldar JP, Huang SY. High-fidelity, high-spatial-resolution diffusion magnetic resonance imaging of ex vivo whole human brain at ultra-high gradient strength with structured low-rank echo-planar imaging ghost correction. NMR Biomed 2023; 36:e4831. [PMID: 36106429 PMCID: PMC9883835 DOI: 10.1002/nbm.4831] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 08/20/2022] [Accepted: 09/11/2022] [Indexed: 06/15/2023]
Abstract
Diffusion magnetic resonance imaging (dMRI) of whole ex vivo human brain specimens enables three-dimensional (3D) mapping of structural connectivity at the mesoscopic scale, providing detailed evaluation of fiber architecture and tissue microstructure at a spatial resolution that is difficult to access in vivo. To account for the short T2 and low diffusivity of fixed tissue, ex vivo dMRI is often acquired using strong diffusion-sensitizing gradients and multishot/segmented 3D echo-planar imaging (EPI) sequences to achieve high spatial resolution. However, the combination of strong diffusion-sensitizing gradients and multishot/segmented EPI readout can result in pronounced ghosting artifacts incurred by nonlinear spatiotemporal variations in the magnetic field produced by eddy currents. Such ghosting artifacts cannot be corrected with conventional correction solutions and pose a significant roadblock to leveraging human MRI scanners with ultrahigh gradients for ex vivo whole-brain dMRI. Here, we show that ghosting-correction approaches that correct for either polarity-related ghosting or shot-to-shot variations in a separate manner are suboptimal for 3D multishot diffusion-weighted EPI experiments in fixed human brain specimens using strong diffusion-sensitizing gradients on the 3-T Connectom MRI scanner, resulting in orientationally biased dMRI estimates. We apply a recently developed advanced k-space reconstruction method based on structured low-rank matrix (SLM) modeling that handles both polarity-related ghosting and shot-to-shot variation simultaneously, to mitigate artifacts in high-angular resolution multishot dMRI data acquired in several fixed human brain specimens at 0.7-0.8-mm isotropic spatial resolution using b-values up to 10,000 s/mm2 and gradient strengths up to 280 mT/m. We demonstrate the improved mapping of diffusion tensor imaging and fiber orientation distribution functions in key neuroanatomical areas distributed across the whole brain using SLM-based EPI ghost correction compared with alternative techniques.
Collapse
Affiliation(s)
- Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Rodrigo A. Lobos
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Tae Hyung Kim
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Computer Engineering, Hongik University, Seoul, Republic of Korea
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Alina Scholz
- Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Sciences, Giessen, Germany
| | - Boris Keil
- Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Sciences, Giessen, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Marburg, Philipps University of Marburg, Marburg, Germany
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Berkin Bilgiç
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| |
Collapse
|
29
|
Lang M, Cartmell S, Tabari A, Briggs D, Pianykh O, Kirsch J, Cauley S, Lo WC, Risacher S, Filho AG, Succi MD, Rapalino O, Schaefer P, Conklin J, Huang SY. Evaluation of the Aggregated Time Savings in Adopting Fast Brain MRI Techniques for Outpatient Brain MRI. Acad Radiol 2023; 30:341-348. [PMID: 34635436 PMCID: PMC8989721 DOI: 10.1016/j.acra.2021.07.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 06/29/2021] [Accepted: 07/02/2021] [Indexed: 01/11/2023]
Abstract
INTRODUCTION Clinical validation studies have demonstrated the ability of accelerated MRI sequences to decrease acquisition time and motion artifact while preserving image quality. The operational benefits, however, have been less explored. Here, we report our initial clinical experience in implementing fast MRI techniques for outpatient brain imaging during the COVID-19 pandemic. METHODS Aggregate acquisition times were extracted from the medical record on consecutive imaging examinations performed during matched pre-implementation (7/1/2019-12/31/2019) and post-implementation periods (7/1/2020-12/31/2020). Expected acquisition time reduction for each MRI protocol was calculated through manual collection of acquisition times for the conventional and accelerated sequences performed during the pre- and post-implementation periods. Aggregate and expected acquisition times were compared for the five most frequently performed brain MRI protocols: brain without contrast (BR-), brain with and without contrast (BR+), multiple sclerosis (MS), memory loss (MML), and epilepsy (EPL). RESULTS The expected time reductions for BR-, BR+, MS, MML, and EPL protocols were 6.6 min, 11.9 min, 14 min, 10.8 min, and 14.1 min, respectively. The overall median aggregate acquisition time was 31 [25, 36] min for the pre-implementation period and 18 [15, 22] min for the post-implementation period, with a difference of 13 min (42%). The median acquisition time was reduced by 4 min (25%) for BR-, 14.0 min (44%) for BR+, 14 min (38%) for MS, 11 min (52%) for MML, and 16 min (35%) for EPL. CONCLUSION The implementation of fast brain MRI sequences significantly reduced the acquisition times for the most commonly performed outpatient brain MRI protocols.
Collapse
Affiliation(s)
- Min Lang
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts; Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, Massachusetts
| | - Samuel Cartmell
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts
| | - Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts
| | - Daniel Briggs
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts
| | - Oleg Pianykh
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts
| | - John Kirsch
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Stephen Cauley
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Wei-Ching Lo
- Siemens Medical Solutions, Boston, Massachusetts
| | - Seretha Risacher
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts
| | - Augusto Goncalves Filho
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Marc D Succi
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts; Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, Massachusetts
| | - Otto Rapalino
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts
| | - Pamela Schaefer
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts
| | - John Conklin
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts
| | - Susie Y Huang
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street Boston, Boston, Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.
| |
Collapse
|
30
|
Krijnen EA, Ngamsombat C, George IC, Yu FF, Fan Q, Tian Q, Huang SY, Klawiter EC. Axonal and myelin changes and their inter-relationship in the optic radiations in people with multiple sclerosis. Mult Scler J Exp Transl Clin 2023; 9:20552173221147620. [PMID: 36814811 PMCID: PMC9940187 DOI: 10.1177/20552173221147620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023] Open
Abstract
Background The imaging g-ratio, estimated from axonal volume fraction (AVF) and myelin volume fraction (MVF), is a novel biomarker of microstructural tissue integrity in multiple sclerosis (MS). Objective To assess axonal and myelin changes and their inter-relationship as measured by g-ratio in the optic radiations (OR) in people with MS (pwMS) with and without previous optic neuritis (ON) compared to healthy controls (HC). Methods Thirty pwMS and 17 HCs were scanned on a 3Tesla Connectom scanner. AVF and MVF, derived from a multi-shell diffusion protocol and macromolecular tissue volume, respectively, were measured in normal-appearing white matter (NAWM) and lesions within the OR and used to calculate imaging g-ratio. Results OR AVF and MVF were decreased in pwMS compared to HC, and in OR lesions compared to NAWM, whereas the g-ratio was not different. Compared to pwMS with previous ON, AVF and g-ratio tended to be higher in pwMS without prior ON. AVF and MVF, particularly in NAWM, were positively correlated with retinal thickness, which was more pronounced in pwMS with prior ON. Conclusion Axonal measures reflect microstructural tissue damage in the OR, particularly in the setting of remote ON, and correlate with established metrics of visual health in MS.
