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Chatterjee A, Dwivedi DK. MRI-based virtual pathology of the prostate. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01163-w. [PMID: 38856839 DOI: 10.1007/s10334-024-01163-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024]
Abstract
Prostate cancer poses significant diagnostic challenges, with conventional methods like prostate-specific antigen (PSA) screening and transrectal ultrasound (TRUS)-guided biopsies often leading to overdiagnosis or miss clinically significant cancers. Multiparametric MRI (mpMRI) has emerged as a more reliable tool. However, it is limited by high inter-observer variability and radiologists missing up to 30% of clinically significant cancers. This article summarizes a few of these recent advancements in quantitative MRI techniques that look at the "Virtual Pathology" of the prostate with an aim to enhance prostate cancer detection and characterization. These techniques include T2 relaxation-based techniques such as luminal water imaging, diffusion based such as vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) and restriction spectrum imaging or combined relaxation-diffusion techniques such as hybrid multi-dimensional MRI (HM-MRI), time-dependent diffusion imaging, and diffusion-relaxation correlation spectrum imaging. These methods provide detailed insights into underlying prostate microstructure and tissue composition and have shown improved diagnostic accuracy over conventional MRI. These innovative MRI methods hold potential for augmenting mpMRI, reducing variability in diagnosis, and paving the way for MRI as a 'virtual histology' tool in prostate cancer diagnosis. However, they require further validation in larger multi-center clinical settings and rigorous in-depth radiological-pathology correlation are needed for broader implementation.
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Affiliation(s)
- Aritrick Chatterjee
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, IL, 60637, USA.
- Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, IL, USA.
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Barsoum S, Latimer CS, Nolan AL, Barrett A, Chang K, Troncoso J, Keene CD, Benjamini D. Resiliency to Alzheimer's disease neuropathology can be distinguished from dementia using cortical astrogliosis imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.06.592719. [PMID: 38766087 PMCID: PMC11100587 DOI: 10.1101/2024.05.06.592719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Despite the presence of significant Alzheimer's disease (AD) pathology, characterized by amyloid β (Aβ) plaques and phosphorylated tau (pTau) tangles, some cognitively normal elderly individuals do not inevitably develop dementia. These findings give rise to the notion of cognitive 'resilience', suggesting maintained cognitive function despite the presence of AD neuropathology, highlighting the influence of factors beyond classical pathology. Cortical astroglial inflammation, a ubiquitous feature of symptomatic AD, shows a strong correlation with cognitive impairment severity, potentially contributing to the diversity of clinical presentations. However, noninvasively imaging neuroinflammation, particularly astrogliosis, using MRI remains a significant challenge. Here we sought to address this challenge and to leverage multidimensional (MD) MRI, a powerful approach that combines relaxation with diffusion MR contrasts, to map cortical astrogliosis in the human brain by accessing sub-voxel information. Our goal was to test whether MD-MRI can map astroglial pathology in the cerebral cortex, and if so, whether it can distinguish cognitive resiliency from dementia in the presence of hallmark AD neuropathological changes. We adopted a multimodal approach by integrating histological and MRI analyses using human postmortem brain samples. Ex vivo cerebral cortical tissue specimens derived from three groups comprised of non-demented individuals with significant AD pathology postmortem, individuals with both AD pathology and dementia, and non-demented individuals with minimal AD pathology postmortem as controls, underwent MRI at 7 T. We acquired and processed MD-MRI, diffusion tensor, and quantitative T 1 and T 2 MRI data, followed by histopathological processing on slices from the same tissue. By carefully co-registering MRI and microscopy data, we performed quantitative multimodal analyses, leveraging targeted immunostaining to assess MD-MRI sensitivity and specificity towards Aβ, pTau, and glial fibrillary acidic protein (GFAP), a marker for astrogliosis. Our findings reveal a distinct MD-MRI signature of cortical astrogliosis, enabling the creation of predictive maps for cognitive resilience amid AD neuropathological changes. Multiple linear regression linked histological values to MRI changes, revealing that the MD-MRI cortical astrogliosis biomarker was significantly associated with GFAP burden (standardized β=0.658, pFDR<0.0001), but not with Aβ (standardized β=0.009, p FDR =0.913) or pTau (standardized β=-0.196, p FDR =0.051). Conversely, none of the conventional MRI parameters showed significant associations with GFAP burden in the cortex. While the extent to which pathological glial activation contributes to neuronal damage and cognitive impairment in AD is uncertain, developing a noninvasive imaging method to see its affects holds promise from a mechanistic perspective and as a potential predictor of cognitive outcomes.
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Johnson JTE, Irfanoglu MO, Manninen E, Ross TJ, Yang Y, Laun FB, Martin J, Topgaard D, Benjamini D. In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI. Hum Brain Mapp 2024; 45:e26697. [PMID: 38726888 PMCID: PMC11082920 DOI: 10.1002/hbm.26697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 03/28/2024] [Accepted: 04/12/2024] [Indexed: 05/13/2024] Open
Abstract
Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD-MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD-MRI with concepts from oscillating gradients. This framework probes the diffusion frequency,ω $$ \omega $$ , in addition to the diffusion tensor,D $$ \mathbf{D} $$ , and relaxation,R 1 $$ {R}_1 $$ ,R 2 $$ {R}_2 $$ , correlations. AD ω - R 1 - R 2 $$ \mathbf{D}\left(\omega \right)-{R}_1-{R}_2 $$ clinical imaging protocol was then introduced, with limited brain coverage and 3 mm3 voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD-MRI acquisition protocol providing whole brain coverage at 2 mm3 voxel size. We demonstrate its feasibility and robustness using a well-defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD-MRI encoding design demands a nuanced denoising approach. The MD-MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on theirD ω - R 1 - R 2 $$ \mathbf{D}\left(\omega \right)-{R}_1-{R}_2 $$ distribution, enabling the creation of microstructure-specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.
