1
|
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
|
2
|
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
|
3
|
Lankinen K, Wang R, Tian Q, Wang QM, Perry BJ, Green JR, Kimberley TJ, Ahveninen J, Li S. Individualized white matter connectivity of the articulatory pathway: An ultra-high field study. Brain Lang 2024; 250:105391. [PMID: 38354542 PMCID: PMC10940181 DOI: 10.1016/j.bandl.2024.105391] [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: 05/11/2023] [Revised: 01/29/2024] [Accepted: 02/07/2024] [Indexed: 02/16/2024]
Abstract
In current sensorimotor theories pertaining to speech perception, there is a notable emphasis on the involvement of the articulatory-motor system in the processing of speech sounds. Using ultra-high field diffusion-weighted imaging at 7 Tesla, we visualized the white matter tracts connected to areas activated during a simple speech-sound production task in 18 healthy right-handed adults. Regions of interest for white matter tractography were individually determined through 7T functional MRI (fMRI) analyses, based on activations during silent vocalization tasks. These precentral seed regions, activated during the silent production of a lip-vowel sound, demonstrated anatomical connectivity with posterior superior temporal gyrus areas linked to the auditory perception of phonetic sounds. Our study provides a macrostructural foundation for understanding connections in speech production and underscores the central role of the articulatory motor system in speech perception. These findings highlight the value of ultra-high field 7T MR acquisition in unraveling the neural underpinnings of speech.
Collapse
Affiliation(s)
- Kaisu Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Ruopeng Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Qiyuan Tian
- Harvard Medical School, Boston, MA, United States
| | - Qing Mei Wang
- Stroke Biological Recovery Laboratory, Spaulding Rehabilitation Hospital, the teaching affiliate of Harvard Medical School, Charlestown, MA, United States
| | - Bridget J Perry
- Department of Communication Sciences and Disorders, MGH Institute of Health Professions Boston, MA, United States
| | - Jordan R Green
- Department of Communication Sciences and Disorders, MGH Institute of Health Professions Boston, MA, United States
| | - Teresa J Kimberley
- Department of Physical Therapy, School of Health and Rehabilitation Sciences, MGH Institute of Health Professions, Boston, MA, United States
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Shasha Li
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
| |
Collapse
|
4
|
Hegarty JP, Monterrey JC, Tian Q, Cleveland SC, Gong X, Phillips JM, Wolke ON, McNab JA, Hallmayer JF, Reiss AL, Hardan AY, Lazzeroni LC. A Twin Study of Altered White Matter Heritability in Youth With Autism Spectrum Disorder. J Am Acad Child Adolesc Psychiatry 2024; 63:65-79. [PMID: 37406770 PMCID: PMC10802971 DOI: 10.1016/j.jaac.2023.05.030] [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] [Received: 07/21/2021] [Revised: 05/08/2023] [Accepted: 06/26/2023] [Indexed: 07/07/2023]
Abstract
OBJECTIVE White matter alterations are frequently reported in autism spectrum disorder (ASD), yet the etiology is currently unknown. The objective of this investigation was to examine, for the first time, the impact of genetic and environmental factors on white matter microstructure in twins with ASD compared to control twins without ASD. METHOD Diffusion-weighted MRIs were obtained from same-sex twin pairs (6-15 years of age) in which at least 1 twin was diagnosed with ASD or neither twin exhibited a history of neurological or psychiatric disorders. Fractional anisotropy (FA) and mean diffusivity (MD) were examined across different white matter tracts in the brain, and statistical and twin modeling were completed to assess the proportion of variation associated with additive genetic (A) and common/shared (C) or unique (E) environmental factors. We also developed a novel Twin-Pair Difference Score analysis method that produces quantitative estimates of the genetic and environmental contributions to shared covariance between different brain and behavioral traits. RESULTS Good-quality data were available from 84 twin pairs, 50 ASD pairs (32 concordant for ASD [16 monozygotic; 16 dizygotic], 16 discordant for ASD [3 monozygotic; 13 dizygotic], and 2 pairs in which 1 twin had ASD and the other exhibited some subthreshold symptoms [1 monozygotic; 1 dizygotic]) and 34 control pairs (20 monozygotic; 14 dizygotic). Average FA and MD across the brain, respectively, were primarily genetically mediated in both control twins (A = 0.80, 95% CI [0.57, 1.02]; A = 0.80 [0.55, 1.04]) and twins concordant for having ASD (A = 0.71 [0.33, 1.09]; A = 0.84 [0.32,1.36]). However, there were also significant tract-specific differences between groups. For instance, genetic effects on commissural fibers were primarily associated with differences in general cognitive abilities and perhaps some diagnostic differences for ASD because Twin-Pair Difference-Score analysis indicated that genetic factors may have contributed to ∼40% to 50% of the covariation between IQ scores and FA of the corpus callosum. Conversely, the increased impact of environmental factors on some projection and association fibers were primarily associated with differences in symptom severity in twins with ASD; for example, our analyses suggested that unique environmental factors may have contributed to ∼10% to 20% of the covariation between autism-related symptom severity and FA of the cerebellar peduncles and external capsule. CONCLUSION White matter alterations in youth with ASD are associated with both genetic contributions and potentially increased vulnerability or responsivity to environmental influences. DIVERSITY & INCLUSION STATEMENT We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper self-identifies as living with a disability. The author list of this paper includes contributors from the location and/or community where the research was conducted and they participated in the data collection, design, analysis, and/or interpretation of the work.
Collapse
Affiliation(s)
- John P Hegarty
- Stanford University School of Medicine, Stanford, California.
| | | | - Qiyuan Tian
- Tsinghua University School of Medicine, Beijing, China
| | - Sue C Cleveland
- Stanford University School of Medicine, Stanford, California
| | - Xinyi Gong
- Stanford University School of Medicine, Stanford, California
| | | | - Olga N Wolke
- Stanford University School of Medicine, Stanford, California
| | | | | | - Allan L Reiss
- Stanford University School of Medicine, Stanford, California
| | | | | |
Collapse
|
5
|
Lankinen K, Ahveninen J, Uluç I, Daneshzand M, Mareyam A, Kirsch JE, Polimeni JR, Healy BC, Tian Q, Khan S, Nummenmaa A, Wang QM, Green JR, Kimberley TJ, Li S. Role of articulatory motor networks in perceptual categorization of speech signals: a 7T fMRI study. Cereb Cortex 2023; 33:11517-11525. [PMID: 37851854 PMCID: PMC10724868 DOI: 10.1093/cercor/bhad384] [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: 07/21/2023] [Revised: 09/28/2023] [Accepted: 09/29/2023] [Indexed: 10/20/2023] Open
Abstract
Speech and language processing involve complex interactions between cortical areas necessary for articulatory movements and auditory perception and a range of areas through which these are connected and interact. Despite their fundamental importance, the precise mechanisms underlying these processes are not fully elucidated. We measured BOLD signals from normal hearing participants using high-field 7 Tesla fMRI with 1-mm isotropic voxel resolution. The subjects performed 2 speech perception tasks (discrimination and classification) and a speech production task during the scan. By employing univariate and multivariate pattern analyses, we identified the neural signatures associated with speech production and perception. The left precentral, premotor, and inferior frontal cortex regions showed significant activations that correlated with phoneme category variability during perceptual discrimination tasks. In addition, the perceived sound categories could be decoded from signals in a region of interest defined based on activation related to production task. The results support the hypothesis that articulatory motor networks in the left hemisphere, typically associated with speech production, may also play a critical role in the perceptual categorization of syllables. The study provides valuable insights into the intricate neural mechanisms that underlie speech processing.
Collapse
Affiliation(s)
- Kaisu Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Harvard Medical School, Boston, MA 02115, United States
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Harvard Medical School, Boston, MA 02115, United States
| | - Işıl Uluç
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Harvard Medical School, Boston, MA 02115, United States
| | - Mohammad Daneshzand
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Harvard Medical School, Boston, MA 02115, United States
| | - Azma Mareyam
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, United States
| | - John E Kirsch
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Harvard Medical School, Boston, MA 02115, United States
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Harvard Medical School, Boston, MA 02115, United States
| | - Brian C Healy
- Partners Multiple Sclerosis Center, Brigham and Women's Hospital, Boston, MA 02115, United States
- Department of Neurology, Harvard Medical School, Boston, MA 02115, United States
- Biostatistics Center, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Harvard Medical School, Boston, MA 02115, United States
| | - Sheraz Khan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Harvard Medical School, Boston, MA 02115, United States
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Harvard Medical School, Boston, MA 02115, United States
| | - Qing Mei Wang
- Stroke Biological Recovery Laboratory, Spaulding Rehabilitation Hospital, The Teaching Affiliate of Harvard Medical School, Charlestown, MA 02129, United States
| | - Jordan R Green
- Department of Communication Sciences and Disorders, MGH Institute of Health Professions, Boston, MA 02129, United States
| | - Teresa J Kimberley
- Department of Physical Therapy, School of Health and Rehabilitation Sciences, MGH Institute of Health Professions, Boston, MA 02129, United States
| | - Shasha Li
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Harvard Medical School, Boston, MA 02115, United States
| |
Collapse
|
6
|
Zhao Y, Pei F, Yang N, Sun H, Gao Z, Tian Q, Lu X. [Epidemiological and clinical characteristics of human ocular helaziasis in China from 2011 to 2022 based on bibliometrics]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2023; 35:513-516. [PMID: 38148542 DOI: 10.16250/j.32.1374.2023061] [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] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
OBJECTIVE To understand the clinical and epidemiological characteristics of human ocular thelaziasis patients in China. METHODS Case reports regarding human ocular thelaziasis cases in China were retrieved in international and national electronic databases, including CNKI, VIP, CBM, Traditional Chinese Medical Literature Analysis and Retrieval System, Wanfang Database, PubMed and Web of Science from 2011 to 2022. Patients' gender, age, clinical symptoms, treatment, recurrence, site of infections, time of onset, affected eye, affected sites, number of infected Thelazia callipaeda, sex of T. callipaeda and source of infections were extracted for descriptive analyses. RESULTS A total of 85 eligible publications were included, covering 101 cases of human ocular thelaziasis, including 57 males (56.44%) and 44 females (43.56%) and aged from 3 months to 85 years. The main clinical manifestations included foreign body sensation (56 case-times, 22.49%), eye itching (38 case-times, 15.26%), abnormal or increased secretions (36 case-times, 14.46%), tears (28 case-times, 11.24%) and eye redness (28 case-times, 11.24%), and conjunctival congestion (50 case-times, 41.67%) was the most common clinical sign. The most common main treatment (99/101, 98.02%) was removal of parasites from eyes using ophthalmic forceps, followed by administration with ofloxacin and pranoprofen. In publications presenting thelaziasis recurrence, there were 90 cases without recurrence (97.83%) and 2 cases with recurrence (2.17%). Of all cases, 51.96% were reported in four provinces of Hubei, Shandong, Sichuan, Hebei and Henan, and ocular thelaziasis predominantly occurred in summer (42.19%) and autumn (42.19%). In addition, 56.45% (35/62) had a contact with dogs. CONCLUSIONS The human thelaziasis cases mainly occur in the continental monsoon and subtropical monsoon climate areas such as the Yellow River and the Yangtze River basin, and people of all ages and genders have the disease, with complex clinical symptoms and signs. Personal hygiene is required during the contact with dogs, cats and other animals, and individual protection is required during outdoor activities to prevent thelaziasis.