Collapse
Affiliation(s)
- Eva A Krijnen
- MS Center Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Chanon Ngamsombat
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Ilena C George
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Fang F Yu
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qiuyun Fan
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Academy of Medical Engineering and Translational Medicine, Medical College, Tianjin University, Tianjin, China
| | - Qiyuan Tian
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Susie Y Huang
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Eric C Klawiter
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
31
|
Huang SY, Gong YL, Zhou DP, Jiang LL, Xiang LJ, Chen ZH, Wang DL, Huang GT. [Plasmids carried by carbapenems-resistant Klebsiella pneumoniae in burn patients and its correlation with strain transmission]. Zhonghua Shao Shang Yu Chuang Mian Xiu Fu Za Zhi 2022; 38:1140-1147. [PMID: 36594144 DOI: 10.3760/cma.j.cn501120-20210930-00343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Objective: To explore the carrier status of carbapenems-resistant Klebsiella pneumoniae (CRKP) plasmids in burn patients and analyze the correlation of these plasmids with the transmission of CRKP. Methods: A retrospective observational study was conducted. A total of 26 CRKP strains, which were isolated from the clinic-related samples of 22 burn patients (with 20 males and 2 females, aged (42±16) years) admitted to the First Affiliated Hospital of Army Medical University (the Third Military Medical University) from January to December 2017, were collected and individually numbered. The plasmids of the strains were extracted by alkali lysis. After determination of the plasmid concentration by a nucleic acid concentration detector, the agarose gel electrophoresis was used to visualize the bands, and rough plasmids typing was performed. The plasmid of the smallest numbered CRKP in each plasmid type was transformed into competent Escherichia coli (E. coli) strain Top10 (hereinafter referred to as TOP10 strain). The growth of each transformed strains and a Top10 strain cultivated in ampicillin containing Luria-Bertani (LB) agar medium overnight was observed, and the proportion of successful transformation was calculated. The plasmids from the smallest numbered plasmid carrying CRKP strain of successfully transformed Top10 strains (hereinafter referred to as the smallest successfully transformed strain) and correspondingly numbered CRKP were extracted, and then, the agarose gel electrophoresis was used to visualize the bands. Aforementioned successfully transformed strains and a TOP10 strain were used for the antimicrobial susceptibility testing with 17 antibiotics commonly used in clinic. The plasmid from the smallest successfully transformed strain was sequenced using the next-generation sequencing technology. Bioinformatics analyses such as protein-coding gene prediction and protein sequence alignment were performed successively. The sequence was subsequently named pKP03-NDM1 according to the carrying of drug resistance gene. According to the whole genome sequence of the plasmid carried by the smallest successfully transformed strain, the polymerase chain reaction, agarose gel electrophoresis, and gene sequencing were used to detect the New Delhi metallo-beta lactamase-1 (blaNDM-1) of plasmids in the remaining 25 strains of CRKP. The ST typing in multilocus sequence typing of 26 strains of CRKP was analyzed based on the literature. Results: Plasmids were successfully extracted from 26 CRKP, with mass concentrations ranging from 19.3 to 189.8 ng/μL. Each of the 26 CRKP carrying plasmids showed at least one band longer than 2 500 bp in the agarose gel electrophoresis, which were roughly divided into 6 patterns of A, B, C, D, E, and F. After overnight cultivation, no growth of strains was observed in LB agar medium containing ampicillin inoculated with the TOP10 strain or TOP10 strains transformed by the plasmid of CRKP patterning A, B, D, or E. In contrast, TOP10 strains transformed by the pattern C plasmid from NO.3 CRKP and the pattern F plasmid from NO.15 CRKP resulted in numerous colony growths, and those transformed strains were named as TOP10-pKP03 and TOP10-pKP15, respectively. The proportion of successful transformation was 1/3. The plasmid carried by TOP10-pKP03 showed a single band in the agarose gel electrophoresis, which was the same size as the largest band of the plasmid from NO.3 CRKP. The TOP10 strain was sensitive to the 17 antibiotics commonly used in clinic. TOP10-pKP03 and TOP10-pKP15 were resistant to penicillins, cephalosporins, and carbapenems but remained sensitive to monocyclic β-lactam, aminoglycosides, quinolones and tigecycline. The full length of the plasmid carried by TOP10-pKP03 was 41 190 bp. In addition to blaNDM-1, this plasmid carried bleMBL, T4SS, bleomycin resistance gene, conjugation transfer elements, and relaxase, etc. The plasmid showed 99% nucleotide identity similarity and the same length to the plasmid pJN24NDM1 extracted from an E. coli isolate JN24. Totally 16 (61.5%) CRKP were confirmed to carrying blaNDM-1 gene, among the ST typing of the 16 strains, 11 strains were ST11, while ST215, ST260, ST395, ST2230, and new ST had 1 strain each. Among the ST typing of 10 blaNDM-1-negative CRKP, 8 strains were ST11, while ST395 and ST2230 had 1 strain each. Conclusions: A blaNDM-1 gene carrying plasmid pKP03-NDM1 was extracted and sequenced from CRKP isolated from burn patients, with a high plasmid carrying rate. Meanwhile, this plasmid may mediate inter-CRKP and CRKP-E. coli horizontal transfer of blaNDM-1, leading to transmission of antimicrobial resistance.
Collapse
Affiliation(s)
- S Y Huang
- Department of Burns and Plastic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
| | - Y L Gong
- State Key Laboratory of Trauma, Burns and Combined Injury, Institute of Burn Research, the First Affiliated Hospital of Army Medical University (the Third Military Medical University), Chongqing 400038, China
| | - D P Zhou
- Department of Burns and Plastic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
| | - L L Jiang
- Department of Burns and Plastic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
| | - L J Xiang
- Department of Laboratory Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
| | - Z H Chen
- Department of Laboratory Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
| | - D L Wang
- Department of Burns and Plastic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
| | - G T Huang
- Department of Burns and Plastic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi 563003, China
| |
Collapse
|
32
|
Russo AW, Stockel KE, Tobyne SM, Ngamsombat C, Brewer K, Nummenmaa A, Huang SY, Klawite EC. Associations between corpus callosum damage, clinical disability, and surface-based homologous inter-hemispheric connectivity in multiple sclerosis. Brain Struct Funct 2022; 227:2909-2922. [PMID: 35536387 PMCID: PMC9850837 DOI: 10.1007/s00429-022-02498-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 04/11/2022] [Indexed: 01/22/2023]
Abstract
Axonal damage in the corpus callosum is prevalent in multiple sclerosis (MS). Although callosal damage is associated with disrupted functional connectivity between hemispheres, it is unclear how this relates to cognitive and physical disability. We investigated this phenomenon using advanced measures of microstructural integrity in the corpus callosum and surface-based homologous inter-hemispheric connectivity (sHIC) in the cortex. We found that sHIC was significantly decreased in primary motor, somatosensory, visual, and temporal cortical areas in a group of 36 participants with MS (29 relapsing-remitting, 4 secondary progressive MS, and 3 primary-progressive MS) compared with 42 healthy controls (cluster level, p < 0.05). In participants with MS, global sHIC correlated with fractional anisotropy and restricted volume fraction in the posterior segment of the corpus callosum (r = 0.426, p = 0.013; r = 0.399, p = 0.020, respectively). Lower sHIC, particularly in somatomotor and posterior cortical areas, was associated with cognitive impairment and higher disability scores on the Expanded Disability Status Scale (EDSS). We demonstrated that higher levels of sHIC attenuated the effects of posterior callosal damage on physical disability and cognitive dysfunction, as measured by the EDSS and Brief Visuospatial Memory Test-Revised (interaction effect, p < 0.05). We also observed a positive association between global sHIC and years of education (r = 0.402, p = 0.018), supporting the phenomenon of "brain reserve" in MS. Our data suggest that preserved sHIC helps prevent cognitive and physical decline in MS.