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Affiliation(s)
- Jessica T. E. Johnson
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIHBaltimoreMarylandUSA
| | - M. Okan Irfanoglu
- Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of HealthBethesdaMarylandUSA
| | - Eppu Manninen
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIHBaltimoreMarylandUSA
| | - Thomas J. Ross
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of HealthBaltimoreMarylandUSA
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of HealthBaltimoreMarylandUSA
| | - Frederik B. Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Jan Martin
- Department of ChemistryLund UniversityLundSweden
| | | | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIHBaltimoreMarylandUSA
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Dai Y, Hu W, Wu G, Wu D, Zhu M, Luo Y, Wang J, Zhou Y, Hu P. Grading Clear Cell Renal Cell Carcinoma Grade Using Diffusion Relaxation Correlated MR Spectroscopic Imaging. J Magn Reson Imaging 2024; 59:699-710. [PMID: 37209407 DOI: 10.1002/jmri.28777] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/01/2023] [Accepted: 05/02/2023] [Indexed: 05/22/2023] Open
Abstract
BACKGROUND Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC, and accurate grading is crucial for prognosis and treatment selection. Biopsy is the reference standard for grading, but MRI methods can improve and complement the grading procedure. PURPOSE Assess the performance of diffusion relaxation correlation spectroscopic imaging (DR-CSI) in grading ccRCC. STUDY TYPE Prospective. SUBJECTS 79 patients (age: 58.1 +/- 11.5 years; 55 male) with ccRCC confirmed by histopathology (grade 1, 7; grade 2, 45; grade 3, 18; grade 4, 9) following surgery. FIELD STRENGTH/SEQUENCE 3.0 T MRI scanner. DR-CSI with a diffusion-weighted echo-planar imaging sequence and T2-mapping with a multi-echo spin echo sequence. ASSESSMENT DR-CSI results were analyzed for the solid tumor regions of interest using spectrum segmentation with five sub-region volume fraction metrics (VA , VB , VC , VD , and VE ). The regulations for spectrum segmentation were determined based on the D-T2 spectra of distinct macro-components. Tumor size, voxel-wise T2, and apparent diffusion coefficient (ADC) values were obtained. Histopathology assessed tumor grade (G1-G4) for each case. STATISTICAL TESTS One-way ANOVA or Kruskal-Wallis test, Spearman's correlation (coefficient, rho), multivariable logistic regression analysis, receiver operating characteristic curve analysis, and DeLong's test. Significance criteria: P < 0.05. RESULTS Significant differences were found in ADC, T2, DR-CSI VB , and VD among the ccRCC grades. Correlations were found for ccRCC grade to tumor size (rho = 0.419), age (rho = 0.253), VB (rho = 0.553) and VD (rho = -0.378). AUC of VB was slightly larger than ADC in distinguishing low-grade (G1-G2) from high-grade (G3-G4) ccRCC (0.801 vs. 0.762, P = 0.406) and G1 from G2 to G4 (0.796 vs. 0.647, P = 0.175), although not significant. Combining VB , VD , and VE had better diagnostic performance than combining ADC and T2 for differentiating G1 from G2-G4 (AUC: 0.814 vs 0.643). DATA CONCLUSION DR-CSI parameters are correlated with ccRCC grades, and may help to differentiate ccRCC grades. EVIDENCE LEVEL 2 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Yongming Dai
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Wentao Hu
- Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Guangyu Wu
- Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Mengying Zhu
- Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yuansheng Luo
- Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jieying Wang
- Clinical Research Center, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yan Zhou
- Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Peng Hu
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
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Johnson JT, Irfanoglu MO, Manninen E, Ross TJ, Yang Y, Laun FB, Martin J, Topgaard D, Benjamini D. In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.10.561702. [PMID: 37987005 PMCID: PMC10659440 DOI: 10.1101/2023.10.10.561702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD-MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD-MRI with concepts from oscillating gradients. This framework probes the diffusion frequency, ω , in addition to the diffusion tensor, D , and relaxation, R 1 , R 2 , correlations. A D ( ω ) - R 1 - R 2 clinical imaging protocol was then introduced, with limited brain coverage and 3 mm3 voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD-MRI acquisition protocol providing whole brain coverage at 2 mm3 voxel size. We demonstrate its feasibility and robustness using a well-defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD-MRI encoding design demands a nuanced denoising approach. The MD-MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on their D ( ω ) - R 1 - R 2 distribution, enabling the creation of microstructure-specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.
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Affiliation(s)
- Jessica T.E. Johnson
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - M. Okan Irfanoglu
- Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
| | - Eppu Manninen
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Thomas J. Ross
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
| | - Frederik B. Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Jan Martin
- Department of Chemistry, Lund University, Lund, Sweden
| | | | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
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Kundu S, Barsoum S, Ariza J, Nolan AL, Latimer CS, Keene CD, Basser PJ, Benjamini D. Mapping the individual human cortex using multidimensional MRI and unsupervised learning. Brain Commun 2023; 5:fcad258. [PMID: 37953850 PMCID: PMC10638106 DOI: 10.1093/braincomms/fcad258] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/31/2023] [Accepted: 10/05/2023] [Indexed: 11/14/2023] Open
Abstract
Human evolution has seen the development of higher-order cognitive and social capabilities in conjunction with the unique laminar cytoarchitecture of the human cortex. Moreover, early-life cortical maldevelopment has been associated with various neurodevelopmental diseases. Despite these connections, there is currently no noninvasive technique available for imaging the detailed cortical laminar structure. This study aims to address this scientific and clinical gap by introducing an approach for imaging human cortical lamina. This method combines diffusion-relaxation multidimensional MRI with a tailored unsupervised machine learning approach that introduces enhanced microstructural sensitivity. This new imaging method simultaneously encodes the microstructure, the local chemical composition and importantly their correlation within complex and heterogenous tissue. To validate our approach, we compared the intra-cortical layers obtained using our ex vivo MRI-based method with those derived from Nissl staining of postmortem human brain specimens. The integration of unsupervised learning with diffusion-relaxation correlation MRI generated maps that demonstrate sensitivity to areal differences in cytoarchitectonic features observed in histology. Significantly, our observations revealed layer-specific diffusion-relaxation signatures, showing reductions in both relaxation times and diffusivities at the deeper cortical levels. These findings suggest a radial decrease in myelin content and changes in cell size and anisotropy, reflecting variations in both cytoarchitecture and myeloarchitecture. Additionally, we demonstrated that 1D relaxation and high-order diffusion MRI scalar indices, even when aggregated and used jointly in a multimodal fashion, cannot disentangle the cortical layers. Looking ahead, our technique holds the potential to open new avenues of research in human neurodevelopment and the vast array of disorders caused by disruptions in neurodevelopment.