Collapse
Affiliation(s)
- Y Zhao
- School of Ophthalmology and Optometry, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250014, China
| | - F Pei
- School of Ophthalmology and Optometry, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250014, China
| | - N Yang
- School of Ophthalmology and Optometry, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250014, China
| | - H Sun
- School of Ophthalmology and Optometry, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250014, China
| | - Z Gao
- School of Ophthalmology and Optometry, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250014, China
| | - Q Tian
- School of Ophthalmology and Optometry, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250014, China
- Affiliated Eye Hospital of Shandong University of Traditional Chinese Medicine, Shandong Academy of Eye Disease Prevention and Therapy, Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Jinan, Shandong 250002, China
| | - X Lu
- School of Ophthalmology and Optometry, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250014, China
- Affiliated Eye Hospital of Shandong University of Traditional Chinese Medicine, Shandong Academy of Eye Disease Prevention and Therapy, Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Jinan, Shandong 250002, China
| |
Collapse
|
7
|
Aja-Fernández S, Martín-Martín C, Planchuelo-Gómez Á, Faiyaz A, Uddin MN, Schifitto G, Tiwari A, Shigwan SJ, Kumar Singh R, Zheng T, Cao Z, Wu D, Blumberg SB, Sen S, Goodwin-Allcock T, Slator PJ, Yigit Avci M, Li Z, Bilgic B, Tian Q, Wang X, Tang Z, Cabezas M, Rauland A, Merhof D, Manzano Maria R, Campos VP, Santini T, da Costa Vieira MA, HashemizadehKolowri S, DiBella E, Peng C, Shen Z, Chen Z, Ullah I, Mani M, Abdolmotalleby H, Eckstrom S, Baete SH, Filipiak P, Dong T, Fan Q, de Luis-García R, Tristán-Vega A, Pieciak T. Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies. Neuroimage Clin 2023; 39:103483. [PMID: 37572514 PMCID: PMC10440596 DOI: 10.1016/j.nicl.2023.103483] [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: 03/02/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 08/14/2023]
Abstract
The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.
Collapse
Affiliation(s)
- Santiago Aja-Fernández
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Spain.
| | - Carmen Martín-Martín
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Spain
| | - Álvaro Planchuelo-Gómez
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Spain; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | | | | | | | | | | | | | | | | | - Dan Wu
- Zhejiang University, China
| | | | | | | | | | | | - Zihan Li
- Athinoula A. Martinos Center for Biomedical Imaging, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Zan Chen
- Zhejiang University of Technology, China
| | | | | | | | | | | | | | | | | | - Rodrigo de Luis-García
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Spain
| | - Antonio Tristán-Vega
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Spain
| | - Tomasz Pieciak
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Spain
| |
Collapse
|
8
|
Liao C, Yarach U, Cao X, Iyer SS, Wang N, Kim TH, Tian Q, Bilgic B, Kerr AB, Setsompop K. High-fidelity mesoscale in-vivo diffusion MRI through gSlider-BUDA and circular EPI with S-LORAKS reconstruction. Neuroimage 2023; 275:120168. [PMID: 37187364 PMCID: PMC10451786 DOI: 10.1016/j.neuroimage.2023.120168] [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: 03/06/2023] [Revised: 04/27/2023] [Accepted: 05/12/2023] [Indexed: 05/17/2023] Open
Abstract
PURPOSE To develop a high-fidelity diffusion MRI acquisition and reconstruction framework with reduced echo-train-length for less T2* image blurring compared to typical highly accelerated echo-planar imaging (EPI) acquisitions at sub-millimeter isotropic resolution. METHODS We first proposed a circular-EPI trajectory with partial Fourier sampling on both the readout and phase-encoding directions to minimize the echo-train-length and echo time. We then utilized this trajectory in an interleaved two-shot EPI acquisition with reversed phase-encoding polarity, to aid in the correction of off-resonance-induced image distortions and provide complementary k-space coverage in the missing partial Fourier regions. Using model-based reconstruction with structured low-rank constraint and smooth phase prior, we corrected the shot-to-shot phase variations across the two shots and recover the missing k-space data. Finally, we combined the proposed acquisition/reconstruction framework with an SNR-efficient RF-encoded simultaneous multi-slab technique, termed gSlider, to achieve high-fidelity 720 µm and 500 µm isotropic resolution in-vivo diffusion MRI. RESULTS Both simulation and in-vivo results demonstrate the effectiveness of the proposed acquisition and reconstruction framework to provide distortion-corrected diffusion imaging at the mesoscale with markedly reduced T2*-blurring. The in-vivo results of 720 µm and 500 µm datasets show high-fidelity diffusion images with reduced image blurring and echo time using the proposed approaches. CONCLUSIONS The proposed method provides high-quality distortion-corrected diffusion-weighted images with ∼40% reduction in the echo-train-length and T2* blurring at 500µm-isotropic-resolution compared to standard multi-shot EPI.
Collapse
Affiliation(s)
- Congyu Liao
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Uten Yarach
- Radiologic Technology Department, Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand
| | - Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
| | - Siddharth Srinivasan Iyer
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nan Wang
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Tae Hyung Kim
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Department of Computer Engineering, Hongik University, Seoul, South Korea
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Adam B Kerr
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Stanford Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| |
Collapse
|
9
|
Lankinen K, Ahveninen J, Uluç I, Daneshzand M, Mareyam A, Kirsch JE, Polimeni JR, Healy BC, Tian Q, Khan S, Nummenmaa A, Wang QM, Green JR, Kimberley TJ, Li S. Role of Articulatory Motor Networks in Perceptual Categorization of Speech Signals: A 7 T fMRI Study. bioRxiv 2023:2023.07.02.547409. [PMID: 37461673 PMCID: PMC10349975 DOI: 10.1101/2023.07.02.547409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
BACKGROUND The association between brain regions involved in speech production and those that play a role in speech perception is not yet fully understood. We compared speech production related brain activity with activations resulting from perceptual categorization of syllables using high field 7 Tesla functional magnetic resonance imaging (fMRI) at 1-mm isotropic voxel resolution, enabling high localization accuracy compared to previous studies. METHODS Blood oxygenation level dependent (BOLD) signals were obtained in 20 normal hearing subjects using a simultaneous multi-slice (SMS) 7T echo-planar imaging (EPI) acquisition with whole-head coverage and 1 mm isotropic resolution. In a speech production localizer task, subjects were asked to produce a silent lip-round vowel /u/ in response to the visual cue "U" or purse their lips when they saw the cue "P". In a phoneme discrimination task, subjects were presented with pairs of syllables, which were equiprobably identical or different along an 8-step continuum between the prototypic /ba/ and /da/ sounds. After the presentation of each stimulus pair, the subjects were asked to indicate whether the two syllables they heard were identical or different by pressing one of two buttons. In a phoneme classification task, the subjects heard only one syllable and asked to indicate whether it was /ba/ or /da/. RESULTS Univariate fMRI analyses using a parametric modulation approach suggested that left motor, premotor, and frontal cortex BOLD activations correlate with phoneme category variability in the /ba/-/da/ discrimination task. In contrast, the variability related to acoustic features of the phonemes were the highest in the right primary auditory cortex. Our multivariate pattern analysis (MVPA) suggested that left precentral/inferior frontal cortex areas, which were associated with speech production according to the localizer task, play a role also in perceptual categorization of the syllables. CONCLUSIONS The results support the hypothesis that articulatory motor networks in the left hemisphere that are activated during speech production could also have a role in perceptual categorization of syllables. Importantly, high voxel-resolution combined with advanced coil technology allowed us to pinpoint the exact brain regions involved in both perception and production tasks.
Collapse
Affiliation(s)
- Kaisu Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, US
- Harvard Medical School, Boston, MA, US
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, US
- Harvard Medical School, Boston, MA, US
| | - Işıl Uluç
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, US
- Harvard Medical School, Boston, MA, US
| | - Mohammad Daneshzand
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, US
- Harvard Medical School, Boston, MA, US
| | - Azma Mareyam
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, US
| | - John E. Kirsch
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, US
- Harvard Medical School, Boston, MA, US
| | - Jonathan R. Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, US
- Harvard Medical School, Boston, MA, US
| | - Brian C. Healy
- Harvard Medical School, Boston, MA, US
- Stroke Biological Recovery Laboratory, Spaulding Rehabilitation Hospital, the teaching affiliate of Harvard Medical School, Charlestown, MA, US
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, US
- Harvard Medical School, Boston, MA, US
| | - Sheraz Khan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, US
- Harvard Medical School, Boston, MA, US
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, US
- Harvard Medical School, Boston, MA, US
| | - Qing-mei Wang
- Stroke Biological Recovery Laboratory, Spaulding Rehabilitation Hospital, the teaching affiliate of Harvard Medical School, Charlestown, MA, US
| | - Jordan R. Green
- Department of Communication Sciences and Disorders, MGH Institute of Health Professions Boston, MA, US
| | - Teresa J. Kimberley
- Department of Physical Therapy, School of Health and Rehabilitation Sciences, MGH Institute of Health Professions, Boston, MA, US
| | - Shasha Li
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, US
- Harvard Medical School, Boston, MA, US
| |
Collapse
|
10
|
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
|
11
|
Luo J, Bai X, Huang K, Wang T, Yang R, Li L, Tian Q, Xu R, Li T, Wang Y, Chen Y, Gao P, Chen J, Yang B, Ma Y, Jiao L. Clinical Relevance of Plaque Distribution for Basilar Artery Stenosis. AJNR Am J Neuroradiol 2023; 44:530-535. [PMID: 37024307 PMCID: PMC10171387 DOI: 10.3174/ajnr.a7839] [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: 11/20/2022] [Accepted: 03/01/2023] [Indexed: 04/08/2023]
Abstract
BACKGROUND AND PURPOSE There is no clear association between plaque distribution and postoperative complications in patients with basilar artery atherosclerotic stenosis. The aim of this study was to determine whether plaque distribution and postoperative complications after endovascular treatment for basilar artery stenosis are related. MATERIALS AND METHODS Our study enrolled patients with severe basilar artery stenosis who were scanned with high-resolution MR imaging and followed by DSA before the intervention. According to high-resolution MR imaging, plaques can be classified as ventral, lateral, dorsal, or involved in 2 quadrants. Plaques affecting the proximal, distal, or junctional segments of the basilar artery were classified according to DSA. An experienced independent team assessed ischemic events after the intervention using MR imaging. Further analysis was conducted to determine the relationship between plaque distribution and postoperative complications. RESULTS A total of 140 eligible patients were included in the study, with a postoperative complication rate of 11.4%. These patients were an average age of 61.9 (SD, 7.7) years. Dorsal wall plaques accounted for 34.3% of all plaques, and plaques distal to the anterior-inferior cerebellar artery accounted for 60.7%. Postoperative complications of endovascular treatment were associated with plaques located at the lateral wall (OR = 4.00; 95% CI, 1.21-13.23; P = .023), junctional segment (OR = 8.75; 95% CI, 1.16-66.22; P = .036), and plaque burden (OR = 1.03; 95% CI, 1.01-1.06; P = .042). CONCLUSIONS Plaques with a large burden located at the junctional segment and lateral wall of the basilar artery may increase the likelihood of postoperative complications following endovascular therapy. A larger sample size is needed for future studies.