Collapse
Affiliation(s)
- Andrew W. Russo
- Department of Neurology, Massachusetts General Hospital, Boston, MA, US
| | | | - Sean M. Tobyne
- Department of Neurology, Massachusetts General Hospital, Boston, MA, US
| | - Chanon Ngamsombat
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, No. 149, 13th Street, Charlestown, Boston, MA 02129, US
| | - Kristina Brewer
- Department of Neurology, Massachusetts General Hospital, Boston, MA, US
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, No. 149, 13th Street, Charlestown, Boston, MA 02129, US
| | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, No. 149, 13th Street, Charlestown, Boston, MA 02129, US
| | - Eric C. Klawite
- Department of Neurology, Massachusetts General Hospital, Boston, MA, US
| |
Collapse
|
33
|
Howard AF, Cottaar M, Drakesmith M, Fan Q, Huang SY, Jones DK, Lange FJ, Mollink J, Rudrapatna SU, Tian Q, Miller KL, Jbabdi S. Estimating axial diffusivity in the NODDI model. Neuroimage 2022; 262:119535. [PMID: 35931306 PMCID: PMC9802007 DOI: 10.1016/j.neuroimage.2022.119535] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/20/2022] [Accepted: 08/01/2022] [Indexed: 01/03/2023] Open
Abstract
To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model (d∥=1.7μm2/ms). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ∼2-2.5μm2/ms, in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data.
Collapse
Affiliation(s)
- Amy Fd Howard
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
| | - Michiel Cottaar
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Mark Drakesmith
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States; Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China; Academy of Medical Engineering and Translational Medicine, Medical College, Tianjin University, Tianjin, China
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Frederik J Lange
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jeroen Mollink
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Suryanarayana Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Philips Innovation Campus, Bangalore, India
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States
| | - Karla L Miller
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Saad Jbabdi
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
34
|
Huang SY, Lin J. [Correlation of periodontitis and oculopathy]. Zhonghua Kou Qiang Yi Xue Za Zhi 2022; 57:642-647. [PMID: 35692010 DOI: 10.3760/cma.j.cn112144-20220228-00080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
With the development of researches on the relationship between periodontal health and general health, more and more evidences showed that periodontitis was closely related to oculopathy, while the mechanisms were not very clear at present. This article will focus on the influences of periodontitis on the occurrence and development of various oculopathy such as diabetic retinopathy and senile macular degeneration, and discuss the possible mechanisms of the influence by periodontitis. This will provide a theoretical basis for the new ideas on prevention and treatment of oculopathy.
Collapse
Affiliation(s)
- S Y Huang
- Department of Stomatology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
| | - J Lin
- Department of Stomatology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
| |
Collapse
|
35
|
Edlow BL, Bodien YG, Baxter T, Belanger H, Cali R, Deary K, Fischl B, Foulkes AS, Gilmore N, Greve DN, Hooker JM, Huang SY, Kelemen JN, Kimberly WT, Maffei C, Masood M, Perl D, Polimeni JR, Rosen BR, Tromly S, Tseng CEJ, Yao EF, Zurcher NR, Mac Donald CL, Dams-O'Connor K. Long-Term Effects of Repeated Blast Exposure in United States Special Operations Forces Personnel: A Pilot Study Protocol. J Neurotrauma 2022; 39:1391-1407. [PMID: 35620901 PMCID: PMC9529318 DOI: 10.1089/neu.2022.0030] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Emerging evidence suggests that repeated blast exposure (RBE) is associated with brain injury in military personnel. United States (U.S.) Special Operations Forces (SOF) personnel experience high rates of blast exposure during training and combat, but the effects of low-level RBE on brain structure and function in SOF have not been comprehensively characterized. Further, the pathophysiological link between RBE-related brain injuries and cognitive, behavioral, and physical symptoms has not been fully elucidated. We present a protocol for an observational pilot study, Long-Term Effects of Repeated Blast Exposure in U.S. SOF Personnel (ReBlast). In this exploratory study, 30 active-duty SOF personnel with RBE will participate in a comprehensive evaluation of: 1) brain network structure and function using Connectome magnetic resonance imaging (MRI) and 7 Tesla MRI; 2) neuroinflammation and tau deposition using positron emission tomography; 3) blood proteomics and metabolomics; 4) behavioral and physical symptoms using self-report measures; and 5) cognition using a battery of conventional and digitized assessments designed to detect subtle deficits in otherwise high-performing individuals. We will identify clinical, neuroimaging, and blood-based phenotypes that are associated with level of RBE, as measured by the Generalized Blast Exposure Value. Candidate biomarkers of RBE-related brain injury will inform the design of a subsequent study that will test a diagnostic assessment battery for detecting RBE-related brain injury. Ultimately, we anticipate that the ReBlast study will facilitate the development of interventions to optimize the brain health, quality of life, and battle readiness of U.S. SOF personnel.
Collapse
Affiliation(s)
- Brian L Edlow
- Harvard Medical School, 1811, 175 Cambridge Street - Suite 300, Boston, Massachusetts, United States, 02115.,Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States;
| | - Yelena G Bodien
- Massachusetts General Hospital, 2348, Department of Neurology, 101 Merrimac, Boston, Massachusetts, United States, 02114;
| | - Timothy Baxter
- University of South Florida, 7831, Institute for Applied Engineering, Tampa, Florida, United States;
| | - Heather Belanger
- University of South Florida, 7831, Department of Psychiatry and Behavioral Neurosciences, Tampa, Florida, United States;
| | - Ryan Cali
- Massachusetts General Hospital, 2348, Boston, Massachusetts, United States;
| | - Katryna Deary
- Navy SEAL Foundation, Virginia Beach, Virginia, United States;
| | - Bruce Fischl
- Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Room 2301, 149 13th Street, Charlestown, Massachusetts, United States, 02129-2020.,Massachusetts General Hospital;
| | - Andrea S Foulkes
- Massachusetts General Hospital, 2348, Boston, Massachusetts, United States;
| | - Natalie Gilmore
- Massachusetts General Hospital, 2348, Boston, Massachusetts, United States;
| | - Douglas N Greve
- Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States;
| | - Jacob M Hooker
- Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States;
| | - Susie Y Huang
- Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States;
| | - Jessica N Kelemen
- Massachusetts General Hospital, 2348, Boston, Massachusetts, United States;
| | - W Taylor Kimberly
- Massachusetts General Hospital, 2348, Boston, Massachusetts, United States;
| | - Chiara Maffei
- Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States;
| | - Maryam Masood
- Massachusetts General Hospital, 2348, Boston, Massachusetts, United States;
| | - Daniel Perl
- Uniformed Services University of the Health Sciences, 1685, Pathology, 4301 Jones Bridge Road, Room B3138, Bethesda, Maryland, United States, 20814;
| | - Jonathan R Polimeni
- Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States;
| | - Bruce R Rosen
- Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States;
| | - Samantha Tromly
- University of South Florida, 7831, Institute for Applied Engineering, Tampa, Florida, United States;
| | - Chieh-En J Tseng
- Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States;
| | - Eveline F Yao
- United States Special Operations Command, Office of the Surgeon General, MacDill Air Force Base, United States;
| | - Nicole R Zurcher
- Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States;
| | - Christine L Mac Donald
- University of Washington, 7284, Department of Neurological Surgery, Seattle, Washington, United States;
| | - Kristen Dams-O'Connor
- Icahn School of Medicine at Mount Sinai, 5925, Rehabilitation Medicine, One Gustave Levy Place, Box 1163, New York, New York, United States, 10029; kristen.dams-o'
| |
Collapse
|
36
|
Zeng R, Liang ZT, Huang SY, Zhang JT, Qiao XR, Yang H, Dong L. [Clinical analysis of chronic eosinophilic pneumonia in 9 cases]. Zhonghua Jie He He Hu Xi Za Zhi 2022; 45:368-372. [PMID: 35381634 DOI: 10.3760/cma.j.cn112147-20211213-00881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective: To investigate the clinical features, treatment and prognosis of chronic eosinophilic pneumonia. Methods: Nine patients with chronic eosinophilic pneumonia diagnosed in Shandong Provincial Qianfoshan Hospital from January 2014 to December 2020 were enrolled and followed up. The data of clinically proven chronic eosinophilic pneumonia were reviewed. Results: The 9 cases included one male and eight females, aged from 16 to 71 years (median 47 years). Among them, 5 cases were complicated with asthma, 1 case was complicated with allergic rhinitis, and 1 case had an allergic history of pollen. All the patients had cough, expectoration, chest tightness and wheezing, and a few had fatigue (3/9), fever (1/9) and chest pain (1/9). Single or multiple patchy high-density shadows (9/9), mediastinal lymphadenopathy (7/9), air bronchogram (2/9), and reticular shadow (1/9) were observed in chest CT. Peripheral eosinophils (EOS) and serum total IgE increased to varying degrees in the 9 patients. Meanwhile, the bronchoscopy of 5 cases showed elevated percentage of eosinophils in alveolar lavage fluid, and the lung biopsy of remaining 4 cases showed EOS infiltration in lung alveolar and interstitium. After receiving glucocorticoid therapy for 0.5 to 1 month, the clinical symptoms of all 9 patients had been improved and lung lesions on CT scans had been obviously absorbed. Four cases relapsed during follow-up. Conclusions: For patients especially women who have a history of allergy, elevated blood eosinophils and serum total IgE with pulmonary high-density shadow or consolidation, chronic eosinophilic pneumonia should be considered, and bronchoscopy or percutaneous lung biopsy is indicated for a definite diagnosis. Glucocorticoid therapy is effective, but the rate of recurrence is high.