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Affiliation(s)
- Shinjini Kundu
- Department of Radiology, The Johns Hopkins Hospital, Baltimore, MD 21287, USA
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20892, USA
| | - Stephanie Barsoum
- Multiscale Imaging and Integrative Biophysics Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Jeanelle Ariza
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Amber L Nolan
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Caitlin S Latimer
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Peter J Basser
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20892, USA
| | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore, MD 21224, USA
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7
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Endt S, Engel M, Naldi E, Assereto R, Molendowska M, Mueller L, Mayrink Verdun C, Pirkl CM, Palombo M, Jones DK, Menzel MI. In Vivo Myelin Water Quantification Using Diffusion-Relaxation Correlation MRI: A Comparison of 1D and 2D Methods. APPLIED MAGNETIC RESONANCE 2023; 54:1571-1588. [PMID: 38037641 PMCID: PMC10682074 DOI: 10.1007/s00723-023-01584-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/18/2023] [Accepted: 07/25/2023] [Indexed: 12/02/2023]
Abstract
Multidimensional Magnetic Resonance Imaging (MRI) is a versatile tool for microstructure mapping. We use a diffusion weighted inversion recovery spin echo (DW-IR-SE) sequence with spiral readouts at ultra-strong gradients to acquire a rich diffusion-relaxation data set with sensitivity to myelin water. We reconstruct 1D and 2D spectra with a two-step convex optimization approach and investigate a variety of multidimensional MRI methods, including 1D multi-component relaxometry, 1D multi-component diffusometry, 2D relaxation correlation imaging, and 2D diffusion-relaxation correlation spectroscopic imaging (DR-CSI), in terms of their potential to quantify tissue microstructure, including the myelin water fraction (MWF). We observe a distinct spectral peak that we attribute to myelin water in multi-component T1 relaxometry, T1-T2 correlation, T1-D correlation, and T2-D correlation imaging. Due to lower achievable echo times compared to diffusometry, MWF maps from relaxometry have higher quality. Whilst 1D multi-component T1 data allows much faster myelin mapping, 2D approaches could offer unique insights into tissue microstructure and especially myelin diffusion.
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Affiliation(s)
- Sebastian Endt
- Technical University of Munich, Munich, Germany
- AImotion Bavaria, Technische Hochschule Ingolstadt, Ingolstadt, Germany
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Maria Engel
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Emanuele Naldi
- Technische Universität Braunschweig, Braunschweig, Germany
| | | | - Malwina Molendowska
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
- Medical Radiation Physics, Lund University, Lund, Sweden
| | - Lars Mueller
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
- Biomedical Imaging Science Department, Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM), University of Leeds, Leeds, United Kingdom
| | - Claudio Mayrink Verdun
- Technical University of Munich, Munich, Germany
- Munich Center for Machine Learning, Munich, Germany
| | | | - Marco Palombo
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Marion I. Menzel
- Technical University of Munich, Munich, Germany
- AImotion Bavaria, Technische Hochschule Ingolstadt, Ingolstadt, Germany
- GE HealthCare, Munich, Germany
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8
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Benjamini D, Priemer DS, Perl DP, Brody DL, Basser PJ. Mapping astrogliosis in the individual human brain using multidimensional MRI. Brain 2023; 146:1212-1226. [PMID: 35953450 PMCID: PMC9976979 DOI: 10.1093/brain/awac298] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 06/13/2022] [Accepted: 08/06/2022] [Indexed: 11/14/2022] Open
Abstract
There are currently no non-invasive imaging methods available for astrogliosis assessment or mapping in the central nervous system despite its essential role in the response to many disease states, such as infarcts, neurodegenerative conditions, traumatic brain injury and infection. Multidimensional MRI is an increasingly employed imaging modality that maximizes the amount of encoded chemical and microstructural information by probing relaxation (T1 and T2) and diffusion mechanisms simultaneously. Here, we harness the exquisite sensitivity of this imagining modality to derive a signature of astrogliosis and disentangle it from normative brain at the individual level using machine learning. We investigated ex vivo cerebral cortical tissue specimens derived from seven subjects who sustained blast-induced injuries, which resulted in scar-border forming astrogliosis without being accompanied by other types of neuropathological abnormality, and from seven control brain donors. By performing a combined post-mortem radiology and histopathology correlation study we found that astrogliosis induces microstructural and chemical changes that are robustly detected with multidimensional MRI, and which can be attributed to astrogliosis because no axonal damage, demyelination or tauopathy were histologically observed in any of the cases in the study. Importantly, we showed that no one-dimensional T1, T2 or diffusion MRI measurement can disentangle the microscopic alterations caused by this neuropathology. Based on these findings, we developed a within-subject anomaly detection procedure that generates MRI-based astrogliosis biomarker maps ex vivo, which were significantly and strongly correlated with co-registered histological images of increased glial fibrillary acidic protein deposition (r = 0.856, P < 0.0001; r = 0.789, P < 0.0001; r = 0.793, P < 0.0001, for diffusion-T2, diffusion-T1 and T1-T2 multidimensional data sets, respectively). Our findings elucidate the underpinning of MRI signal response from astrogliosis, and the demonstrated high spatial sensitivity and specificity in detecting reactive astrocytes at the individual level, and if reproduced in vivo, will significantly impact neuroimaging studies of injury, disease, repair and aging, in which astrogliosis has so far been an invisible process radiologically.
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Affiliation(s)
- Dan Benjamini
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20891, USA
- Multiscale Imaging and Integrative Biophysics Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore, MD 21224, USA
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
| | - David S Priemer
- Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD 20814, USA
- The Department of Defense/Uniformed Services, University Brain Tissue Repository, Bethesda, MD 20814, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD 20817, USA
| | - Daniel P Perl
- Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD 20814, USA
- The Department of Defense/Uniformed Services, University Brain Tissue Repository, Bethesda, MD 20814, USA
| | - David L Brody
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
- Department of Neurology, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD 20814, USA
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD 20892, USA
| | - Peter J Basser
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD 20891, USA
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
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Avram AV, Saleem KS, Basser PJ. COnstrained Reference frame diffusion TEnsor Correlation Spectroscopic (CORTECS) MRI: A practical framework for high-resolution diffusion tensor distribution imaging. Front Neurosci 2022; 16:1054509. [PMID: 36590291 PMCID: PMC9798222 DOI: 10.3389/fnins.2022.1054509] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022] Open
Abstract
High-resolution imaging studies have consistently shown that in cortical tissue water diffuses preferentially along radial and tangential orientations with respect to the cortical surface, in agreement with histology. These dominant orientations do not change significantly even if the relative contributions from microscopic water pools to the net voxel signal vary across experiments that use different diffusion times, b-values, TEs, and TRs. With this in mind, we propose a practical new framework for imaging non-parametric diffusion tensor distributions (DTDs) by constraining the microscopic diffusion tensors of the DTD to be diagonalized using the same orthonormal reference frame of the mesoscopic voxel. In each voxel, the constrained DTD (cDTD) is completely determined by the correlation spectrum of the microscopic principal diffusivities associated with the axes of the voxel reference frame. Consequently, all cDTDs are inherently limited to the domain of positive definite tensors and can be reconstructed efficiently using Inverse Laplace Transform methods. Moreover, the cDTD reconstruction can be performed using only data acquired efficiently with single diffusion encoding, although it also supports datasets with multiple diffusion encoding. In tissues with a well-defined architecture, such as the cortex, we can further constrain the cDTD to contain only cylindrically symmetric diffusion tensors and measure the 2D correlation spectra of principal diffusivities along the radial and tangential orientation with respect to the cortical surface. To demonstrate this framework, we perform numerical simulations and analyze high-resolution dMRI data from a fixed macaque monkey brain. We estimate 2D cDTDs in the cortex and derive, in each voxel, the marginal distributions of the microscopic principal diffusivities, the corresponding distributions of the microscopic fractional anisotropies and mean diffusivities along with their 2D correlation spectra to quantify the cDTD shape-size characteristics. Signal components corresponding to specific bands in these cDTD-derived spectra show high specificity to cortical laminar structures observed with histology. Our framework drastically simplifies the measurement of non-parametric DTDs in high-resolution datasets with mesoscopic voxel sizes much smaller than the radius of curvature of the underlying anatomy, e.g., cortical surface, and can be applied retrospectively to analyze existing diffusion MRI data from fixed cortical tissues.