Collapse
Affiliation(s)
- J Luo
- From the China International Neuroscience Institute (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.), Beijing, China
- Department of Neurosurgery (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.)
| | - X Bai
- From the China International Neuroscience Institute (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.), Beijing, China
- Department of Neurosurgery (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.)
| | - K Huang
- The Eighth Affiliated Hospital (K.H.), SUN YAT-SEN University, Shenzhen, Guangdong Province, China
| | - T Wang
- From the China International Neuroscience Institute (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.), Beijing, China
- Department of Neurosurgery (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.)
| | - R Yang
- From the China International Neuroscience Institute (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.), Beijing, China
- Department of Neurosurgery (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.)
| | - L Li
- From the China International Neuroscience Institute (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.), Beijing, China
- Department of Neurosurgery (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.)
| | - Q Tian
- Xuanwu Hospital, Beijing Key Laboratory of Clinical Epidemiology (Q.T.), School of Public Health
| | - R Xu
- From the China International Neuroscience Institute (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.), Beijing, China
- Department of Neurosurgery (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.)
| | - T Li
- From the China International Neuroscience Institute (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.), Beijing, China
- Department of Neurosurgery (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.)
| | - Y Wang
- From the China International Neuroscience Institute (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.), Beijing, China
- Department of Neurosurgery (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.)
| | - Y Chen
- From the China International Neuroscience Institute (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.), Beijing, China
- Department of Neurosurgery (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.)
| | - P Gao
- From the China International Neuroscience Institute (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.), Beijing, China
- Department of Neurosurgery (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.)
- Department of Interventional Radiology (P.G., L.J.), Xuanwu Hospital, Capital Medical University, Beijing, China
| | - J Chen
- From the China International Neuroscience Institute (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.), Beijing, China
- Department of Neurosurgery (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.)
| | - B Yang
- From the China International Neuroscience Institute (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.), Beijing, China
- Department of Neurosurgery (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.)
| | - Y Ma
- From the China International Neuroscience Institute (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.), Beijing, China
- Department of Neurosurgery (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.)
| | - L Jiao
- From the China International Neuroscience Institute (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.), Beijing, China
- Department of Neurosurgery (J.L., X.B., T.W., R.Y., L.L., R.X., T.L., Y.W., Y.C., P.G., J.C., B.Y., Y.M., L.J.)
- Department of Interventional Radiology (P.G., L.J.), Xuanwu Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
12
|
Chen Z, Liao C, Cao X, Poser BA, Xu Z, Lo WC, Wen M, Cho J, Tian Q, Wang Y, Feng Y, Xia L, Chen W, Liu F, Bilgic B. 3D-EPI blip-up/down acquisition (BUDA) with CAIPI and joint Hankel structured low-rank reconstruction for rapid distortion-free high-resolution T 2 * mapping. Magn Reson Med 2023; 89:1961-1974. [PMID: 36705076 PMCID: PMC10072851 DOI: 10.1002/mrm.29578] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [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: 07/25/2022] [Revised: 12/19/2022] [Accepted: 12/22/2022] [Indexed: 01/28/2023]
Abstract
PURPOSE This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitativeT 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping. METHODS 3D Blip-up and -down acquisition (3D-BUDA) sequence is designed for both single- and multi-echo 3D gradient recalled echo (GRE)-EPI imaging using multiple shots with blip-up and -down readouts to encode B0 field map information. Complementary k-space coverage is achieved using controlled aliasing in parallel imaging (CAIPI) sampling across the shots. For image reconstruction, an iterative hard-thresholding algorithm is employed to minimize the cost function that combines field map information informed parallel imaging with the structured low-rank constraint for multi-shot 3D-BUDA data. Extending 3D-BUDA to multi-echo imaging permitsT 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping. For this, we propose constructing a joint Hankel matrix along both echo and shot dimensions to improve the reconstruction. RESULTS Experimental results on in vivo multi-echo data demonstrate that, by performing joint reconstruction along with both echo and shot dimensions, reconstruction accuracy is improved compared to standard 3D-BUDA reconstruction. CAIPI sampling is further shown to enhance image quality. ForT 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping, parameter values from 3D-Joint-CAIPI-BUDA and reference multi-echo GRE are within limits of agreement as quantified by Bland-Altman analysis. CONCLUSIONS The proposed technique enables rapid 3D distortion-free high-resolution imaging andT 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping. Specifically, 3D-BUDA enables 1-mm isotropic whole-brain imaging in 22 s at 3T and 9 s on a 7T scanner. The combination of multi-echo 3D-BUDA with CAIPI acquisition and joint reconstruction enables distortion-free whole-brainT 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping in 47 s at 1.1 × 1.1 × 1.0 mm3 resolution.
Collapse
Affiliation(s)
- Zhifeng Chen
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Charlestown, MA, USA
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC, Australia
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Benedikt A. Poser
- Maastricht Brain Imaging Center, Faculty of Psychology and Neuroscience, University of Maastricht, the Netherlands
| | - Zhongbiao Xu
- Department of Radiotherapy, Cancer Center, Guangdong Provincial People’s Hospital & Guangdong Academy of Medical Science, Guangzhou, China
| | | | - Manyi Wen
- Department of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong, China
| | - Jaejin Cho
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Yaohui Wang
- Division of Superconducting Magnet Science and Technology, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China
| | - Yanqiu Feng
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
| | - Ling Xia
- Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Wufan Chen
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Feng Liu
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Charlestown, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| |
Collapse
|
13
|
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
|
14
|
Shen W, Peng Z, Wang X, Wang H, Cen J, Jiang D, Xie L, Yang X, Tian Q. A Survey on Label-Efficient Deep Image Segmentation: Bridging the Gap Between Weak Supervision and Dense Prediction. IEEE Trans Pattern Anal Mach Intell 2023; PP:1-20. [PMID: 37027561 DOI: 10.1109/tpami.2023.3246102] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The rapid development of deep learning has made a great progress in image segmentation, one of the fundamental tasks of computer vision. However, the current segmentation algorithms mostly rely on the availability of pixel-level annotations, which are often expensive, tedious, and laborious. To alleviate this burden, the past years have witnessed an increasing attention in building label-efficient, deep-learning-based image segmentation algorithms. This paper offers a comprehensive review on label-efficient image segmentation methods. To this end, we first develop a taxonomy to organize these methods according to the supervision provided by different types of weak labels (including no supervision, inexact supervision, incomplete supervision and inaccurate supervision) and supplemented by the types of segmentation problems (including semantic segmentation, instance segmentation and panoptic segmentation). Next, we summarize the existing label-efficient image segmentation methods from a unified perspective that discusses an important question: how to bridge the gap between weak supervision and dense prediction - the current methods are mostly based on heuristic priors, such as cross-pixel similarity, cross-label constraint, cross-view consistency, and cross-image relation. Finally, we share our opinions about the future research directions for label-efficient deep image segmentation.
Collapse
|
15
|
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
|
16
|
Thaler C, Tian Q, Wintermark M, Ghanouni P, Halpern CH, Henderson JM, Airan RD, Zeineh M, Goubran M, Leuze C, Fiehler J, Butts Pauly K, McNab JA. Changes in the Cerebello-Thalamo-Cortical Network After Magnetic Resonance-Guided Focused Ultrasound Thalamotomy. Brain Connect 2023; 13:28-38. [PMID: 35678063 PMCID: PMC9942176 DOI: 10.1089/brain.2021.0157] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [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] [Indexed: 11/12/2022] Open
Abstract
Objective: In recent years, transcranial magnetic resonance-guided focused ultrasound (tcMRgFUS) has been established as a potential treatment option for movement disorders, including essential tremor (ET). So far, however, little is known about the impact of tcMRgFUS on structural connectivity. The objective of this study was to detect microstructural changes in tremor- and motor-related white matter tracts in ET patients treated with tcMRgFUS thalamotomy. Methods: Eleven patients diagnosed with ET were enrolled in this tcMRgFUS thalamotomy study. For each patient, 3 Tesla magnetic resonance imaging (3T MRI) including structural and diffusion MRI were acquired and the Clinical Rating Scale for Tremor was assessed before the procedure as well as 1 year after the treatment. Diffusion MRI tractography was performed to identify the cerebello-thalamo-cortical tract (CTCT), the medial lemniscus, and the corticospinal tract in both hemispheres on pre-treatment data. Pre-treatment tractography results were co-registered to post-treatment diffusion data. Diffusion tensor imaging (DTI) metrics, including fractional anisotropy (FA), mean diffusivity (MD) and radial diffusivity (RD), were averaged across the tracts in the pre- and post-treatment data. Results: The mean value of tract-specific DTI metrics changed significantly within the thalamic lesion and in the CTCT on the treated side (p < 0.05). Changes of DTI-derived indices within the CTCT correlated well with lesion overlap (FA: r = -0.54, p = 0.04; MD: r = 0.57, p = 0.04); RD: r = 0.67, p = 0.036). Further, a trend was seen for the correlation between changes of DTI-derived indices within the CTCT and clinical improvement (FA: r = 0.58; p = 0.062; MD: r = -0.52, p = 0.64; RD: r = -0.61 p = 0.090). Conclusions: Microstructural changes were detected within the CTCT after tcMRgFUS, and these changes correlated well with lesion-tract overlap. Our results show that diffusion MRI is able to detect the microstructural effects of tcMRgFUS, thereby further elucidating the treatment mechanism, and ultimately to improve targeting prospectively. Impact statement The results of this study demonstrate microstructural changes within the cerebello-thalamo-cortical pathways 1 year after MR-guided focused ultrasound thalamotomy. Even more, microstructural changes within the cerebello-thalamo-cortical pathways correlated significantly with clinical outcome. These findings do not only highly emphasize the need of new targeting strategies for MR-guided focused ultrasound thalamotomy but also help to elucidate the treatment mechanism of it.
Collapse
Affiliation(s)
- Christian Thaler
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Qiyuan Tian
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Max Wintermark
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Pejman Ghanouni
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Casey H. Halpern
- Department of Neurosurgery, Stanford University, Stanford, California, USA
| | | | - Raag D. Airan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Michael Zeineh
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Maged Goubran
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Christoph Leuze
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Kim Butts Pauly
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Jennifer A. McNab
- Department of Radiology, Stanford University, Stanford, California, USA
| |
Collapse
|
17
|
Ke P, Xu M, Xu J, Yuan X, Ni W, Sun Y, Zhang H, Zhang Y, Tian Q, Dowling R, Jiang H, Zhao Z, Lu Z. Association of residential greenness with the risk of metabolic syndrome in Chinese older adults: a longitudinal cohort study. J Endocrinol Invest 2023; 46:327-335. [PMID: 36006585 DOI: 10.1007/s40618-022-01904-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/12/2022] [Indexed: 01/27/2023]
Abstract
AIMS We aimed to investigate the association between residential greenness and MetS in older Chinese adults. METHODS Longitudinal data on sociodemographic characteristics and lifestyle were collected from the Shenzhen Healthy Ageing Research (SHARE) cohort. Greenness exposure was assessed through satellite-derived Normalized Difference Vegetation Index (NDVI) values in the 250-m, 500-m, and 1250-m radius around the residential address for each participant. MetS was defined by standard guidelines for the Chinese population. RESULTS A total of 49,893 older Chinese adults with a mean age of 70.96 (SD = 5.26) years were included in the study. In the fully adjusted models, participants who lived in the highest quartile of NDVI250-m, NDVI500-m, and NDVI1250-m had a 15% (odds ratio, OR = 0.85, 95% confidence interval, CI: 0.80-0.90), 12% (OR = 0.88, 95% CI: 0.83-0.93), and 11% (OR = 0.89, 95% CI: 0.85-0.95) lower incidence of MetS, respectively, than those living in the lowest quartile (all p-trend < 0.01). Interactions and subgroup analyses showed that age, sex, smoking status, and drinking status were significant effect modifiers (p-interaction for all NDVI < 0.05). CONCLUSIONS Residential greenness is associated with a lower risk of MetS in Chinese older adults, especially for young older adults, females, non-smokers, and non-drinkers.