Collapse
Affiliation(s)
- R Zeng
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Shandong Institute of Respiratory Diseases, Jinan 250014, China
| | - Z T Liang
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Shandong Institute of Respiratory Diseases, Jinan 250014, China
| | - S Y Huang
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Shandong Institute of Respiratory Diseases, Jinan 250014, China
| | - J T Zhang
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Shandong Institute of Respiratory Diseases, Jinan 250014, China
| | - X R Qiao
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Shandong Institute of Respiratory Diseases, Jinan 250014, China
| | - H Yang
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Shandong Institute of Respiratory Diseases, Jinan 250014, China
| | - L Dong
- Department of Respiratory and Critical Care Medicine, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Shandong Institute of Respiratory Diseases, Jinan 250014, China
| |
Collapse
|
37
|
Tian Q, Li Z, Fan Q, Polimeni JR, Bilgic B, Salat DH, Huang SY. SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI. Neuroimage 2022; 253:119033. [PMID: 35240299 DOI: 10.1016/j.neuroimage.2022.119033] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 02/15/2022] [Accepted: 02/21/2022] [Indexed: 12/12/2022] Open
Abstract
Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging method for the in vivo mapping of brain tissue microstructure and white matter tracts. Nonetheless, the noise in the diffusion-weighted images (DWIs) decreases the accuracy and precision of DTI derived microstructural parameters and leads to prolonged acquisition time for achieving improved signal-to-noise ratio (SNR). Deep learning-based image denoising using convolutional neural networks (CNNs) has superior performance but often requires additional high-SNR data for supervising the training of CNNs, which reduces the feasibility of supervised learning-based denoising in practice. In this work, we develop a self-supervised deep learning-based method entitled "SDnDTI" for denoising DTI data, which does not require additional high-SNR data for training. Specifically, SDnDTI divides multi-directional DTI data into many subsets of six DWI volumes and transforms DWIs from each subset to along the same diffusion-encoding directions through the diffusion tensor model, generating multiple repetitions of DWIs with identical image contrasts but different noise observations. SDnDTI removes noise by first denoising each repetition of DWIs using a deep 3-dimensional CNN with the average of all repetitions with higher SNR as the training target, following the same approach as normal supervised learning based denoising methods, and then averaging CNN-denoised images for achieving higher SNR. The denoising efficacy of SDnDTI is demonstrated in terms of the similarity of output images and resultant DTI metrics compared to the ground truth generated using substantially more DWI volumes on two datasets with different spatial resolutions, b-values and numbers of input DWI volumes provided by the Human Connectome Project (HCP) and the Lifespan HCP in Aging. The SDnDTI results preserve image sharpness and textural details and substantially improve upon those from the raw data. The results of SDnDTI are comparable to those from supervised learning-based denoising and outperform those from state-of-the-art conventional denoising algorithms including BM4D, AONLM and MPPCA. By leveraging domain knowledge of diffusion MRI physics, SDnDTI makes it easier to use CNN-based denoising methods in practice and has the potential to benefit a wider range of research and clinical applications that require accelerated DTI acquisition and high-quality DTI data for mapping of tissue microstructure, fiber tracts and structural connectivity in the living human brain.
Collapse
Affiliation(s)
- Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States.
| | - Ziyu Li
- Department of Biomedical Engineering, Tsinghua University, Beijing, PR China
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - David H Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, United States; Department of Radiology, Harvard Medical School, Boston, MA, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| |
Collapse
|
38
|
Fan Q, Eichner C, Afzali M, Mueller L, Tax CMW, Davids M, Mahmutovic M, Keil B, Bilgic B, Setsompop K, Lee HH, Tian Q, Maffei C, Ramos-Llordén G, Nummenmaa A, Witzel T, Yendiki A, Song YQ, Huang CC, Lin CP, Weiskopf N, Anwander A, Jones DK, Rosen BR, Wald LL, Huang SY. Mapping the Human Connectome using Diffusion MRI at 300 mT/m Gradient Strength: Methodological Advances and Scientific Impact. Neuroimage 2022; 254:118958. [PMID: 35217204 DOI: 10.1016/j.neuroimage.2022.118958] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 01/27/2022] [Accepted: 01/31/2022] [Indexed: 12/20/2022] Open
Abstract
Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide - one in the United Kingdom in Cardiff, Wales, another in Continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength dMRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for dMRI and where the field is headed in the coming years.
Collapse
Affiliation(s)
- Qiuyun Fan
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Cornelius Eichner
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, LS2 9JT, UK
| | - Lars Mueller
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, LS2 9JT, UK
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Image Sciences Institute, University Medical Center (UMC) Utrecht, Utrecht, Netherlands
| | - Mathias Davids
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Mirsad Mahmutovic
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Boris Keil
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | | | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Yi-Qiao Song
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA USA
| | - Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China; Shanghai Changning Mental Health Center, Shanghai, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.; Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Alfred Anwander
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States.
| |
Collapse
|
39
|
Tian Q, Fan Q, Witzel T, Polackal MN, Ohringer NA, Ngamsombat C, Russo AW, Machado N, Brewer K, Wang F, Setsompop K, Polimeni JR, Keil B, Wald LL, Rosen BR, Klawiter EC, Nummenmaa A, Huang SY. Comprehensive diffusion MRI dataset for in vivo human brain microstructure mapping using 300 mT/m gradients. Sci Data 2022; 9:7. [PMID: 35042861 PMCID: PMC8766594 DOI: 10.1038/s41597-021-01092-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 10/25/2021] [Indexed: 12/27/2022] Open
Abstract
Strong gradient systems can improve the signal-to-noise ratio of diffusion MRI measurements and enable a wider range of acquisition parameters that are beneficial for microstructural imaging. We present a comprehensive diffusion MRI dataset of 26 healthy participants acquired on the MGH-USC 3 T Connectome scanner equipped with 300 mT/m maximum gradient strength and a custom-built 64-channel head coil. For each participant, the one-hour long acquisition systematically sampled the accessible diffusion measurement space, including two diffusion times (19 and 49 ms), eight gradient strengths linearly spaced between 30 mT/m and 290 mT/m for each diffusion time, and 32 or 64 uniformly distributed directions. The diffusion MRI data were preprocessed to correct for gradient nonlinearity, eddy currents, and susceptibility induced distortions. In addition, scan/rescan data from a subset of seven individuals were also acquired and provided. The MGH Connectome Diffusion Microstructure Dataset (CDMD) may serve as a test bed for the development of new data analysis methods, such as fiber orientation estimation, tractography and microstructural modelling.