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Affiliation(s)
- Alexandru V. Avram
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
- Center for Neuroscience and Regenerative Medicine, Bethesda, MD, United States
- Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD, United States
| | - Kadharbatcha S. Saleem
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
- Center for Neuroscience and Regenerative Medicine, Bethesda, MD, United States
- Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD, United States
| | - Peter J. Basser
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
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Rosenberg JT, Grant SC, Topgaard D. Nonparametric 5D D-R 2 distribution imaging with single-shot EPI at 21.1 T: Initial results for in vivo rat brain. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 341:107256. [PMID: 35753184 PMCID: PMC9339475 DOI: 10.1016/j.jmr.2022.107256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/27/2022] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
In vivo human diffusion MRI is by default performed using single-shot EPI with greater than 50-ms echo times and associated signal loss from transverse relaxation. The individual benefits of the current trends of increasing B0 to boost SNR and employing more advanced signal preparation schemes to improve the specificity for selected microstructural properties eventually may be cancelled by increased relaxation rates at high B0 and echo times with advanced encoding. Here, initial attempts to translate state-of-the-art diffusion-relaxation correlation methods from 3 T to 21.1 T are made to identify hurdles that need to be overcome to fulfill the promises of both high SNR and readily interpretable microstructural information.
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Affiliation(s)
- Jens T Rosenberg
- National High Magnetic Field Laboratory, Florida State University, Tallahassee FL, United States.
| | - Samuel C Grant
- National High Magnetic Field Laboratory, Florida State University, Tallahassee FL, United States; Chemical and Biomedical Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL, United States.
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11
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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 PMCID: PMC9121330 DOI: 10.1016/j.neuroimage.2022.118958] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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 diffusion MRI 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 diffusion MRI and where the field is headed in the coming years.
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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, USA; 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, the Netherlands
| | - Mathias Davids
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; 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, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - 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, USA; Harvard Medical School, Boston, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | | | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Yi-Qiao Song
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; 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, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lawrence L Wald
- 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
| | - 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.
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12
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Silletta EV, Velasco MI, Monti GA, Acosta RH. Comparison of experimental times in T 1-D and D-T 2 correlation experiments in single-sided NMR. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 334:107112. [PMID: 34864390 DOI: 10.1016/j.jmr.2021.107112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 11/16/2021] [Accepted: 11/22/2021] [Indexed: 06/13/2023]
Abstract
Diffusion-relaxation correlation experiments in nuclear magnetic resonance are a powerful technique for the characterization of fluid dynamics in confined geometries or soft matter, in which relaxation may be either spin-spin (T2) or spin-lattice (T1). The general approach is to acquire a set of bidimensional data in which diffusion is codified by the evolution of the magnetization with either Hahn or stimulated echoes (STE) in the presence of a constant magnetic field gradient. While T2 is codified by a Carr-Purcell-Meiboom-Gil (CPMG) sequence, T1 is either encoded by saturation or inversion-recovery methods. In this work, we analyse the measurement time of diffusion-relaxation times in single-sided NMR and show that T1-D acquisition is always shorter than D-T2. Depending on the hardware characteristics, this time reduction can be up to an order of magnitude. We present analytical calculations and examples in model porous media saturated with water and in a dairy product.
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Affiliation(s)
- Emilia V Silletta
- Universidad Nacional de Córdoba, Facultad de Matemática, Astronomía, Física y Computación, Córdoba, Argentina; CONICET, Instituto de Física Enrique Gaviola (IFEG), Córdoba, Argentina
| | - Manuel I Velasco
- Universidad Nacional de Córdoba, Facultad de Matemática, Astronomía, Física y Computación, Córdoba, Argentina; CONICET, Instituto de Física Enrique Gaviola (IFEG), Córdoba, Argentina
| | - Gustavo A Monti
- Universidad Nacional de Córdoba, Facultad de Matemática, Astronomía, Física y Computación, Córdoba, Argentina; CONICET, Instituto de Física Enrique Gaviola (IFEG), Córdoba, Argentina
| | - Rodolfo H Acosta
- Universidad Nacional de Córdoba, Facultad de Matemática, Astronomía, Física y Computación, Córdoba, Argentina; CONICET, Instituto de Física Enrique Gaviola (IFEG), Córdoba, Argentina.
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13
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Nonparametric D-R 1-R 2 distribution MRI of the living human brain. Neuroimage 2021; 245:118753. [PMID: 34852278 DOI: 10.1016/j.neuroimage.2021.118753] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/17/2021] [Accepted: 11/22/2021] [Indexed: 11/23/2022] Open
Abstract
Diffusion-relaxation correlation NMR can simultaneously characterize both the microstructure and the local chemical composition of complex samples that contain multiple populations of water. Recent developments on tensor-valued diffusion encoding and Monte Carlo inversion algorithms have made it possible to transfer diffusion-relaxation correlation NMR from small-bore scanners to clinical MRI systems. Initial studies on clinical MRI systems employed 5D D-R1 and D-R2 correlation to characterize healthy brain in vivo. However, these methods are subject to an inherent bias that originates from not including R2 or R1 in the analysis, respectively. This drawback can be remedied by extending the concept to 6D D-R1-R2 correlation. In this work, we present a sparse acquisition protocol that records all data necessary for in vivo 6D D-R1-R2 correlation MRI across 633 individual measurements within 25 min-a time frame comparable to previous lower-dimensional acquisition protocols. The data were processed with a Monte Carlo inversion algorithm to obtain nonparametric 6D D-R1-R2 distributions. We validated the reproducibility of the method in repeated measurements of healthy volunteers. For a post-therapy glioblastoma case featuring cysts, edema, and partially necrotic remains of tumor, we present representative single-voxel 6D distributions, parameter maps, and artificial contrasts over a wide range of diffusion-, R1-, and R2-weightings based on the rich information contained in the D-R1-R2 distributions.