Collapse
Affiliation(s)
- P Ke
- Department of Social Medicine and Health Management, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan, 430030, Hubei, People's Republic of China
| | - M Xu
- Department of Social Medicine and Health Management, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan, 430030, Hubei, People's Republic of China
| | - J Xu
- Shenzhen Center for Chronic Disease Control, No. 2021 Buxin Road, Shenzhen, 518020, Guangdong, People's Republic of China
| | - X Yuan
- Shenzhen Center for Chronic Disease Control, No. 2021 Buxin Road, Shenzhen, 518020, Guangdong, People's Republic of China
| | - W Ni
- Shenzhen Center for Chronic Disease Control, No. 2021 Buxin Road, Shenzhen, 518020, Guangdong, People's Republic of China
| | - Y Sun
- Shenzhen Center for Chronic Disease Control, No. 2021 Buxin Road, Shenzhen, 518020, Guangdong, People's Republic of China
| | - H Zhang
- Shenzhen Center for Chronic Disease Control, No. 2021 Buxin Road, Shenzhen, 518020, Guangdong, People's Republic of China
| | - Y Zhang
- Shenzhen Center for Chronic Disease Control, No. 2021 Buxin Road, Shenzhen, 518020, Guangdong, People's Republic of China
| | - Q Tian
- School of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - R Dowling
- Centre for Alcohol Policy Research, School of Psychology and Public Health, La Trobe University, Bundoora, Melbourne, VIC, 3086, Australia
| | - H Jiang
- Centre for Alcohol Policy Research, School of Psychology and Public Health, La Trobe University, Bundoora, Melbourne, VIC, 3086, Australia.
- Centre for Health Equity, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.
| | - Z Zhao
- Shenzhen Center for Chronic Disease Control, No. 2021 Buxin Road, Shenzhen, 518020, Guangdong, People's Republic of China.
| | - Z Lu
- Department of Social Medicine and Health Management, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan, 430030, Hubei, People's Republic of China.
| |
Collapse
|
18
|
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
|
19
|
Bai X, Fu Z, Sun Z, Xu R, Guo X, Tian Q, Dmytriw AA, Zhao H, Wang W, Wang X, Patel AB, Yang B, Jiao L. Thrombectomy Using the EmboTrap Clot-Retrieving Device for the Treatment of Acute Ischemic Stroke: A Glimpse of Clinical Evidence. AJNR Am J Neuroradiol 2022; 43:1736-1742. [PMID: 36456081 DOI: 10.3174/ajnr.a7708] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 10/11/2022] [Indexed: 12/03/2022]
Abstract
BACKGROUND The EmboTrap Recanalization Device is a novel stent retriever for thrombectomy in the setting of acute ischemic stroke due to large-vessel occlusion. PURPOSE Our aim was to summarize the safety and efficacy of the EmboTrap Recanalization Device in acute ischemic stroke-large-vessel occlusion through a systematic review and meta-analysis. DATA SOURCES Medline, EMBASE, the Cochrane Library, Web of Science, and Google Scholar were searched up to April 2022. STUDY SELECTION Nine observational studies using the EmboTrap Recanalization Device were selected. DATA ANALYSIS We adapted effect size with 95% CIs for dichotomous data. P value <.05 was statistically significant. DATA SYNTHESIS The estimated rate of successful recanalization (modified TICI 2b-3) was 90% (95% CI, 86%-95%; I 2 = 82.4%); 90-day favorable outcome (mRS 0-2), 53% (95% CI, 42%-63%; I 2 = 88.6%); modified first-pass effect, 43% (95% CI, 35%-51%; I 2 = 63.7%); and first-pass effect, 36% (95% CI, 29%-46%; I 2 = 10.7%). The rate of any intracerebral hemorrhage was 19% (95% CI, 16%-22%; I 2 = 0.0%); symptomatic intracerebral hemorrhage, 5% (95% CI, 1%-8%; I 2 = 84.6%); and 90-day mortality, 14% (95% CI, 9%-19%; I 2 = 79.3%). Subgroup analysis showed higher rates of complete recanalization for EmboTrap II than for the EmboTrap System. LIMITATIONS The included studies are single-arm without direct comparison with other stent retrievers. Some of the studies recruited had a small sample size and were limited by the retrospective study design. In addition, the uncertain heterogeneity among studies was high. CONCLUSIONS The EmboTrap Recanalization Device is safe and efficient in treating acute ischemic stroke due to large-vessel occlusion.
Collapse
Affiliation(s)
- X Bai
- From the Departments of Neurosurgery (X.B., Z.F., Z.S., R.X., H.Z., B.Y., L.J.).,China International Neuroscience Institute (X.B., Z.F., Z.S., R.X., H.Z., B.Y., L.J.), Beijing, China
| | - Z Fu
- From the Departments of Neurosurgery (X.B., Z.F., Z.S., R.X., H.Z., B.Y., L.J.).,China International Neuroscience Institute (X.B., Z.F., Z.S., R.X., H.Z., B.Y., L.J.), Beijing, China
| | - Z Sun
- From the Departments of Neurosurgery (X.B., Z.F., Z.S., R.X., H.Z., B.Y., L.J.).,China International Neuroscience Institute (X.B., Z.F., Z.S., R.X., H.Z., B.Y., L.J.), Beijing, China
| | - R Xu
- From the Departments of Neurosurgery (X.B., Z.F., Z.S., R.X., H.Z., B.Y., L.J.).,China International Neuroscience Institute (X.B., Z.F., Z.S., R.X., H.Z., B.Y., L.J.), Beijing, China
| | - X Guo
- Department of Neurology (X.G.), Loma Linda University Health, Loma Linda, California
| | - Q Tian
- Beijing Key Laboratory of Clinical Epidemiology (Q.T.), School of Public Health, Capital Medical University, Beijing, China
| | - A A Dmytriw
- Neuroendovascular Program (A.A.D.), Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - H Zhao
- From the Departments of Neurosurgery (X.B., Z.F., Z.S., R.X., H.Z., B.Y., L.J.).,China International Neuroscience Institute (X.B., Z.F., Z.S., R.X., H.Z., B.Y., L.J.), Beijing, China
| | - W Wang
- Library (W.W., X.W., A.B.P.)
| | - X Wang
- Library (W.W., X.W., A.B.P.)
| | | | - B Yang
- From the Departments of Neurosurgery (X.B., Z.F., Z.S., R.X., H.Z., B.Y., L.J.).,China International Neuroscience Institute (X.B., Z.F., Z.S., R.X., H.Z., B.Y., L.J.), Beijing, China
| | - L Jiao
- From the Departments of Neurosurgery (X.B., Z.F., Z.S., R.X., H.Z., B.Y., L.J.) .,Interventional Neuroradiology (L.J.), Xuanwu Hospital, Capital Medical University, Xicheng District, Beijing, China.,China International Neuroscience Institute (X.B., Z.F., Z.S., R.X., H.Z., B.Y., L.J.), Beijing, China
| |
Collapse
|
20
|
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
|
21
|
Ruan J, Tian Q, Wang Y, Chang K, Yi X. 8659 Interleukin-33 Promotes Endometriosis Fibrosis by Inducing Fibroblast to Myofibroblast Transformation. J Minim Invasive Gynecol 2022. [DOI: 10.1016/j.jmig.2022.09.455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
22
|
Tian Q, Zheng Y, Chang K, Yi X. 8795 Impact of Surgical Procedures on Intestinal Function and Quality of Life in Patients with Deep Endometriosis: A Prospective Study. J Minim Invasive Gynecol 2022. [DOI: 10.1016/j.jmig.2022.09.486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
23
|
Viessmann O, Tian Q, Bernier M, Polimeni JR. Static and dynamic BOLD fMRI components along white matter fibre tracts and their dependence on the orientation of the local diffusion tensor axis relative to the B 0-field. J Cereb Blood Flow Metab 2022; 42:1905-1919. [PMID: 35650710 PMCID: PMC9536127 DOI: 10.1177/0271678x221106277] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Recent studies have reported functional MRI (fMRI) activation within cerebral white matter (WM) using blood-oxygenation-level-dependent (BOLD) contrast. Many blood vessels in WM run parallel to the fibre bundles, and other studies observed dependence of susceptibility contrast-based measures of blood volume on the local orientation of the fibre bundles relative to the magnetic field or B0 axis. Motivated by this, we characterized the dependence of gradient-echo BOLD fMRI on fibre orientation (estimated by the local diffusion tensor) relative to the B0 axis to test whether the alignment between bundles and vessels imparts an orientation dependence on resting-state BOLD fluctuations in the WM. We found that the baseline signal level of the T2*-weighted data is 11% higher in voxels containing fibres parallel to B0 than those containing perpendicular fibres, consistent with a static influence of either fibre or vessel orientation on local T2* values. We also found that BOLD fluctuations in most bundles exhibit orientation effects expected from oxygenation changes, with larger amplitudes from voxels containing perpendicular fibres. Different magnitudes of this orientation effect were observed across the major WM bundles, with inferior fasciculus, corpus callosum and optic radiation exhibiting 14-19% higher fluctuations in voxels containing perpendicular compared to parallel fibres.
Collapse
Affiliation(s)
- Olivia Viessmann
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Michaël Bernier
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard-Massachusetts Institute of Technology, Division of Health Sciences and Technology, Cambridge, MA, USA
| |
Collapse
|
24
|
Cho J, Liao C, Tian Q, Zhang Z, Xu J, Lo WC, Poser BA, Stenger VA, Stockmann J, Setsompop K, Bilgic B. Highly accelerated EPI with wave encoding and multi-shot simultaneous multislice imaging. Magn Reson Med 2022; 88:1180-1197. [PMID: 35678236 DOI: 10.1002/mrm.29291] [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: 07/12/2021] [Revised: 04/14/2022] [Accepted: 04/15/2022] [Indexed: 11/09/2022]
Abstract
PURPOSE To introduce wave-encoded acquisition and reconstruction techniques for highly accelerated EPI with reduced g-factor penalty and image artifacts. THEORY AND METHODS Wave-EPI involves application of sinusoidal gradients during the EPI readout, which spreads the aliasing in all spatial directions, thereby taking better advantage of 3D coil sensitivity profiles. The amount of voxel spreading that can be achieved by the wave gradients during the short EPI readout period is constrained by the slew rate of the gradient coils and peripheral nerve stimulation monitor. We propose to use a "half-cycle" sinusoidal gradient to increase the amount of voxel spreading that can be achieved while respecting the slew and stimulation constraints. Extending wave-EPI to multi-shot acquisition minimizes geometric distortion and voxel blurring at high in-plane resolutions, while structured low-rank regularization mitigates shot-to-shot phase variations. To address gradient imperfections, we propose to use different point spread functions for the k-space lines with positive and negative polarities, which are calibrated with a FLEET-based reference scan. RESULTS Wave-EPI enabled whole-brain single-shot gradient-echo (GE) and multi-shot spin-echo (SE) EPI acquisitions at high acceleration factors at 3T and was combined with g-Slider encoding to boost the SNR level in 1 mm isotropic diffusion imaging. Relative to blipped-CAIPI, wave-EPI reduced average and maximum g-factors by up to 1.21- and 1.37-fold at Rin × Rsms = 3 × 3, respectively. CONCLUSION Wave-EPI allows highly accelerated single- and multi-shot EPI with reduced g-factor and artifacts and may facilitate clinical and neuroscientific applications of EPI by improving the spatial and temporal resolution in functional and diffusion imaging.