Collapse
Affiliation(s)
- Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Thomas Witzel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Maya N Polackal
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
| | - Ned A Ohringer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
| | - Chanon Ngamsombat
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
| | - Andrew W Russo
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Natalya Machado
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Kristina Brewer
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Boris Keil
- Department of Life Science Engineering, Institute of Medical Physics and Radiation Protection, Giessen, Germany
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Eric C Klawiter
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States.
- Harvard Medical School, Boston, Massachusetts, United States.
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States.
| |
Collapse
|
40
|
Demir S, Clifford B, Lo WC, Tabari A, Goncalves Filho ALM, Lang M, Cauley SF, Setsompop K, Bilgic B, Lev MH, Schaefer PW, Rapalino O, Huang SY, Hilbert T, Feiweier T, Conklin J. Optimization of magnetization transfer contrast for EPI FLAIR brain imaging. Magn Reson Med 2022; 87:2380-2387. [PMID: 34985151 PMCID: PMC8847235 DOI: 10.1002/mrm.29141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 12/10/2021] [Accepted: 12/11/2021] [Indexed: 11/06/2022]
Abstract
PURPOSE To evaluate the impact of magnetization transfer (MT) on brain tissue contrast in turbo-spin-echo (TSE) and EPI fluid-attenuated inversion recovery (FLAIR) images, and to optimize an MT-prepared EPI FLAIR pulse sequence to match the tissue contrast of a clinical reference TSE FLAIR protocol. METHODS Five healthy volunteers underwent 3T brain MRI, including single slice TSE FLAIR, multi-slice TSE FLAIR, EPI FLAIR without MT-preparation, and MT-prepared EPI FLAIR with variations of the MT-preparation parameters, including number of preparation pulses, pulse amplitude, and resonance offset. Automated co-registration and gray matter (GM) versus white matter (WM) segmentation was performed using a T1-MPRAGE acquisition, and the GM versus WM signal intensity ratio (contrast ratio) was calculated for each FLAIR acquisition. RESULTS Without MT preparation, EPI FLAIR showed poor tissue contrast (contrast ratio = 0.98), as did single slice TSE FLAIR. Multi-slice TSE FLAIR provided high tissue contrast (contrast ratio = 1.14). MT-prepared EPI FLAIR closely approximated the contrast of the multi-slice TSE FLAIR images for two combinations of the MT-preparation parameters (contrast ratio = 1.14). Optimized MT-prepared EPI FLAIR provided a 50% reduction in scan time compared to the reference TSE FLAIR acquisition. CONCLUSION Optimized MT-prepared EPI FLAIR provides comparable brain tissue contrast to the multi-slice TSE FLAIR images used in clinical practice.
Collapse
Affiliation(s)
- Serdest Demir
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Bryan Clifford
- Siemens Medical Solutions USA, Boston, Massachusetts, USA
| | - Wei-Ching Lo
- Siemens Medical Solutions USA, Boston, Massachusetts, USA
| | - Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | - Min Lang
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Stephen F Cauley
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Berkin Bilgic
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Michael H Lev
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Pamela W Schaefer
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Otto Rapalino
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Susie Y Huang
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA
| | | | | | - John Conklin
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA
| |
Collapse
|
41
|
Polak D, Splitthoff DN, Clifford B, Lo WC, Huang SY, Conklin J, Wald LL, Setsompop K, Cauley S. Scout accelerated motion estimation and reduction (SAMER). Magn Reson Med 2022; 87:163-178. [PMID: 34390505 PMCID: PMC8616778 DOI: 10.1002/mrm.28971] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 06/29/2021] [Accepted: 07/26/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE To demonstrate a navigator/tracking-free retrospective motion estimation technique that facilitates clinically acceptable reconstruction time. METHODS Scout accelerated motion estimation and reduction (SAMER) uses a single 3-5 s, low-resolution scout scan and a novel sequence reordering to independently determine motion states by minimizing the data-consistency error in a SENSE plus motion forward model. This eliminates time-consuming alternating optimization as no updates to the imaging volume are required during the motion estimation. The SAMER approach was assessed quantitatively through extensive simulation and was evaluated in vivo across multiple motion scenarios and clinical imaging contrasts. Finally, SAMER was synergistically combined with advanced encoding (Wave-CAIPI) to facilitate rapid motion-free imaging. RESULTS The highly accelerated scout provided sufficient information to achieve accurate motion trajectory estimation (accuracy ~0.2 mm or degrees). The novel sequence reordering improved the stability of the motion parameter estimation and image reconstruction while preserving the clinical imaging contrast. Clinically acceptable computation times for the motion estimation (~4 s/shot) are demonstrated through a fully separable (non-alternating) motion search across the shots. Substantial artifact reduction was demonstrated in vivo as well as corresponding improvement in the quantitative error metric. Finally, the extension of SAMER to Wave-encoding enabled rapid high-quality imaging at up to R = 9-fold acceleration. CONCLUSION SAMER significantly improved the computational scalability for retrospective motion estimation and correction.
Collapse
Affiliation(s)
- Daniel Polak
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Siemens Healthcare GmbH, Erlangen, Germany
| | | | | | | | - Susie Y. Huang
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - John Conklin
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Lawrence L. Wald
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford School of Medicine, Stanford, California, USA
| | - Stephen Cauley
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
42
|
Clifford B, Conklin J, Huang SY, Feiweier T, Hosseini Z, Goncalves Filho ALM, Tabari A, Demir S, Lo WC, Longo MGF, Lev M, Schaefer P, Rapalino O, Setsompop K, Bilgic B, Cauley S. An artificial intelligence-accelerated 2-minute multi-shot echo planar imaging protocol for comprehensive high-quality clinical brain imaging. Magn Reson Med 2021; 87:2453-2463. [PMID: 34971463 DOI: 10.1002/mrm.29117] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 10/29/2021] [Accepted: 11/22/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE We introduce and validate an artificial intelligence (AI)-accelerated multi-shot echo-planar imaging (msEPI)-based method that provides T1w, T2w, T 2 ∗ , T2-FLAIR, and DWI images with high SNR, high tissue contrast, low specific absorption rates (SAR), and minimal distortion in 2 minutes. METHODS The rapid imaging technique combines a novel machine learning (ML) scheme to limit g-factor noise amplification and improve SNR, a magnetization transfer preparation module to provide clinically desirable contrast, and high per-shot EPI undersampling factors to reduce distortion. The ML training and image reconstruction incorporates a tunable parameter for controlling the level of denoising/smoothness. The performance of the reconstruction method is evaluated across various acceleration factors, contrasts, and SNR conditions. The 2-minute protocol is directly compared to a 10-minute clinical reference protocol through deployment in a clinical setting, where five representative cases with pathology are examined. RESULTS Optimization of custom msEPI sequences and protocols was performed to balance acquisition efficiency and image quality compared to the five-fold longer clinical reference. Training data from 16 healthy subjects across multiple contrasts and orientations were used to produce ML networks at various acceleration levels. The flexibility of the ML reconstruction was demonstrated across SNR levels, and an optimized regularization was determined through radiological review. Network generalization toward novel pathology, unobserved during training, was illustrated in five clinical case studies with clinical reference images provided for comparison. CONCLUSION The rapid 2-minute msEPI-based protocol with tunable ML reconstruction allows for advantageous trade-offs between acquisition speed, SNR, and tissue contrast when compared to the five-fold slower standard clinical reference exam.