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14
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Slator PJ, Palombo M, Miller KL, Westin C, Laun F, Kim D, Haldar JP, Benjamini D, Lemberskiy G, de Almeida Martins JP, Hutter J. Combined diffusion-relaxometry microstructure imaging: Current status and future prospects. Magn Reson Med 2021; 86:2987-3011. [PMID: 34411331 PMCID: PMC8568657 DOI: 10.1002/mrm.28963] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 06/25/2021] [Accepted: 07/20/2021] [Indexed: 12/15/2022]
Abstract
Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that has the potential to provide a more detailed assessment of tissue microstructure-combined diffusion-relaxometry imaging. Combined diffusion-relaxometry acquisitions vary multiple MR contrast encodings-such as b-value, gradient direction, inversion time, and echo time-in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters-such as diffusivity, T 1 , T 2 , and T 2 ∗ . This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single-contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.
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Affiliation(s)
- Paddy J. Slator
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Marco Palombo
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Carl‐Fredrik Westin
- Department of RadiologyBrigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA
| | - Frederik Laun
- Institute of RadiologyUniversity Hospital ErlangenFriedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Daeun Kim
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Dan Benjamini
- The Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBethesdaMDUSA
- The Center for Neuroscience and Regenerative MedicineUniformed Service University of the Health SciencesBethesdaMDUSA
| | | | - Joao P. de Almeida Martins
- Division of Physical Chemistry, Department of ChemistryLund UniversityLundSweden
- Department of Radiology and Nuclear MedicineSt. Olav’s University HospitalTrondheimNorway
| | - Jana Hutter
- Centre for Biomedical EngineeringSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
- Centre for the Developing BrainSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
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15
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Benjamini D, Bouhrara M, Komlosh ME, Iacono D, Perl DP, Brody DL, Basser PJ. Multidimensional MRI for Characterization of Subtle Axonal Injury Accelerated Using an Adaptive Nonlocal Multispectral Filter. FRONTIERS IN PHYSICS 2021; 9:737374. [PMID: 37408700 PMCID: PMC10321473 DOI: 10.3389/fphy.2021.737374] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
Abstract
Multidimensional MRI is an emerging approach that simultaneously encodes water relaxation (T1 and T2) and mobility (diffusion) and replaces voxel-averaged values with subvoxel distributions of those MR properties. While conventional (i.e., voxel-averaged) MRI methods cannot adequately quantify the microscopic heterogeneity of biological tissue, using subvoxel information allows to selectively map a specific T1-T2-diffusion spectral range that corresponds to a group of tissue elements. The major obstacle to the adoption of rich, multidimensional MRI protocols for diagnostic or monitoring purposes is the prolonged scan time. Our main goal in the present study is to evaluate the performance of a nonlocal estimation of multispectral magnitudes (NESMA) filter on reduced datasets to limit the total acquisition time required for reliable multidimensional MRI characterization of the brain. Here we focused and reprocessed results from a recent study that identified potential imaging biomarkers of axonal injury pathology from the joint analysis of multidimensional MRI, in particular voxelwise T1-T2 and diffusion-T2 spectra in human Corpus Callosum, and histopathological data. We tested the performance of NESMA and its effect on the accuracy of the injury biomarker maps, relative to the co-registered histological reference. Noise reduction improved the accuracy of the resulting injury biomarker maps, while permitting data reduction of 35.7 and 59.6% from the full dataset for T1-T2 and diffusion-T2 cases, respectively. As successful clinical proof-of-concept applications of multidimensional MRI are continuously being introduced, reliable and robust noise removal and consequent acquisition acceleration would advance the field towards clinically-feasible diagnostic multidimensional MRI protocols.
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Affiliation(s)
- Dan Benjamini
- Section on Quantitative Imaging and Tissue Sciences, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
- The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, United States
| | - Mustapha Bouhrara
- Magnetic Resonance Physics of Aging and Dementia Unit, National Institute of Aging, National Institutes of Health, Baltimore, MD, United States
| | - Michal E. Komlosh
- Section on Quantitative Imaging and Tissue Sciences, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
- The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, United States
| | - Diego Iacono
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
- The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, United States
- Brain Tissue Repository and Neuropathology Program, Uniformed Services University (USU), Bethesda, MD, United States
- Department of Neurology, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD, United States
- Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD, United States
- Department of Anatomy, Physiology and Genetics, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD, United States
- Neurodegeneration Disorders Clinic, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Daniel P. Perl
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
- Brain Tissue Repository and Neuropathology Program, Uniformed Services University (USU), Bethesda, MD, United States
- Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University, Bethesda, MD, United States
| | - David L. Brody
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Peter J. Basser
- Section on Quantitative Imaging and Tissue Sciences, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
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16
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Benjamini D, Iacono D, Komlosh ME, Perl DP, Brody DL, Basser PJ. Diffuse axonal injury has a characteristic multidimensional MRI signature in the human brain. Brain 2021; 144:800-816. [PMID: 33739417 DOI: 10.1093/brain/awaa447] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 09/11/2020] [Accepted: 10/11/2020] [Indexed: 02/01/2023] Open
Abstract
Axonal injury is a major contributor to the clinical symptomatology in patients with traumatic brain injury. Conventional neuroradiological tools, such as CT and MRI, are insensitive to diffuse axonal injury (DAI) caused by trauma. Diffusion tensor MRI parameters may change in DAI lesions; however, the nature of these changes is inconsistent. Multidimensional MRI is an emerging approach that combines T1, T2, and diffusion, and replaces voxel-averaged values with distributions, which allows selective isolation of specific potential abnormal components. By performing a combined post-mortem multidimensional MRI and histopathology study, we aimed to investigate T1-T2-diffusion changes linked to DAI and to define their histopathological correlates. Corpora callosa derived from eight subjects who had sustained traumatic brain injury, and three control brain donors underwent post-mortem ex vivo MRI at 7 T. Multidimensional, diffusion tensor, and quantitative T1 and T2 MRI data were acquired and processed. Following MRI acquisition, slices from the same tissue were tested for amyloid precursor protein (APP) immunoreactivity to define DAI severity. A robust image co-registration method was applied to accurately match MRI-derived parameters and histopathology, after which 12 regions of interest per tissue block were selected based on APP density, but blind to MRI. We identified abnormal multidimensional T1-T2, diffusion-T2, and diffusion-T1 components that are strongly associated with DAI and used them to generate axonal injury images. We found that compared to control white matter, mild and severe DAI lesions contained significantly larger abnormal T1-T2 component (P = 0.005 and P < 0.001, respectively), and significantly larger abnormal diffusion-T2 component (P = 0.005 and P < 0.001, respectively). Furthermore, within patients with traumatic brain injury the multidimensional MRI biomarkers differentiated normal-appearing white matter from mild and severe DAI lesions, with significantly larger abnormal T1-T2 and diffusion-T2 components (P = 0.003 and P < 0.001, respectively, for T1-T2; P = 0.022 and P < 0.001, respectively, for diffusion-T2). Conversely, none of the conventional quantitative MRI parameters were able to differentiate lesions and normal-appearing white matter. Lastly, we found that the abnormal T1-T2, diffusion-T1, and diffusion-T2 components and their axonal damage images were strongly correlated with quantitative APP staining (r = 0.876, P < 0.001; r = 0.727, P < 0.001; and r = 0.743, P < 0.001, respectively), while producing negligible intensities in grey matter and in normal-appearing white matter. These results suggest that multidimensional MRI may provide non-invasive biomarkers for detection of DAI, which is the pathological substrate for neurological disorders ranging from concussion to severe traumatic brain injury.