Collapse
Affiliation(s)
- Jaejin Cho
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Congyu Liao
- Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Zijing Zhang
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China
| | - Jinmin Xu
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China
| | - Wei-Ching Lo
- Siemens Medical Solutions, Boston, Massachusetts, USA
| | - Benedikt A Poser
- Maastricht Brain Imaging Center, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - V Andrew Stenger
- MR Research Program, Department of Medicine, John A. Burns School of Medicine, University of Hawai'i, Honolulu, Hawaii, USA
| | - Jason Stockmann
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Kawin Setsompop
- Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| |
Collapse
|
25
|
He K, Chen X, Shi Z, Shi S, Tian Q, Hu X, Song R, Bai K, Shi W, Wang J, Li H, Ding J, Geng S, Sheng X. Relationship of resting heart rate and blood pressure with all-cause and cardiovascular disease mortality. Public Health 2022; 208:80-88. [PMID: 35728416 DOI: 10.1016/j.puhe.2022.03.020] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/21/2022] [Accepted: 03/30/2022] [Indexed: 10/18/2022]
Abstract
OBJECTIVES This study aimed to investigate associations of resting heart rate (RHR) and blood pressure (BP) with all-cause and cardiovascular disease (CVD) mortality. STUDY DESIGN A retrospective cohort study. METHODS A total of 67,028 Chinese participants aged ≥60 years were included in the analysis. RHR, systolic blood pressure (SBP), and diastolic blood pressure (DBP) were evaluated according to quartiles ([41-69, 70-74, 75-79, 80-127 beats/min], [80-119, 120-129, 130-139, 140-238 mm Hg], and [40-70, 71-79, 80-84, 85-133 mm Hg]). Cox proportional hazard models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of all-cause and CVD mortality with RHR, SBP, and DBP. Restricted cubic splines were used to evaluate the dose-response association. RESULTS During the 361,975 person-year follow-up, 9326 deaths were recorded, of which 5039 deaths were due to CVD. The risk of all-cause mortality was increased by 25% with the quartiles four vs quartile one of RHR (HR [95% CI]:1.25 [1.17-1.33]), and CVD mortality was increased by 32% (HR [95% CI]: 1.32 [1.22-1.44]). Similar results were observed when comparing the quartiles four vs quartile one of SBP with the risk of all-cause and CVD mortality (HRs [95% CIs]: 1.14 [1.07, 1.22] and 1.23 [1.12. 1.34]) and DBP with the risk of all-cause and CVD mortality (HRs [95% CIs]: 1.17 [1.11. 1.24] and 1.36 [1.26. 1.47]). We found linear associations of RHR, SBP, and DBP with all-cause and CVD mortality (Pnon-linearity >0.05), except for the approximately J-shaped association between DBP and all-cause mortality (Pnon-linearity = 0.008). There was a significant interaction of RHR and SBP with all-cause and CVD mortality (Pinteraction <0.05). CONCLUSIONS RHR and BP increased the risk of all-cause and CVD mortality, especially fast RHR combined with high SBP.
Collapse
Affiliation(s)
- K He
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - X Chen
- Department of Social Medicine and Health Management, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Z Shi
- Department of Pharmacy, Zhengzhou People's Hospital, Zhengzhou, Henan, People's Republic of China
| | - S Shi
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
| | - Q Tian
- Department of Social Medicine and Health Management, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - X Hu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - R Song
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - K Bai
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - W Shi
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - J Wang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - H Li
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - J Ding
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - S Geng
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - X Sheng
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| |
Collapse
|
26
|
Shi J, Xia C, Tian Q, Zeng X, Wu Z, Guo Y, Pan D. Untargeted metabolomics based on LC–MS to elucidate the mechanism underlying nitrite degradation by Limosilactobacillus fermentum RC4. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
27
|
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
|
28
|
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
|
29
|
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
|
30
|
Zhai L, Jiang W, Zang Y, Gao Y, Jiang D, Tian Q, Zhao C. Impact of Thyroid Tissue Status on the Cut-Off Value of Lymph Node Fine-Needle Aspiration Thyroglobulin Measurements in Papillary Thyroid Cancer. Br J Biomed Sci 2022; 79:10210. [PMID: 35996517 PMCID: PMC8915611 DOI: 10.3389/bjbs.2021.10210] [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] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 12/02/2021] [Indexed: 11/13/2022]
Abstract
Objective: To study the optimal cut-off value of thyroglobulin measurement in a fine-needle aspiration (FNA-Tg) in diagnosing malignant lymph nodes and benign lymph nodes (LNs) according to the thyroid tissue status. Methods: A total of 517 LNs were aspirated: 401 preoperative LNs, 42 LNs after subtotal thyroidectomy and 74 suspected LNs after total thyroidectomy. The cut-off value of FNA-Tg was obtained from receiver operating characteristic (ROC) analysis. The cut-off value with the best diagnostic performance was then obtained by comparing different cut-off values from other studies. Results: LN FNA-Tg levels differed between preoperative and total thyroid disease (p < 0.001) and subtotal thyroidectomy and total thyroidectomy (p = 0.03), but not between preoperative and subtotal thyroidectomy (p = 1.00). Accordingly, those 443 LNs with preoperative and subtotal thyroidectomy were compared to those 74 without thyroid tissue. The optimal cut-off value in thyroid tissue group was 19.4 ng/ml and the area under the ROC curve (AUC) was 0.95 (95% CI 0.92–0.97). The optimal cut-off value in thyroid tissue absence group was 1.2 ng/ml and the AUC was 0.93 (0.85–0.98). After the analysis and comparison of multiple cut-off values, the optimal diagnostic performance was still found to be 19.4 ng/ml and 1.2 ng/ml. Conclusion: The influential factors of FNA-Tg are still controversial, and the optimal cut-off value of FNA-Tg can be determined based on the presence or absence of thyroid tissue. FNA-Tg can be used as an important auxiliary method for diagnosing cervical metastatic LNs of thyroid cancer.
Collapse
Affiliation(s)
- L. Zhai
- Department of Abdominal Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, China
- Department of Ultrasound, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
| | - W. Jiang
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Y. Zang
- Department of Abdominal Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Y. Gao
- Department of Abdominal Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - D. Jiang
- Department of Abdominal Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Q. Tian
- Department of Laboratory Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - C. Zhao
- Department of Abdominal Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, China
- *Correspondence: C. Zhao,
| |
Collapse
|
31
|
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
|
32
|
Tian Q, Gao H, Zhou Y, Yang J. Overall survival and progression-free survival with cyclin-dependent kinase 4/6 inhibitors plus endocrine therapy in breast cancer: an updated meta-analysis of randomized controlled trials. Eur Rev Med Pharmacol Sci 2021; 25:7252-7267. [PMID: 34919224 DOI: 10.26355/eurrev_202112_27418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) in combination with endocrine therapy (ET) have been recommended as standard therapeutic strategies for hormone receptor-positive (HR+), human epidermal growth factor receptor type 2-negative (Her2-) advanced breast cancer (ABC). While the benefits to progression-free survival (PFS) rates have been confirmed, whether the combination of CDK4/6i and ET leads to overall survival (OS) rate improvements remains controversial. This study aimed to assess the long-term efficacy and safety of CDK4/6i in HR+, Her2- ABC patients and identify a population suitable for treatment with CDK4/6i by subgroup analysis. MATERIALS AND METHODS Electronic literature databases (MEDLINE, EMBASE and the Cochrane Library) were searched for relevant randomized controlled trials (rcts) published from Jan 2014 to Jan 2020. In addition, abstracts and presentations from all major conference proceedings were reviewed. All rcts that compared the efficacy and safety of CDK4/6i plus ET with ET alone in HR+, Her2- ABC patients were selected. The pooled analyses of hazard ratios (hrs) for PFS and OS, and risk ratios (rrs) for the objective response rate (ORR) and adverse events (aes) were obtained with the random-effects model. RESULTS A total of 6 rcts and 3421 HR+, Her2- ABC patients were enrolled for OS outcome analysis, while all 8 trials and 4580 patients were included for PFS outcome analysis. The pooled hrs for the OS and PFS were 0.76 (95% CI: 0.67-0.84) and 0.55 (95% CI: 0.50-0.59), respectively, and were consistent in the subgroup analysis. Moreover, CDK4/6i meaningfully improved the ORR in both the intention-to-treat population (RR=1.47; 95% CI: 1.29-1.67) and patients with measurable disease (RR=1.47; 95% CI: 1.30-1.67); however, CDK4/6i increased the incidence of grade 3/4 aes (RR=2.69; 95% CI: 2.43-2.97). CONCLUSIONS The combination of CDK4/6i and ET was superior to ET alone in terms of OS and PFS regardless of the drugs administered, the treatment line, age distribution, race, PR status, menopausal status, metastasis site and endocrine resistance status.
Collapse
Affiliation(s)
- Q Tian
- Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
| | | | | | | |
Collapse
|
33
|
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
|
34
|
Xu R, Tian Q, Wan H, Wen JW, Zhang Q, Zhang Y. Spatial and Temporal Characteristics of PM2.5 Sources and Pollution Events in a Low Industrialized City. NEPT 2021. [DOI: 10.46488/nept.2021.v20i03.016] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
In recent years, cities in southern China have experienced severe air pollution, despite having few sources of pollutants. To study the pollution characteristics of PM2.5 in these “low industrialized” cities, a numerical method based on the HYSPLIT4 Model and Kriging Spatial Interpolation Technology was established. Simulation results showed that the PM2.5 pollution in Guilin was affected by both internal and external sources. The backward air mass trajectory from July 2017 to June 2018 was simulated using the HYSPLIT model. The cluster analysis results indicated that the direction of trajectory ? accounted for 63.09% of the air pollution in the city. The average concentration of PM2.5 pollution was 45.94 ?g.m-3. The pollutant originated from the “Xiang-Gui Corridor.” The location of the sources was collocated with high industry regions. The spatial characteristics of the four pollution processes in the winter of 2017 were analyzed using a spatial interpolation method. The results showed that the transport of air masses in the direction of trajectory ? was obstructed by a mountain system in the northeast. Therefore, two air pollution accumulation centers and a topographic weakening zone dominated by internal and external sources were formed. It can be inferred that the air pollution in Guilin is affected by both internal and external factors. These results provide important theoretical and technical support for regional air pollution control and environmental protection.