Collapse
Affiliation(s)
- Bryan Clifford
- Siemens Medical Solutions USA, Boston, Massachusetts, USA
| | - John Conklin
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Susie Y Huang
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | | | | | - Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Serdest Demir
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wei-Ching Lo
- Siemens Medical Solutions USA, Boston, Massachusetts, USA
| | | | - Michael Lev
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Pam Schaefer
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Otto Rapalino
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Kawin Setsompop
- Department of Radiology and Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Berkin Bilgic
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Stephen Cauley
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
43
|
Li Z, Tian Q, Ngamsombat C, Cartmell S, Conklin J, Filho ALMG, Lo WC, Wang G, Ying K, Setsompop K, Fan Q, Bilgic B, Cauley S, Huang SY. High-fidelity fast volumetric brain MRI using synergistic wave-controlled aliasing in parallel imaging and a hybrid denoising generative adversarial network (HDnGAN). Med Phys 2021; 49:1000-1014. [PMID: 34961944 DOI: 10.1002/mp.15427] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 11/22/2021] [Accepted: 12/12/2021] [Indexed: 11/08/2022] Open
Abstract
PURPOSE The goal of this study is to leverage an advanced fast imaging technique, wave-controlled aliasing in parallel imaging (Wave-CAIPI), and a generative adversarial network (GAN) for denoising to achieve accelerated high-quality high-signal-to-noise-ratio (SNR) volumetric MRI. METHODS Three-dimensional (3D) T2 -weighted fluid-attenuated inversion recovery (FLAIR) image data were acquired on 33 multiple sclerosis (MS) patients using a prototype Wave-CAIPI sequence (acceleration factor R = 3×2, 2.75 minutes) and a standard T2 -SPACE FLAIR sequence (R = 2, 7.25 minutes). A hybrid denoising GAN entitled "HDnGAN" consisting of a 3D generator and a 2D discriminator was proposed to denoise highly accelerated Wave-CAIPI images. HDnGAN benefits from the improved image synthesis performance provided by the 3D generator and increased training samples from a limited number of patients for training the 2D discriminator. HDnGAN was trained and validated on data from 25 MS patients with the standard FLAIR images as the target and evaluated on data from 8 MS patients not seen during training. HDnGAN was compared to other denoising methods including AONLM, BM4D, MU-Net, and 3D GAN in qualitative and quantitative analysis of output images using the mean squared error (MSE) and VGG perceptual loss compared to standard FLAIR images, and a reader assessment by two neuroradiologists regarding sharpness, SNR, lesion conspicuity, and overall quality. Finally, the performance of these denoising methods was compared at higher noise levels using simulated data with added Rician noise. RESULTS HDnGAN effectively denoised low-SNR Wave-CAIPI images with sharpness and rich textural details, which could be adjusted by controlling the contribution of the adversarial loss to the total loss when training the generator. Quantitatively, HDnGAN (λ = 10-3 ) achieved low MSE and the lowest VGG perceptual loss. The reader study showed that HDnGAN (λ = 10-3 ) significantly improved the SNR of Wave-CAIPI images (P<0.001), outperformed AONLM (P = 0.015), BM4D (P<0.001), MU-Net (P<0.001) and 3D GAN (λ = 10-3 ) (P<0.001) regarding image sharpness, and outperformed MU-Net (P<0.001) and 3D GAN (λ = 10-3 ) (P = 0.001) regarding lesion conspicuity. The overall quality score of HDnGAN (λ = 10-3 ) (4.25±0.43) was significantly higher than those from Wave-CAIPI (3.69±0.46, P = 0.003), BM4D (3.50±0.71, P = 0.001), MU-Net (3.25±0.75, P<0.001), and 3D GAN (λ = 10-3 ) (3.50±0.50, P<0.001), with no significant difference compared to standard FLAIR images (4.38±0.48, P = 0.333). The advantages of HDnGAN over other methods were more obvious at higher noise levels. CONCLUSION HDnGAN provides robust and feasible denoising while preserving rich textural detail in empirical volumetric MRI data. Our study using empirical patient data and systematic evaluation supports the use of HDnGAN in combination with modern fast imaging techniques such as Wave-CAIPI to achieve high-fidelity fast volumetric MRI and represents an important step to the clinical translation of GANs. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Ziyu Li
- Department of Biomedical Engineering, Tsinghua University, Beijing, P.R. China
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Chanon Ngamsombat
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Mahidol, Thailand
| | - Samuel Cartmell
- Department of Radiology, Massachusetts General Hospital, Boston, USA
| | - John Conklin
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA.,Department of Radiology, Massachusetts General Hospital, Boston, USA
| | - Augusto Lio M Gonçalves Filho
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Massachusetts General Hospital, Boston, USA
| | | | - Guangzhi Wang
- Department of Biomedical Engineering, Tsinghua University, Beijing, P.R. China
| | - Kui Ying
- Department of Engineering Physics, Tsinghua University, Beijing, P. R. China
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stephen Cauley
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| |
Collapse
|
44
|
Huang SY, Witzel T, Keil B, Scholz A, Davids M, Dietz P, Rummert E, Ramb R, Kirsch JE, Yendiki A, Fan Q, Tian Q, Ramos-Llordén G, Lee HH, Nummenmaa A, Bilgic B, Setsompop K, Wang F, Avram AV, Komlosh M, Benjamini D, Magdoom KN, Pathak S, Schneider W, Novikov DS, Fieremans E, Tounekti S, Mekkaoui C, Augustinack J, Berger D, Shapson-Coe A, Lichtman J, Basser PJ, Wald LL, Rosen BR. Connectome 2.0: Developing the next-generation ultra-high gradient strength human MRI scanner for bridging studies of the micro-, meso- and macro-connectome. Neuroimage 2021; 243:118530. [PMID: 34464739 PMCID: PMC8863543 DOI: 10.1016/j.neuroimage.2021.118530] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/10/2021] [Accepted: 08/27/2021] [Indexed: 11/26/2022] Open
Abstract
The first phase of the Human Connectome Project pioneered advances in MRI technology for mapping the macroscopic structural connections of the living human brain through the engineering of a whole-body human MRI scanner equipped with maximum gradient strength of 300 mT/m, the highest ever achieved for human imaging. While this instrument has made important contributions to the understanding of macroscale connectional topology, it has also demonstrated the potential of dedicated high-gradient performance scanners to provide unparalleled in vivo assessment of neural tissue microstructure. Building on the initial groundwork laid by the original Connectome scanner, we have now embarked on an international, multi-site effort to build the next-generation human 3T Connectome scanner (Connectome 2.0) optimized for the study of neural tissue microstructure and connectional anatomy across multiple length scales. In order to maximize the resolution of this in vivo microscope for studies of the living human brain, we will push the diffusion resolution limit to unprecedented levels by (1) nearly doubling the current maximum gradient strength from 300 mT/m to 500 mT/m and tripling the maximum slew rate from 200 T/m/s to 600 T/m/s through the design of a one-of-a-kind head gradient coil optimized to minimize peripheral nerve stimulation; (2) developing high-sensitivity multi-channel radiofrequency receive coils for in vivo and ex vivo human brain imaging; (3) incorporating dynamic field monitoring to minimize image distortions and artifacts; (4) developing new pulse sequences to integrate the strongest diffusion encoding and highest spatial resolution ever achieved in the living human brain; and (5) calibrating the measurements obtained from this next-generation instrument through systematic validation of diffusion microstructural metrics in high-fidelity phantoms and ex vivo brain tissue at progressively finer scales with accompanying diffusion simulations in histology-based micro-geometries. We envision creating the ultimate diffusion MRI instrument capable of capturing the complex multi-scale organization of the living human brain - from the microscopic scale needed to probe cellular geometry, heterogeneity and plasticity, to the mesoscopic scale for quantifying the distinctions in cortical structure and connectivity that define cyto- and myeloarchitectonic boundaries, to improvements in estimates of macroscopic connectivity.