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Affiliation(s)
- Dan Benjamini
- Section on Quantitative Imaging and Tissue Sciences, The Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.,Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.,The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
| | - Diego Iacono
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.,The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA.,Department of Neurology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, MD, USA.,Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, MD, USA.,Neuroscience Graduate Program, Department of Anatomy, Physiology, and Genetics, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, MD, USA.,Motor Neuron Disorders Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Michal E Komlosh
- Section on Quantitative Imaging and Tissue Sciences, The Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.,Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.,The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, MD, USA
| | - Daniel P Perl
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.,Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, MD, USA
| | - David L Brody
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.,Department of Neurology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, MD, USA.,Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Peter J Basser
- Section on Quantitative Imaging and Tissue Sciences, The Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.,Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
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17
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Avram AV, Sarlls JE, Basser PJ. Whole-Brain Imaging of Subvoxel T1-Diffusion Correlation Spectra in Human Subjects. Front Neurosci 2021; 15:671465. [PMID: 34177451 PMCID: PMC8232058 DOI: 10.3389/fnins.2021.671465] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 05/14/2021] [Indexed: 12/12/2022] Open
Abstract
T1 relaxation and water mobility generate eloquent MRI tissue contrasts with great diagnostic value in many neuroradiological applications. However, conventional methods do not adequately quantify the microscopic heterogeneity of these important biophysical properties within a voxel, and therefore have limited biological specificity. We describe a new correlation spectroscopic (CS) MRI method for measuring how T1 and mean diffusivity (MD) co-vary in microscopic tissue environments. We develop a clinical pulse sequence that combines inversion recovery (IR) with single-shot isotropic diffusion encoding (IDE) to efficiently acquire whole-brain MRIs with a wide range of joint T1-MD weightings. Unlike conventional diffusion encoding, the IDE preparation ensures that all subvoxel water pools are weighted by their MDs regardless of the sizes, shapes, and orientations of their corresponding microscopic diffusion tensors. Accordingly, IR-IDE measurements are well-suited for model-free, quantitative spectroscopic analysis of microscopic water pools. Using numerical simulations, phantom experiments, and data from healthy volunteers we demonstrate how IR-IDE MRIs can be processed to reconstruct maps of two-dimensional joint probability density functions, i.e., correlation spectra, of subvoxel T1-MD values. In vivo T1-MD spectra show distinct cerebrospinal fluid and parenchymal tissue components specific to white matter, cortical gray matter, basal ganglia, and myelinated fiber pathways, suggesting the potential for improved biological specificity. The one-dimensional marginal distributions derived from the T1-MD correlation spectra agree well with results from other relaxation spectroscopic and quantitative MRI studies, validating the T1-MD contrast encoding and the spectral reconstruction. Mapping subvoxel T1-diffusion correlations in patient populations may provide a more nuanced, comprehensive, sensitive, and specific neuroradiological assessment of the non-specific changes seen on fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted MRIs (DWIs) in cancer, ischemic stroke, or brain injury.
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Affiliation(s)
- Alexandru V Avram
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States.,Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States
| | - Joelle E Sarlls
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Peter J Basser
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
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18
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Reymbaut A, Critchley J, Durighel G, Sprenger T, Sughrue M, Bryskhe K, Topgaard D. Toward nonparametric diffusion- T1 characterization of crossing fibers in the human brain. Magn Reson Med 2021; 85:2815-2827. [PMID: 33301195 PMCID: PMC7898694 DOI: 10.1002/mrm.28604] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 10/26/2020] [Accepted: 10/27/2020] [Indexed: 12/24/2022]
Abstract
PURPOSE To estimate T 1 for each distinct fiber population within voxels containing multiple brain tissue types. METHODS A diffusion- T 1 correlation experiment was carried out in an in vivo human brain using tensor-valued diffusion encoding and multiple repetition times. The acquired data were inverted using a Monte Carlo algorithm that retrieves nonparametric distributions P ( D , R 1 ) of diffusion tensors and longitudinal relaxation rates R 1 = 1 / T 1 . Orientation distribution functions (ODFs) of the highly anisotropic components of P ( D , R 1 ) were defined to visualize orientation-specific diffusion-relaxation properties. Finally, Monte Carlo density-peak clustering (MC-DPC) was performed to quantify fiber-specific features and investigate microstructural differences between white matter fiber bundles. RESULTS Parameter maps corresponding to P ( D , R 1 ) 's statistical descriptors were obtained, exhibiting the expected R 1 contrast between brain tissue types. Our ODFs recovered local orientations consistent with the known anatomy and indicated differences in R 1 between major crossing fiber bundles. These differences, confirmed by MC-DPC, were in qualitative agreement with previous model-based works but seem biased by the limitations of our current experimental setup. CONCLUSIONS Our Monte Carlo framework enables the nonparametric estimation of fiber-specific diffusion- T 1 features, thereby showing potential for characterizing developmental or pathological changes in T 1 within a given fiber bundle, and for investigating interbundle T 1 differences.
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Affiliation(s)
- Alexis Reymbaut
- Department of Physical ChemistryLund UniversityLundSweden
- Random Walk Imaging ABLundSweden
| | | | | | - Tim Sprenger
- Karolinska InstituteStockholmSweden
- GE HealthcareStockholmSweden
| | | | | | - Daniel Topgaard
- Department of Physical ChemistryLund UniversityLundSweden
- Random Walk Imaging ABLundSweden
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19
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Slator PJ, Hutter J, Marinescu RV, Palombo M, Jackson LH, Ho A, Chappell LC, Rutherford M, Hajnal JV, Alexander DC. Data-Driven multi-Contrast spectral microstructure imaging with InSpect: INtegrated SPECTral component estimation and mapping. Med Image Anal 2021; 71:102045. [PMID: 33934005 PMCID: PMC8543043 DOI: 10.1016/j.media.2021.102045] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 02/08/2021] [Accepted: 03/16/2021] [Indexed: 11/19/2022]
Abstract
Unsupervised learning technique for spectroscopic analysis of quantitative MRI. Shares information across voxels to improve estimation of multi-dimensional or single-dimensional spectra. Spectral maps are dramatically improved compared to existing approaches. Can potentially identify and map tissue environments; in placental diffusion-relaxometry MRI we demonstrate that it identifies components that correspond to distinct tissue types.