Collapse
|
35
|
Zeng P, Tang X, Wu T, Tian Q, Li M, Ding J. [Identification of potential regulatory genes for embryonic stem cell self-renewal and pluripotency by random forest]. Nan Fang Yi Ke Da Xue Xue Bao 2021; 41:1234-1238. [PMID: 34549716 DOI: 10.12122/j.issn.1673-4254.2021.08.16] [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] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
OBJECTIVE To identify novel genes associated with self-renewal and pluripotency of mouse embryonic stem cells(mESCs)by integrating multiomics data based on machine learning methods. METHODS We integrated multiomics information of mESCs involving transcriptome, histone modifications, chromatin accessibility, transcription factor binding and architectural protein binding, and compared the signal differences between known stem cell self-renewal and pluripotency genes and other genes.By integrating these multiomics data, we established prediction models based on several machine learning classifiers including random forests and performed 5-fold cross validations.The model was trained using the training dataset containing two thirds of the input samples, and the remaining one third of the input samples were used as the test dataset to assess the performance of the model in independent tests.Finally, the results predicted by the model were validated through gene function annotation and cell function experiments including cell viability assay, colony formation assay and cell cycle analysis. RESULTS Compared with the random genes, the genes known to be associated with self-renewal and pluripotency of mESCs in the multiomics data showed significantly different features.Random forest outperformed the other machine learning algorithms tested on these multiomics data, with an area under the curve (AUC) of 0.883±0.018 for cross validation and an AUC of 0.880±0.028 for independent test.Based on this model, we identified 893 potential regulatory genes associated wwith self-renewal and pluripotency of mESCs, which were similar to the known genes in functional annotation.Known-down of the predicted novel regulator gene Cct6a resulted in significant decreases in the cell viability of mESCs (P < 0.0001) and the number of cell clones (P < 0.01), significantly increased the number of cells in G1 phase (P < 0.01) and decreasedthe number of S phase cells (P < 0.05).Knockdown of Cct6a also led to failure of positive alkaline phosphatase staining of the mESCs. CONCLUSION Machine learning model based on multiomics data can be used to predict potential self-renewal and pluripotency regulators with high performance.By using this model, we predicted potential self-renewal and pluripotency regulatory genes including Cct6a and applied experimental validation.This model provides new insights into the regulatory mechanism of mESCs and contribute to stem cell research.
Collapse
Affiliation(s)
- P Zeng
- School of Basic Medical Science, Southern Medical University, Guangzhou 510515, China
| | - X Tang
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
| | - T Wu
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
| | - Q Tian
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
| | - M Li
- School of Basic Medical Science, Southern Medical University, Guangzhou 510515, China
| | - J Ding
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
| |
Collapse
|
36
|
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
|
37
|
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
|
38
|
Scholz A, Etzel R, May MW, Mahmutovic M, Tian Q, Ramos-Llordén G, Maffei C, Bilgiç B, Witzel T, Stockmann JP, Mekkaoui C, Wald LL, Huang SY, Yendiki A, Keil B. A 48-channel receive array coil for mesoscopic diffusion-weighted MRI of ex vivo human brain on the 3 T connectome scanner. Neuroimage 2021; 238:118256. [PMID: 34118399 PMCID: PMC8439104 DOI: 10.1016/j.neuroimage.2021.118256] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [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: 04/27/2021] [Revised: 06/04/2021] [Accepted: 06/07/2021] [Indexed: 12/14/2022] Open
Abstract
In vivo diffusion-weighted magnetic resonance imaging is limited in signal-to-noise-ratio (SNR) and acquisition time, which constrains spatial resolution to the macroscale regime. Ex vivo imaging, which allows for arbitrarily long scan times, is critical for exploring human brain structure in the mesoscale regime without loss of SNR. Standard head array coils designed for patients are sub-optimal for imaging ex vivo whole brain specimens. The goal of this work was to design and construct a 48-channel ex vivo whole brain array coil for high-resolution and high b-value diffusion-weighted imaging on a 3T Connectome scanner. The coil was validated with bench measurements and characterized by imaging metrics on an agar brain phantom and an ex vivo human brain sample. The two-segment coil former was constructed for a close fit to a whole human brain, with small receive elements distributed over the entire brain. Imaging tests including SNR and G-factor maps were compared to a 64-channel head coil designed for in vivo use. There was a 2.9-fold increase in SNR in the peripheral cortex and a 1.3-fold gain in the center when compared to the 64-channel head coil. The 48-channel ex vivo whole brain coil also decreases noise amplification in highly parallel imaging, allowing acceleration factors of approximately one unit higher for a given noise amplification level. The acquired diffusion-weighted images in a whole ex vivo brain specimen demonstrate the applicability and advantage of the developed coil for high-resolution and high b-value diffusion-weighted ex vivo brain MRI studies.
Collapse
Affiliation(s)
- Alina Scholz
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), 14 Wiesenstrasse, Giessen 35390, Germany.
| | - Robin Etzel
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), 14 Wiesenstrasse, Giessen 35390, Germany
| | - Markus W May
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), 14 Wiesenstrasse, Giessen 35390, Germany
| | - Mirsad Mahmutovic
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), 14 Wiesenstrasse, Giessen 35390, Germany
| | - Qiyuan Tian
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Gabriel Ramos-Llordén
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Chiara Maffei
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Berkin Bilgiç
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA
| | - Thomas Witzel
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Jason P Stockmann
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Choukri Mekkaoui
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Lawrence L Wald
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA
| | - Susie Yi Huang
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA
| | - Anastasia Yendiki
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Boris Keil
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), 14 Wiesenstrasse, Giessen 35390, Germany; Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| |
Collapse
|
39
|
Ning HT, Du Y, Zhao LJ, Tian Q, Feng H, Deng HW. Racial and gender differences in the relationship between sarcopenia and bone mineral density among older adults. Osteoporos Int 2021; 32:841-851. [PMID: 33231702 PMCID: PMC8044008 DOI: 10.1007/s00198-020-05744-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [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] [Received: 08/04/2020] [Accepted: 11/13/2020] [Indexed: 12/19/2022]
Abstract
Both sarcopenia and low bone mineral density (BMD) have become public health concerns. We found that presarcopenic and/or sarcopenic individuals were more likely to have lower BMD. And this relationship has race and sex-specific discrepancy. PURPOSE The purpose of the study was to investigate the racial and gender differences in the relationship between sarcopenia and BMD among older adults. METHODS Totally, 5476 subjects (mean age = 65.7 ± 6.4) of non-Hispanic White (n = 3297), non-Hispanic Black (n = 1265), and non-Hispanic Asian (n = 914) were analyzed. Sarcopenia was defined according to the revised European consensus on definition and diagnosis of sarcopenia (EWGSOP2). General linear model and multivariable linear regression model were used to examine the relationship between sarcopenia and regional/whole body BMD stratified by race and sex. Adjustments were conducted for physiological, behavioral, and disease factors. RESULTS Comparing with normal older participants, presarcopenic and sarcopenic elderly were more likely to have lower BMD. Although the difference was not statistically significant in a few sub-groups, among the three racial groups, the strongest association between sarcopenia and BMD was found in non-Hispanic Black people, followed by non-Hispanic White people and non-Hispanic Asian people. In addition, significant differences of BMD across sarcopenia stages were found in more sub-groups in women than in men after adjusting for covariates. CONCLUSIONS In this older cohort, sarcopenia is significantly related to low regional/whole-body BMD, and these associations vary by race and sex. Consideration in race and sex is warranted when developing strategies to maintain or minimize BMD loss.
Collapse
Affiliation(s)
- H-T Ning
- Xiangya School of Nursing, Central South University, Changsha, Hunan, China
| | - Y Du
- School of Nursing, University of Texas Health Science Center at San Antonio, TX, San Antonio, USA
| | - L-J Zhao
- Center for Bioinformatics and Genomics, Department of Biostatistics, School of Public Health and Tropical Medicine, Tulane University, LA, New Orleans, USA
| | - Q Tian
- Center for Bioinformatics and Genomics, Department of Biostatistics, School of Public Health and Tropical Medicine, Tulane University, LA, New Orleans, USA
| | - H Feng
- Xiangya School of Nursing, Xiangya-Oceanwide Health Management Research Institute, Central South University, Changsha, Hunan, China
| | - H-W Deng
- School of Medicine, Tulane University, New Orleans, LA, USA.
| |
Collapse
|
40
|
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
|
41
|
Xu Y, Wu T, Wang P, Liang ZX, Shi SS, Xu SF, Liu XJ, Tian Q. Perfluorocarbon Protects against Lipopolysaccharide-Induced Apoptosis of Endothelial Cells in Pulmonary Microvessels. Bull Exp Biol Med 2021; 170:410-414. [PMID: 33725245 DOI: 10.1007/s10517-021-05077-8] [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: 05/30/2020] [Indexed: 10/21/2022]
Abstract
This study was aimed to explore the effect and mechanisms of action of perfluorocarbon on LPS-induced apoptosis of pulmonary microvascular endothelial cells (PMVEC) isolated from Sprague-Dawley rats. Apoptosis rates were assessed by flow cytometry. Ultrastructural characteristics of PMVEC were evaluated by transmission electron microscopy. The protein expression of cleaved caspase-3 was measured using Western blotting. LPS significantly increased the level of apoptosis, induced the appearance of ultrastructural changes typical of apoptosis, up-regulated the expression of active caspase-3 protein. These effects of LPS were attenuated by co-administration of perfluorocarbon. These results suggest that perfluorocarbon can attenuate LPS-induced apoptosis of PMVEC by inhibiting TLR-4 signaling and caspase-3 activation.
Collapse
Affiliation(s)
- Y Xu
- Department of Respiratory Diseases, the First Medical Center, Chinese PLA General Hospital, Beijing, P.R. China
| | - T Wu
- Department of Respiratory Diseases, the First Hospital of Qinhuangdao, Qinhuangdao, P.R. China
| | - P Wang
- Department of Respiratory Diseases, the First Medical Center, Chinese PLA General Hospital, Beijing, P.R. China
| | - Z X Liang
- Department of Respiratory Diseases, the First Medical Center, Chinese PLA General Hospital, Beijing, P.R. China
| | - S S Shi
- Department of Respiratory Diseases, the First Hospital of Qinhuangdao, Qinhuangdao, P.R. China
| | - S F Xu
- Department of Respiratory Diseases, the First Hospital of Qinhuangdao, Qinhuangdao, P.R. China.