Collapse
Affiliation(s)
- Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | | | - Boris Keil
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Alina Scholz
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Mathias Davids
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | | | | | - John E Kirsch
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Kawin Setsompop
- Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, CA, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexandru V Avram
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Michal Komlosh
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Dan Benjamini
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Kulam Najmudeen Magdoom
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Sudhir Pathak
- Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Walter Schneider
- Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA
| | - Slimane Tounekti
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Choukri Mekkaoui
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jean Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniel Berger
- Department of Molecular and Cell Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Alexander Shapson-Coe
- Department of Molecular and Cell Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Jeff Lichtman
- Department of Molecular and Cell Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Peter J Basser
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
45
|
Vachha B, Huang SY. MRI with ultrahigh field strength and high-performance gradients: challenges and opportunities for clinical neuroimaging at 7 T and beyond. Eur Radiol Exp 2021; 5:35. [PMID: 34435246 PMCID: PMC8387544 DOI: 10.1186/s41747-021-00216-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 03/30/2021] [Indexed: 12/12/2022] Open
Abstract
Research in ultrahigh magnetic field strength combined with ultrahigh and ultrafast gradient technology has provided enormous gains in sensitivity, resolution, and contrast for neuroimaging. This article provides an overview of the technical advantages and challenges of performing clinical neuroimaging studies at ultrahigh magnetic field strength combined with ultrahigh and ultrafast gradient technology. Emerging clinical applications of 7-T MRI and state-of-the-art gradient systems equipped with up to 300 mT/m gradient strength are reviewed, and the impact and benefits of such advances to anatomical, structural and functional MRI are discussed in a variety of neurological conditions. Finally, an outlook and future directions for ultrahigh field MRI combined with ultrahigh and ultrafast gradient technology in neuroimaging are examined.
Collapse
Affiliation(s)
- Behroze Vachha
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, 149 13th Street, Room 2301, Charlestown, MA, 02129, USA.
| |
Collapse
|
46
|
Mancini M, Tian Q, Fan Q, Cercignani M, Huang SY. Dissecting whole-brain conduction delays through MRI microstructural measures. Brain Struct Funct 2021; 226:2651-2663. [PMID: 34390416 PMCID: PMC8448685 DOI: 10.1007/s00429-021-02358-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 07/28/2021] [Indexed: 01/24/2023]
Abstract
Network models based on structural connectivity have been increasingly used as the blueprint for large-scale simulations of the human brain. As the nodes of this network are distributed through the cortex and interconnected by white matter pathways with different characteristics, modeling the associated conduction delays becomes important. The goal of this study is to estimate and characterize these delays directly from the brain structure. To achieve this, we leveraged microstructural measures from a combination of advanced magnetic resonance imaging acquisitions and computed the main determinants of conduction velocity, namely axonal diameter and myelin content. Using the model proposed by Rushton, we used these measures to calculate the conduction velocity and estimated the associated delays using tractography. We observed that both the axonal diameter and conduction velocity distributions presented a rather constant trend across different connection lengths, with resulting delays that scale linearly with the connection length. Relying on insights from graph theory and Kuramoto simulations, our results support the approximation of constant conduction velocity but also show path- and region-specific differences.
Collapse
Affiliation(s)
- Matteo Mancini
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK. .,Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK. .,NeuroPoly Lab, Polytechnique Montréal, Montréal, Canada.
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Mara Cercignani
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| |
Collapse
|
47
|
Edlow BL, Conklin J, Huang SY. Reader Response: Critical Illness-Associated Cerebral Microbleeds in COVID-19 Acute Respiratory Distress Syndrome. Neurology 2021; 97:254. [PMID: 34341081 DOI: 10.1212/wnl.0000000000012364] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
|
48
|
Ngamsombat C, Gonçalves Filho ALM, Longo MGF, Cauley SF, Setsompop K, Kirsch JE, Tian Q, Fan Q, Polak D, Liu W, Lo WC, Gilberto González R, Schaefer PW, Rapalino O, Conklin J, Huang SY. Evaluation of Ultrafast Wave-Controlled Aliasing in Parallel Imaging 3D-FLAIR in the Visualization and Volumetric Estimation of Cerebral White Matter Lesions. AJNR Am J Neuroradiol 2021; 42:1584-1590. [PMID: 34244127 DOI: 10.3174/ajnr.a7191] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 03/29/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND AND PURPOSE Our aim was to evaluate an ultrafast 3D-FLAIR sequence using Wave-controlled aliasing in parallel imaging encoding (Wave-FLAIR) compared with standard 3D-FLAIR in the visualization and volumetric estimation of cerebral white matter lesions in a clinical setting. MATERIALS AND METHODS Forty-two consecutive patients underwent 3T brain MR imaging, including standard 3D-FLAIR (acceleration factor = 2, scan time = 7 minutes 50 seconds) and resolution-matched ultrafast Wave-FLAIR sequences (acceleration factor = 6, scan time = 2 minutes 45 seconds for the 20-channel coil; acceleration factor = 9, scan time = 1 minute 50 seconds for the 32-channel coil) as part of clinical evaluation for demyelinating disease. Automated segmentation of cerebral white matter lesions was performed using the Lesion Segmentation Tool in SPM. Student t tests, intraclass correlation coefficients, relative lesion volume difference, and Dice similarity coefficients were used to compare volumetric measurements among sequences. Two blinded neuroradiologists evaluated the visualization of white matter lesions, artifacts, and overall diagnostic quality using a predefined 5-point scale. RESULTS Standard and Wave-FLAIR sequences showed excellent agreement of lesion volumes with an intraclass correlation coefficient of 0.99 and mean Dice similarity coefficient of 0.97 (SD, 0.05) (range, 0.84-0.99). Wave-FLAIR was noninferior to standard FLAIR for visualization of lesions and motion. The diagnostic quality for Wave-FLAIR was slightly greater than for standard FLAIR for infratentorial lesions (P < .001), and there were fewer pulsation artifacts on Wave-FLAIR compared with standard FLAIR (P < .001). CONCLUSIONS Ultrafast Wave-FLAIR provides superior visualization of infratentorial lesions while preserving overall diagnostic quality and yields white matter lesion volumes comparable with those estimated using standard FLAIR. The availability of ultrafast Wave-FLAIR may facilitate the greater use of 3D-FLAIR sequences in the evaluation of patients with suspected demyelinating disease.