We introduce and demonstrate an unsupervised machine learning technique for spectroscopic analysis of quantitative MRI experiments. Our algorithm supports estimation of one-dimensional spectra from single-contrast data, and multidimensional correlation spectra from simultaneous multi-contrast data. These spectrum-based approaches allow model-free investigation of tissue properties, but require regularised inversion of a Laplace transform or Fredholm integral, which is an ill-posed calculation. Here we present a method that addresses this limitation in a data-driven way. The algorithm simultaneously estimates a canonical basis of spectral components and voxelwise maps of their weightings, thereby pooling information across whole images to regularise the ill-posed problem. We show in simulations that our algorithm substantially outperforms current voxelwise spectral approaches. We demonstrate the method on multi-contrast diffusion-relaxometry placental MRI scans, revealing anatomically-relevant sub-structures, and identifying dysfunctional placentas. Our algorithm vastly reduces the data required to reliably estimate spectra, opening up the possibility of quantitative MRI spectroscopy in a wide range of new applications. Our InSpect code is available at github.com/paddyslator/inspect.
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Affiliation(s)
- Paddy J Slator
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK.
| | - Jana Hutter
- Centre for the Developing Brain, Kings College London, London, UK; Biomedical Engineering Department, Kings College London, London, UK
| | - Razvan V Marinescu
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| | - Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| | - Laurence H Jackson
- Centre for the Developing Brain, Kings College London, London, UK; Biomedical Engineering Department, Kings College London, London, UK
| | - Alison Ho
- Women's Health Department, King's College London, London, UK
| | - Lucy C Chappell
- Women's Health Department, King's College London, London, UK
| | - Mary Rutherford
- Centre for the Developing Brain, Kings College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, Kings College London, London, UK; Biomedical Engineering Department, Kings College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
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20
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Parasram T, Daoud R, Xiao D. T 2 analysis using artificial neural networks. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2021; 325:106930. [PMID: 33640586 DOI: 10.1016/j.jmr.2021.106930] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 11/11/2020] [Accepted: 01/28/2021] [Indexed: 06/12/2023]
Abstract
Quantitative analysis of magnetic resonance signal lifetimes could reveal molecular scale information. However, it is non-trivial to recover the relaxation times from MR experiments in the multi-component exponential decay analysis. Constraints are required for the ill-posed problem in conventional inversion methods, which could lead to biased solutions. Artificial neural networks (ANNs) are a series of densely connected information processing nodes which cumulatively map a set of inputs to a set of outputs. They have proven to be universal approximators and powerful tools for solving complex nonlinear problems. In this work, ANNs were trained to recover T2 relaxation times. Both the discrete T2 spectrum and continuous T2 distribution were considered. Increased accuracy was achieved compared to the traditional methods. The continuous spectrum peak widths, generally not reliable in the traditional approach, could be determined accurately with ANN when the signal-to-noise ratio permitted.
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Affiliation(s)
- Tristhal Parasram
- Department of Physics, University of Windsor, 401 Sunset Avenue, Windsor, Canada
| | - Rebecca Daoud
- Department of Physics, University of Windsor, 401 Sunset Avenue, Windsor, Canada
| | - Dan Xiao
- Department of Physics, University of Windsor, 401 Sunset Avenue, Windsor, Canada.
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21
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de Almeida Martins JP, Tax CMW, Reymbaut A, Szczepankiewicz F, Chamberland M, Jones DK, Topgaard D. Computing and visualising intra-voxel orientation-specific relaxation-diffusion features in the human brain. Hum Brain Mapp 2021; 42:310-328. [PMID: 33022844 PMCID: PMC7776010 DOI: 10.1002/hbm.25224] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 09/04/2020] [Accepted: 09/22/2020] [Indexed: 12/12/2022] Open
Abstract
Diffusion MRI techniques are used widely to study the characteristics of the human brain connectome in vivo. However, to resolve and characterise white matter (WM) fibres in heterogeneous MRI voxels remains a challenging problem typically approached with signal models that rely on prior information and constraints. We have recently introduced a 5D relaxation-diffusion correlation framework wherein multidimensional diffusion encoding strategies are used to acquire data at multiple echo-times to increase the amount of information encoded into the signal and ease the constraints needed for signal inversion. Nonparametric Monte Carlo inversion of the resulting datasets yields 5D relaxation-diffusion distributions where contributions from different sub-voxel tissue environments are separated with minimal assumptions on their microscopic properties. Here, we build on the 5D correlation approach to derive fibre-specific metrics that can be mapped throughout the imaged brain volume. Distribution components ascribed to fibrous tissues are resolved, and subsequently mapped to a dense mesh of overlapping orientation bins to define a smooth orientation distribution function (ODF). Moreover, relaxation and diffusion measures are correlated to each independent ODF coordinate, thereby allowing the estimation of orientation-specific relaxation rates and diffusivities. The proposed method is tested on a healthy volunteer, where the estimated ODFs were observed to capture major WM tracts, resolve fibre crossings, and, more importantly, inform on the relaxation and diffusion features along with distinct fibre bundles. If combined with fibre-tracking algorithms, the methodology presented in this work has potential for increasing the depth of characterisation of microstructural properties along individual WM pathways.