| | - X J Liu
- Department of Respiratory Diseases, the First Hospital of Qinhuangdao, Qinhuangdao, P.R. China
| | - Q Tian
- Department of Respiratory Diseases, the First Hospital of Qinhuangdao, Qinhuangdao, P.R. China
| |
Collapse
|
42
|
Liao C, Bilgic B, Tian Q, Stockmann JP, Cao X, Fan Q, Iyer SS, Wang F, Ngamsombat C, Lo WC, Manhard MK, Huang SY, Wald LL, Setsompop K. Distortion-free, high-isotropic-resolution diffusion MRI with gSlider BUDA-EPI and multicoil dynamic B 0 shimming. Magn Reson Med 2021; 86:791-803. [PMID: 33748985 DOI: 10.1002/mrm.28748] [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: 08/07/2020] [Revised: 01/10/2021] [Accepted: 02/04/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE We combine SNR-efficient acquisition and model-based reconstruction strategies with newly available hardware instrumentation to achieve distortion-free in vivo diffusion MRI of the brain at submillimeter-isotropic resolution with high fidelity and sensitivity on a clinical 3T scanner. METHODS We propose blip-up/down acquisition (BUDA) for multishot EPI using interleaved blip-up/blip-down phase encoding and incorporate B0 forward-modeling into structured low-rank reconstruction to enable distortion-free and navigator-free diffusion MRI. We further combine BUDA-EPI with an SNR-efficient simultaneous multislab acquisition (generalized slice-dithered enhanced resolution ["gSlider"]), to achieve high-isotropic-resolution diffusion MRI. To validate gSlider BUDA-EPI, whole-brain diffusion data at 860-μm and 780-μm data sets were acquired. Finally, to improve the conditioning and minimize noise penalty in BUDA reconstruction at very high resolutions where B0 inhomogeneity can have a detrimental effect, the level of B0 inhomogeneity was reduced by incorporating slab-by-slab dynamic shimming with a 32-channel AC/DC coil into the acquisition. Whole-brain 600-μm diffusion data were then acquired with this combined approach of gSlider BUDA-EPI with dynamic shimming. RESULTS The results of 860-μm and 780-μm datasets show high geometry fidelity with gSlider BUDA-EPI. With dynamic shimming, the BUDA reconstruction's noise penalty was further alleviated. This enables whole-brain 600-μm isotropic resolution diffusion imaging with high image quality. CONCLUSIONS The gSlider BUDA-EPI method enables high-quality, distortion-free diffusion imaging across the whole brain at submillimeter resolution, where the use of multicoil dynamic B0 shimming further improves reconstruction performance, which can be particularly useful at very high resolutions.
Collapse
Affiliation(s)
- Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Jason P Stockmann
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Xiaozhi Cao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Siddharth Srinivasan Iyer
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Chanon Ngamsombat
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA.,Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | | | - Mary Kate Manhard
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Charlestown, MA, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| |
Collapse
|
43
|
Tian Q, Zaretskaya N, Fan Q, Ngamsombat C, Bilgic B, Polimeni JR, Huang SY. Improved cortical surface reconstruction using sub-millimeter resolution MPRAGE by image denoising. Neuroimage 2021; 233:117946. [PMID: 33711484 PMCID: PMC8421085 DOI: 10.1016/j.neuroimage.2021.117946] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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: 09/17/2020] [Revised: 02/28/2021] [Accepted: 03/03/2021] [Indexed: 11/24/2022] Open
Abstract
Automatic cerebral cortical surface reconstruction is a useful tool for cortical anatomy quantification, analysis and visualization. Recently, the Human Connectome Project and several studies have shown the advantages of using T1-weighted magnetic resonance (MR) images with sub-millimeter isotropic spatial resolution instead of the standard 1-mm isotropic resolution for improved accuracy of cortical surface positioning and thickness estimation. Nonetheless, sub-millimeter resolution images are noisy by nature and require averaging multiple repetitions to increase the signal-to-noise ratio for precisely delineating the cortical boundary. The prolonged acquisition time and potential motion artifacts pose significant barriers to the wide adoption of cortical surface reconstruction at sub-millimeter resolution for a broad range of neuroscientific and clinical applications. We address this challenge by evaluating the cortical surface reconstruction resulting from denoised single-repetition sub-millimeter T1-weighted images. We systematically characterized the effects of image denoising on empirical data acquired at 0.6 mm isotropic resolution using three classical denoising methods, including denoising convolutional neural network (DnCNN), block-matching and 4-dimensional filtering (BM4D) and adaptive optimized non-local means (AONLM). The denoised single-repetition images were found to be highly similar to 6-repetition averaged images, with a low whole-brain averaged mean absolute difference of ~0.016, high whole-brain averaged peak signal-to-noise ratio of ~33.5 dB and structural similarity index of ~0.92, and minimal gray matter–white matter contrast loss (2% to 9%). The whole-brain mean absolute discrepancies in gray matter–white matter surface placement, gray matter–cerebrospinal fluid surface placement and cortical thickness estimation were lower than 165 μm, 155 μm and 145 μm—sufficiently accurate for most applications. These discrepancies were approximately one third to half of those from 1-mm isotropic resolution data. The denoising performance was equivalent to averaging ~2.5 repetitions of the data in terms of image similarity, and 1.6–2.2 repetitions in terms of the cortical surface placement accuracy. The scan-rescan variability of the cortical surface positioning and thickness estimation was lower than 170 μm. Our unique dataset and systematic characterization support the use of denoising methods for improved cortical surface reconstruction at sub-millimeter resolution.
Collapse
Affiliation(s)
- Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States.
| | - Natalia Zaretskaya
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States; Institute of Psychology, University of Graz, Graz, Austria; BioTechMed-Graz, Austria
| | - 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
| | - Chanon Ngamsombat
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Thailand
| | - 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; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - 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; 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, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| |
Collapse
|
44
|
Leuze C, Goubran M, Barakovic M, Aswendt M, Tian Q, Hsueh B, Crow A, Weber EMM, Steinberg GK, Zeineh M, Plowey ED, Daducci A, Innocenti G, Thiran JP, Deisseroth K, McNab JA. Comparison of diffusion MRI and CLARITY fiber orientation estimates in both gray and white matter regions of human and primate brain. Neuroimage 2021; 228:117692. [PMID: 33385546 PMCID: PMC7953593 DOI: 10.1016/j.neuroimage.2020.117692] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 12/16/2020] [Accepted: 12/18/2020] [Indexed: 11/30/2022] Open
Abstract
Diffusion MRI (dMRI) represents one of the few methods for mapping brain fiber orientations non-invasively. Unfortunately, dMRI fiber mapping is an indirect method that relies on inference from measured diffusion patterns. Comparing dMRI results with other modalities is a way to improve the interpretation of dMRI data and help advance dMRI technologies. Here, we present methods for comparing dMRI fiber orientation estimates with optical imaging of fluorescently labeled neurofilaments and vasculature in 3D human and primate brain tissue cuboids cleared using CLARITY. The recent advancements in tissue clearing provide a new opportunity to histologically map fibers projecting in 3D, which represents a captivating complement to dMRI measurements. In this work, we demonstrate the capability to directly compare dMRI and CLARITY in the same human brain tissue and assess multiple approaches for extracting fiber orientation estimates from CLARITY data. We estimate the three-dimensional neuronal fiber and vasculature orientations from neurofilament and vasculature stained CLARITY images by calculating the tertiary eigenvector of structure tensors. We then extend CLARITY orientation estimates to an orientation distribution function (ODF) formalism by summing multiple sub-voxel structure tensor orientation estimates. In a sample containing part of the human thalamus, there is a mean angular difference of 19o±15o between the primary eigenvectors of the dMRI tensors and the tertiary eigenvectors from the CLARITY neurofilament stain. We also demonstrate evidence that vascular compartments do not affect the dMRI orientation estimates by showing an apparent lack of correspondence (mean angular difference = 49o±23o) between the orientation of the dMRI tensors and the structure tensors in the vasculature stained CLARITY images. In a macaque brain dataset, we examine how the CLARITY feature extraction depends on the chosen feature extraction parameters. By varying the volume of tissue over which the structure tensor estimates are derived, we show that orientation estimates are noisier with more spurious ODF peaks for sub-voxels below 30 µm3 and that, for our data, the optimal gray matter sub-voxel size is between 62.5 µm3 and 125 µm3. The example experiments presented here represent an important advancement towards robust multi-modal MRI-CLARITY comparisons.
Collapse
Affiliation(s)
- C Leuze
- Department of Radiology, Stanford University, Stanford, CA, USA.
| | - M Goubran
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - M Barakovic
- Department of Radiology, Stanford University, Stanford, CA, USA; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - M Aswendt
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Q Tian
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - B Hsueh
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - A Crow
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - E M M Weber
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - G K Steinberg
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - M Zeineh
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - E D Plowey
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - A Daducci
- Department of Computer Science, University of Verona, Verona, Italy
| | - G Innocenti
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden; Brain and Mind Institute, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - J-P Thiran
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - K Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA, USA; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - J A McNab
- Department of Radiology, Stanford University, Stanford, CA, USA
| |
Collapse
|
45
|
Hu Y, Xu Y, Tian Q, Chen F, Shi X, Moran CJ, Daniel BL, Hargreaves BA. RUN-UP: Accelerated multishot diffusion-weighted MRI reconstruction using an unrolled network with U-Net as priors. Magn Reson Med 2021; 85:709-720. [PMID: 32783339 PMCID: PMC8095163 DOI: 10.1002/mrm.28446] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [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: 03/27/2020] [Revised: 06/17/2020] [Accepted: 07/06/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE To accelerate and improve multishot diffusion-weighted MRI reconstruction using deep learning. METHODS An unrolled pipeline containing recurrences of model-based gradient updates and neural networks was introduced for accelerating multishot DWI reconstruction with shot-to-shot phase correction. The network was trained to predict results of jointly reconstructed multidirection data using single-direction data as input. In vivo brain and breast experiments were performed for evaluation. RESULTS The proposed method achieves a reconstruction time of 0.1 second per image, over 100-fold faster than a shot locally low-rank reconstruction. The resultant image quality is comparable to the target from the joint reconstruction with a peak signal-to-noise ratio of 35.3 dB, a normalized root-mean-square error of 0.0177, and a structural similarity index of 0.944. The proposed method also improves upon the locally low-rank reconstruction (2.9 dB higher peak signal-to-noise ratio, 29% lower normalized root-mean-square error, and 0.037 higher structural similarity index). With training data from the brain, this method also generalizes well to breast diffusion-weighted imaging, and fine-tuning further reduces aliasing artifacts. CONCLUSION A proposed data-driven approach enables almost real-time reconstruction with improved image quality, which improves the feasibility of multishot DWI in a wide range of clinical and neuroscientific studies.
Collapse
Affiliation(s)
- Yuxin Hu
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Yunyingying Xu
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Qiyuan Tian
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Feiyu Chen
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Xinwei Shi
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | | | - Bruce L. Daniel
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Brian A. Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| |
Collapse
|
46
|
Tian Q, Bilgic B, Fan Q, Ngamsombat C, Zaretskaya N, Fultz NE, Ohringer NA, Chaudhari AS, Hu Y, Witzel T, Setsompop K, Polimeni JR, Huang SY. Improving in vivo human cerebral cortical surface reconstruction using data-driven super-resolution. Cereb Cortex 2021; 31:463-482. [PMID: 32887984 PMCID: PMC7727379 DOI: 10.1093/cercor/bhaa237] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [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: 08/02/2019] [Revised: 07/30/2020] [Accepted: 07/30/2020] [Indexed: 11/14/2022] Open
Abstract
Accurate and automated reconstruction of the in vivo human cerebral cortical surface from anatomical magnetic resonance (MR) images facilitates the quantitative analysis of cortical structure. Anatomical MR images with sub-millimeter isotropic spatial resolution improve the accuracy of cortical surface and thickness estimation compared to the standard 1-millimeter isotropic resolution. Nonetheless, sub-millimeter resolution acquisitions require averaging multiple repetitions to achieve sufficient signal-to-noise ratio and are therefore long and potentially vulnerable to subject motion. We address this challenge by synthesizing sub-millimeter resolution images from standard 1-millimeter isotropic resolution images using a data-driven supervised machine learning-based super-resolution approach achieved via a deep convolutional neural network. We systematically characterize our approach using a large-scale simulated dataset and demonstrate its efficacy in empirical data. The super-resolution data provide improved cortical surfaces similar to those obtained from native sub-millimeter resolution data. The whole-brain mean absolute discrepancy in cortical surface positioning and thickness estimation is below 100 μm at the single-subject level and below 50 μm at the group level for the simulated data, and below 200 μm at the single-subject level and below 100 μm at the group level for the empirical data, making the accuracy of cortical surfaces derived from super-resolution sufficient for most applications.