Collapse
Affiliation(s)
- C Ngamsombat
- From the Department of (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.).,Athinoula A. Martinos Center for Biomedical Imaging (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F, D.P., J.C., S.Y.H.), Massachusetts General Hospital, Boston, Massachusetts.,Department of Radiology (C.N.), Faculty of Medicine, Siriraj Hospital, Mahidol University, Thailand
| | - A L M Gonçalves Filho
- From the Department of (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.).,Athinoula A. Martinos Center for Biomedical Imaging (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F, D.P., J.C., S.Y.H.), Massachusetts General Hospital, Boston, Massachusetts.,Harvard Medical School (A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.), Boston, Massachusetts
| | - M G F Longo
- From the Department of (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.).,Athinoula A. Martinos Center for Biomedical Imaging (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F, D.P., J.C., S.Y.H.), Massachusetts General Hospital, Boston, Massachusetts.,Harvard Medical School (A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.), Boston, Massachusetts
| | - S F Cauley
- From the Department of (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.).,Athinoula A. Martinos Center for Biomedical Imaging (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F, D.P., J.C., S.Y.H.), Massachusetts General Hospital, Boston, Massachusetts.,Harvard Medical School (A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.), Boston, Massachusetts
| | - K Setsompop
- From the Department of (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.).,Athinoula A. Martinos Center for Biomedical Imaging (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F, D.P., J.C., S.Y.H.), Massachusetts General Hospital, Boston, Massachusetts.,Harvard Medical School (A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.), Boston, Massachusetts.,Harvard-MIT Division of Health Sciences and Technology (K.S., S.Y.H.), Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - J E Kirsch
- From the Department of (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.).,Athinoula A. Martinos Center for Biomedical Imaging (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F, D.P., J.C., S.Y.H.), Massachusetts General Hospital, Boston, Massachusetts.,Harvard Medical School (A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.), Boston, Massachusetts
| | - Q Tian
- From the Department of (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.).,Athinoula A. Martinos Center for Biomedical Imaging (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F, D.P., J.C., S.Y.H.), Massachusetts General Hospital, Boston, Massachusetts.,Harvard Medical School (A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.), Boston, Massachusetts
| | - Q Fan
- From the Department of (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.).,Athinoula A. Martinos Center for Biomedical Imaging (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F, D.P., J.C., S.Y.H.), Massachusetts General Hospital, Boston, Massachusetts.,Harvard Medical School (A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.), Boston, Massachusetts
| | - D Polak
- Athinoula A. Martinos Center for Biomedical Imaging (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F, D.P., J.C., S.Y.H.), Massachusetts General Hospital, Boston, Massachusetts.,Department of Physics and Astronomy (D.P.), Heidelberg University, Heidelberg, Germany.,Siemens Healthcare GmbH, (D.P., W.-C.L.), Erlangen, Germany
| | - W Liu
- Siemens Shenzhen Magnetic Resonance Ltd (W.L.), Shenzhen, China
| | - W-C Lo
- Siemens Healthcare GmbH, (D.P., W.-C.L.), Erlangen, Germany
| | - R Gilberto González
- From the Department of (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.).,Harvard Medical School (A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.), Boston, Massachusetts
| | - P W Schaefer
- From the Department of (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.).,Harvard Medical School (A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.), Boston, Massachusetts
| | - O Rapalino
- From the Department of (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.).,Harvard Medical School (A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.), Boston, Massachusetts
| | - J Conklin
- From the Department of (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.).,Athinoula A. Martinos Center for Biomedical Imaging (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F, D.P., J.C., S.Y.H.), Massachusetts General Hospital, Boston, Massachusetts.,Harvard Medical School (A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.), Boston, Massachusetts
| | - S Y Huang
- From the Department of (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.) .,Athinoula A. Martinos Center for Biomedical Imaging (C.N., A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F, D.P., J.C., S.Y.H.), Massachusetts General Hospital, Boston, Massachusetts.,Harvard Medical School (A.L.M.G.F., M.G.F.L., S.F.C., K.S., J.E.K., Q.T., Q.F., R.G.G., P.W.S., O.R., J.C., S.Y.H.), Boston, Massachusetts.,Harvard-MIT Division of Health Sciences and Technology (K.S., S.Y.H.), Massachusetts Institute of Technology, Cambridge, Massachusetts
| |
Collapse
|
49
|
Goncalves Filho ALM, Longo MGF, Conklin J, Cauley SF, Polak D, Liu W, Splitthoff DN, Lo WC, Kirsch JE, Setsompop K, Schaefer PW, Huang SY, Rapalino O. MRI Highly Accelerated Wave-CAIPI T1-SPACE versus Standard T1-SPACE to detect brain gadolinium-enhancing lesions at 3T. J Neuroimaging 2021; 31:893-901. [PMID: 34081374 DOI: 10.1111/jon.12893] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 05/10/2021] [Accepted: 05/21/2021] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE High-resolution three-dimensional (3D) post-contrast imaging of the brain is essential for comprehensive evaluation of inflammatory, neoplastic, and neurovascular diseases of the brain. 3D T1-weighted spin-echo-based sequences offer increased sensitivity for the detection of enhancing lesions but are relatively prolonged examinations. We evaluated whether a highly accelerated Wave-controlled aliasing in parallel imaging (Wave-CAIPI) post-contrast 3D T1-sampling perfection with application-optimized contrasts using different flip angle evolutions (T1-SPACE) sequence (Wave-T1-SPACE) was noninferior to the standard high-resolution 3D T1-SPACE sequence for visualizing enhancing lesions with comparable diagnostic quality. METHODS One hundred and three consecutive patients were prospectively evaluated with a standard post-contrast 3D T1-SPACE sequence (acquisition time [TA] = 4 min 19 s) and an optimized Wave-CAIPI 3D T1-SPACE sequence (TA = 1 min 40 s) that was nearly three times faster than the standard sequence. Two blinded neuroradiologists performed a head-to-head comparison to evaluate the visualization of enhancing pathology, perception of artifacts, and overall diagnostic quality. A 15% margin was used to test whether post-contrast Wave-T1-SPACE was noninferior to standard T1-SPACE. RESULTS Wave-T1-SPACE was noninferior to standard T1-SPACE for delineating parenchymal and meningeal enhancing pathology (p < 0.01). Wave-T1-SPACE showed marginally higher background noise compared to the standard sequence and was noninferior in the overall diagnostic quality (p = 0.03). CONCLUSIONS Our findings show that Wave-T1-SPACE was noninferior to standard T1-SPACE for visualization of enhancing pathology and overall diagnostic quality with a three-fold reduction in acquisition time compared to the standard sequence. Wave-T1-SPACE may be used to accelerate 3D post-contrast T1-weighted spin-echo imaging without loss of clinically important information.
Collapse
Affiliation(s)
- Augusto Lio M Goncalves Filho
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
| | - M Gabriela Figueiro Longo
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - John Conklin
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Stephen F Cauley
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
| | | | - Wei Liu
- Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | | | - Wei-Ching Lo
- Siemens Medical Solutions, Boston, Massachusetts, USA
| | - John E Kirsch
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Kawin Setsompop
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
| | - Pamela W Schaefer
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Susie Y Huang
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
| | - Otto Rapalino
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
50
|
Wang F, Dong Z, Tian Q, Liao C, Fan Q, Hoge WS, Keil B, Polimeni JR, Wald LL, Huang SY, Setsompop K. In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution. Sci Data 2021; 8:122. [PMID: 33927203 PMCID: PMC8084962 DOI: 10.1038/s41597-021-00904-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 03/26/2021] [Indexed: 01/18/2023] Open
Abstract
We present a whole-brain in vivo diffusion MRI (dMRI) dataset acquired at 760 μm isotropic resolution and sampled at 1260 q-space points across 9 two-hour sessions on a single healthy participant. The creation of this benchmark dataset is possible through the synergistic use of advanced acquisition hardware and software including the high-gradient-strength Connectom scanner, a custom-built 64-channel phased-array coil, a personalized motion-robust head stabilizer, a recently developed SNR-efficient dMRI acquisition method, and parallel imaging reconstruction with advanced ghost reduction algorithm. With its unprecedented resolution, SNR and image quality, we envision that this dataset will have a broad range of investigational, educational, and clinical applications that will advance the understanding of human brain structures and connectivity. This comprehensive dataset can also be used as a test bed for new modeling, sub-sampling strategies, denoising and processing algorithms, potentially providing a common testing platform for further development of in vivo high resolution dMRI techniques. Whole brain anatomical T1-weighted and T2-weighted images at submillimeter scale along with field maps are also made available.
Collapse
Affiliation(s)
- Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA.
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA.
| | - Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - W Scott Hoge
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Boris Keil
- Department of Life Science Engineering, Institute of Medical Physics and Radiation Protection, Giessen, Germany
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| |
Collapse
|