Collapse
Affiliation(s)
- Qiyuan Tian
- 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
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - 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
| | - Chanon Ngamsombat
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
| | - Natalia Zaretskaya
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
- Department of Experimental Psychology and Cognitive Neuroscience, Institute of Psychology, University of Graz, Graz, Austria
- BioTechMed-Graz, Austria
| | - Nina E Fultz
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
| | - Ned A Ohringer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
| | - Akshay S Chaudhari
- Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, CA, United States
| | - Yuxin Hu
- Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, CA, United States
| | - Thomas Witzel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Harvard Medical School, Boston, MA, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - 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
- 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, United States
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| |
Collapse
|
47
|
Tian Q. Phylogenetic relationships and morphological reappraisal of Chaetothyriales. MYCOSPHERE 2021. [DOI: 10.5943/mycosphere/12/1/15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
|
48
|
Tian Q, Si J, Jiang F, Xu R, Wei B, Huang B, Li Q, Jiang Z, Zhao T. Caspofungin combined with TMP/SMZ as a first-line therapy for moderate-to-severe PCP in patients with human immunodeficiency virus infection. HIV Med 2020; 22:307-313. [PMID: 33277811 PMCID: PMC7984216 DOI: 10.1111/hiv.13013] [Citation(s) in RCA: 10] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/12/2020] [Indexed: 01/05/2023]
Abstract
OBJECTIVES The effectiveness of trimethoprim/sulfamethoxazole (TMP/SMZ) for pneumocystis pneumonia (PCP) is limited with adverse events. Caspofungin, by inhibiting the cyst form of Pneumocystis jirovecii, may be an alternative therapy for PCP. However, the availability of clinical data about caspofungin combined with TMP/SMZ in the treatment of PCP in HIV-infected patients is limited. Thus, we aimed to examine the clinical effectiveness and safety of caspofungin combined with TMP/SMZ as a first-line therapy for moderate-to-severe PCP in HIV-infected patients. METHODS From January 2017 to December 2019, data of HIV-infected patients with moderate-to-severe PCP who received either TMP/SMZ alone or caspofungin combined with TMP/SMZ as first-line therapy were retrospectively reviewed to assess the effectiveness and safety of each regimen. The Kaplan-Meier curve and log-rank test were used for survival analysis. RESULTS A total of 278 patients met the criteria. The overall positive response rate of PCP treatment was 48.92%, and the overall all-cause in-hospital mortality rate was 33.09%. Patients who received combination therapy consisting of caspofungin and TMP/SMZ had a better positive response rate (59.44% vs. 37.78%, P < 0.001) and lower all-cause in-hospital mortality rate (24.48% vs. 42.22%, P = 0.003). Also, patients who received combination therapy had higher survival rate during a hospital stay (75.52% vs. 57.78%, P = 0.004), and those who received longer combination therapy were more likely to have higher survival rate (P = 0.042). We found that age (P = 0.019), CD4 cell count (P = 0.001) and therapeutic regimen (P = 0.002) were significant risk factors for all-cause in-hospital mortality rate in univariate analysis. In multivariate analysis, only CD4 cell count and therapeutic regimen were statistically significant factors associated with all-cause in-hospital mortality rate. Patients with a CD4 count of > 30 cells/µL and patients who received combination therapy consisting of caspofungin and TMP/SMZ were more likely to survive from PCP (P = 0.011 and P = 0.002, respectively). There were no additional severe adverse events caused by adding caspofungin. CONCLUSIONS For HIV-infected patients with moderate-to-severe PCP, combination therapy with caspofungin and TMP/SMZ is an effective and promising first-line therapy with no greater number of adverse events compared with TMP/SMZ monotherapy. Patients who received caspofungin had better positive response rates and lower all-cause in-hospital mortality rates. Also, we recommend early initiation of caspofungin.
Collapse
Affiliation(s)
- Q Tian
- The Third People's Hospital of Guilin, Guangxi, China
| | - J Si
- The First Hospital of Jiaxing, Zhejiang, China
| | - F Jiang
- The Third People's Hospital of Guilin, Guangxi, China
| | - R Xu
- The Third People's Hospital of Guilin, Guangxi, China
| | - B Wei
- The Third People's Hospital of Guilin, Guangxi, China
| | - B Huang
- The Third People's Hospital of Guilin, Guangxi, China
| | - Q Li
- The Third People's Hospital of Guilin, Guangxi, China
| | - Z Jiang
- People's Hospital of Liuzhou, Guangxi, China
| | - T Zhao
- The Third People's Hospital of Guilin, Guangxi, China
| |
Collapse
|
49
|
Tian Q, Bilgic B, Fan Q, Liao C, Ngamsombat C, Hu Y, Witzel T, Setsompop K, Polimeni JR, Huang SY. DeepDTI: High-fidelity six-direction diffusion tensor imaging using deep learning. Neuroimage 2020; 219:117017. [PMID: 32504817 PMCID: PMC7646449 DOI: 10.1016/j.neuroimage.2020.117017] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [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: 01/19/2020] [Revised: 05/15/2020] [Accepted: 06/02/2020] [Indexed: 12/14/2022] Open
Abstract
Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue microstructure and structural connectivity in the living human brain. Nonetheless, the angular sampling requirement for DTI leads to long scan times and poses a critical barrier to performing high-quality DTI in routine clinical practice and large-scale research studies. In this work we present a new processing framework for DTI entitled DeepDTI that minimizes the data requirement of DTI to six diffusion-weighted images (DWIs) required by conventional voxel-wise fitting methods for deriving the six unique unknowns in a diffusion tensor using data-driven supervised deep learning. DeepDTI maps the input non-diffusion-weighted (b = 0) image and six DWI volumes sampled along optimized diffusion-encoding directions, along with T1-weighted and T2-weighted image volumes, to the residuals between the input and high-quality output b = 0 image and DWI volumes using a 10-layer three-dimensional convolutional neural network (CNN). The inputs and outputs of DeepDTI are uniquely formulated, which not only enables residual learning to boost CNN performance but also enables tensor fitting of resultant high-quality DWIs to generate orientational DTI metrics for tractography. The very deep CNN used by DeepDTI leverages the redundancy in local and non-local spatial information and across diffusion-encoding directions and image contrasts in the data. The performance of DeepDTI was systematically quantified in terms of the quality of the output images, DTI metrics, DTI-based tractography and tract-specific analysis results. We demonstrate rotationally-invariant and robust estimation of DTI metrics from DeepDTI that are comparable to those obtained with two b = 0 images and 21 DWIs for the primary eigenvector derived from DTI and two b = 0 images and 26-30 DWIs for various scalar metrics derived from DTI, achieving 3.3-4.6 × acceleration, and twice as good as those of a state-of-the-art denoising algorithm at the group level. The twenty major white-matter tracts can be accurately identified from the tractography of DeepDTI results. The mean distance between the core of the major white-matter tracts identified from DeepDTI results and those from the ground-truth results using 18 b = 0 images and 90 DWIs measures around 1-1.5 mm. DeepDTI leverages domain knowledge of diffusion MRI physics and power of deep learning to render DTI, DTI-based tractography, major white-matter tracts identification and tract-specific analysis more feasible for a wider range of neuroscientific and clinical studies.
Collapse
Affiliation(s)
- Qiyuan Tian
- 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; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - 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
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Chanon Ngamsombat
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Thailand
| | - Yuxin Hu
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States
| | - Thomas Witzel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - 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; 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, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| |
Collapse
|
50
|
Zhang H, Liu L, Ni JJ, Wei XT, Feng GJ, Yang XL, Xu Q, Zhang ZJ, Hai R, Tian Q, Shen H, Deng HW, Pei YF, Zhang L. Pleiotropic loci underlying bone mineral density and bone size identified by a bivariate genome-wide association analysis. Osteoporos Int 2020; 31:1691-1701. [PMID: 32314116 PMCID: PMC7883523 DOI: 10.1007/s00198-020-05389-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 03/11/2020] [Indexed: 01/30/2023]
Abstract
UNLABELLED Aiming to identify pleiotropic genomic loci for bone mineral density and bone size, we performed a bivariate GWAS in five discovery samples and replicated in two large-scale samples. We identified 2 novel loci at 2q37.1 and 6q26. Our findings provide insight into common genetic architecture underlying both traits. INTRODUCTION Bone mineral density (BMD) and bone size (BS) are two important factors that contribute to the development of osteoporosis and osteoporotic fracture. Both BMD and BS are highly heritable and they are genetically correlated. In this study, we aim to identify pleiotropic loci associated with BMD and BS. METHODS We conducted a bivariate genome-wide association (GWA) analysis of hip BMD and hip BS in 6180 participants from 5 samples, followed by in silico replication in the UK Biobank study of BMD (N = 426,824) and the deCODE study of BS (N = 28,954), respectively. RESULTS SNPs from 2 genomic loci were significant at the genome-wide significance (GWS) level (p lt; 5 × 10-8) in the discovery samples and were successfully replicated in the replication samples (2q37.1, lead SNP rs7575512, discovery p = 1.49 × 10-10, replication p = 0.05; 6q26, lead SNP rs1040724, discovery p = 1.95 × 10-8, replication p = 0.03). Functional annotations suggested functional relevance of the identified variants to bone development. CONCLUSION Our findings provide insight into the common genetic architecture underlying BMD and BS, and enhance our understanding of the potential mechanism of osteoporosis fracture.
Collapse
Affiliation(s)
- H Zhang
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
| | - L Liu
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
- Kunshan Hospital of Traditional Chinese Medicine, SuZhou, Jiangsu, People's Republic of China
| | - J-J Ni
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
| | - X-T Wei
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
- Department of Epidemiology and Health Statistics, School of Public Health, Medical College of Soochow University, SuZhou, Jiangsu, People's Republic of China
| | - G-J Feng
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
- Department of Epidemiology and Health Statistics, School of Public Health, Medical College of Soochow University, SuZhou, Jiangsu, People's Republic of China
| | - X-L Yang
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
| | - Q Xu
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
- Department of Epidemiology and Health Statistics, School of Public Health, Medical College of Soochow University, SuZhou, Jiangsu, People's Republic of China
| | - Z-J Zhang
- People's Hospital of Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, People's Republic of China
| | - R Hai
- People's Hospital of Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, People's Republic of China
| | - Q Tian
- Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - H Shen
- Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - H-W Deng
- Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA.
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal St., Suite 2001, New Orleans, LA, 70112, USA.
| | - Y-F Pei
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China.
- Department of Epidemiology and Health Statistics, School of Public Health, Medical College of Soochow University, SuZhou, Jiangsu, People's Republic of China.
| | - L Zhang
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China.
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China.
| |
Collapse
|