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Affiliation(s)
- João P. de Almeida Martins
- Division of Physical Chemistry, Department of ChemistryLund UniversityLundSweden
- Random Walk Imaging ABLundSweden
| | - Chantal M. W. Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff UniversityCardiffUK
- University Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
| | - Alexis Reymbaut
- Division of Physical Chemistry, Department of ChemistryLund UniversityLundSweden
- Random Walk Imaging ABLundSweden
| | - Filip Szczepankiewicz
- Department of Clinical SciencesLund UniversityLundSweden
- Harvard Medical SchoolBostonMassachusettsUSA
- Radiology, Brigham and Women's HospitalBostonMassachusettsUSA
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff UniversityCardiffUK
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff UniversityCardiffUK
- Mary MacKillop Institute for Health Research, Australian Catholic UniversityMelbourneAustralia
| | - Daniel Topgaard
- Division of Physical Chemistry, Department of ChemistryLund UniversityLundSweden
- Random Walk Imaging ABLundSweden
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22
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Fair MJ, Liao C, Manhard MK, Setsompop K. Diffusion-PEPTIDE: Distortion- and blurring-free diffusion imaging with self-navigated motion-correction and relaxometry capabilities. Magn Reson Med 2020; 85:2417-2433. [PMID: 33314281 DOI: 10.1002/mrm.28579] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 10/11/2020] [Accepted: 10/12/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE To implement the time-resolved relaxometry PEPTIDE technique into a diffusion acquisition to provide self-navigated, distortion- and blurring-free diffusion imaging that is robust to motion, while simultaneously providing T2 and T 2 ∗ mapping. THEORY AND METHODS The PEPTIDE readout was implemented into a spin-echo diffusion acquisition, enabling reconstruction of a time-series of T2 - and T 2 ∗ -weighted images, free from conventional echo planar imaging (EPI) distortion and blurring, for each diffusion-encoding. Robustness of PEPTIDE to motion and shot-to-shot phase variation was examined through a deliberate motion-corrupted diffusion experiment. Two diffusion-relaxometry in vivo brain protocols were also examined: (1)1 × 1 × 3 mm3 across 32 diffusion directions in 20 min, (2)1.5 × 1.5 × 3.0 mm3 across 6 diffusion-weighted images in 3.4 min. T2 , T 2 ∗ , and diffusion parameter maps were calculated from these data. As initial exploration of the rich diffusion-relaxometry data content for use in multi-compartment modeling, PEPTIDE data were acquired of a gadolinium-doped asparagus phantom. These datasets contained two compartments with different relaxation parameters and different diffusion orientation properties, and T2 relaxation variations across these diffusion directions were explored. RESULTS Diffusion-PEPTIDE showed the capability to provide high quality diffusion images and T2 and T 2 ∗ maps from both protocols. The reconstructions were distortion-free, avoided potential resolution losses exceeding 100% in equivalent EPI acquisitions, and showed tolerance to nearly 30° of rotational motion. Expected variation in T2 values as a function of diffusion direction was observed in the two-compartment asparagus phantom (P < .01), demonstrating potential to explore diffusion-PEPTIDE data for multi-compartment modeling. CONCLUSIONS Diffusion-PEPTIDE provides highly robust diffusion and relaxometry data and offers potential for future applications in diffusion-relaxometry multi-compartment modeling.
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Affiliation(s)
- Merlin J Fair
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Mary Kate Manhard
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.,Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
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23
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Zibetti MVW, Helou ES, Sharafi A, Regatte RR. Fast multicomponent 3D-T 1ρ relaxometry. NMR IN BIOMEDICINE 2020; 33:e4318. [PMID: 32359000 PMCID: PMC7606711 DOI: 10.1002/nbm.4318] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 03/10/2020] [Accepted: 04/05/2020] [Indexed: 05/06/2023]
Abstract
NMR relaxometry can provide information about the relaxation of the magnetization in different tissues, increasing our understanding of molecular dynamics and biochemical composition in biological systems. In general, tissues have complex and heterogeneous structures composed of multiple pools. As a result, bulk magnetization returns to its original state with different relaxation times, in a multicomponent relaxation. Recovering the distribution of relaxation times in each voxel is a difficult inverse problem; it is usually unstable and requires long acquisition time, especially on clinical scanners. MRI can also be viewed as an inverse problem, especially when compressed sensing (CS) is used. The solution of these two inverse problems, CS and relaxometry, can be obtained very efficiently in a synergistically combined manner, leading to a more stable multicomponent relaxometry obtained with short scan times. In this paper, we will discuss the details of this technique from the viewpoint of inverse problems.
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Affiliation(s)
- Marcelo V W Zibetti
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, US
| | - Elias S Helou
- Institute of Mathematical Sciences and Computation, University of São Paulo, São Carlos, SP, Brazil
| | - Azadeh Sharafi
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, US
| | - Ravinder R Regatte
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, US
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24
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Benjamini D, Basser PJ. Multidimensional correlation MRI. NMR IN BIOMEDICINE 2020; 33:e4226. [PMID: 31909516 PMCID: PMC11062766 DOI: 10.1002/nbm.4226] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 10/24/2019] [Accepted: 10/28/2019] [Indexed: 05/23/2023]
Abstract
Multidimensional correlation spectroscopy is emerging as a novel MRI modality that is well suited for microstructure and microdynamic imaging studies, especially of biological specimens. Conventional MRI methods only provide voxel-averaged and mostly macroscopically averaged information; these methods cannot disentangle intra-voxel heterogeneity on the basis of both water mobility and local chemical interactions. By correlating multiple MR contrast mechanisms and processing the data in an integrated manner, correlation spectroscopy is able to resolve the distribution of water populations according to their chemical and physical interactions with the environment. The use of a non-parametric, phenomenological representation of the multidimensional MR signal makes no assumptions about tissue structure, thereby allowing the study of microscopic structure and composition of complex heterogeneous biological systems. However, until recently, vast data requirements have confined these types of measurement to non-localized NMR applications and prevented them from being widely and successfully used in conjunction with imaging. Recent groundbreaking advancements have allowed this powerful NMR methodology to be migrated to MRI, initiating its emergence as a promising imaging approach. This review is not intended to cover the entire field of multidimensional MR; instead, it focuses on pioneering imaging applications and the challenges involved. In addition, the background and motivation that have led to multidimensional correlation MR development are discussed, along with the basic underlying mathematical concepts. The goal of the present work is to provide the reader with a fundamental understanding of the techniques developed and their potential benefits, and to provide guidance to help refine future applications of this technology.
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Affiliation(s)
- Dan Benjamini
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation, Bethesda, MD, USA
| | - Peter J. Basser
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
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25
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Kim D, Wisnowski JL, Nguyen CT, Haldar JP. Multidimensional correlation spectroscopic imaging of exponential decays: From theoretical principles to in vivo human applications. NMR IN BIOMEDICINE 2020; 33:e4244. [PMID: 31909534 PMCID: PMC7338241 DOI: 10.1002/nbm.4244] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 10/09/2019] [Accepted: 11/27/2019] [Indexed: 05/02/2023]
Abstract
Multiexponential modeling of relaxation or diffusion MR signal decays is a popular approach for estimating and spatially mapping different microstructural tissue compartments. While this approach can be quite powerful, it is also limited by the fact that one-dimensional multiexponential modeling is an ill-posed inverse problem with substantial ambiguities. In this article, we present an overview of a recent multidimensional correlation spectroscopic imaging approach to this problem. This approach helps to alleviate ill-posedness by making advantageous use of multidimensional contrast encoding (e.g., 2D diffusion-relaxation encoding or 2D relaxation-relaxation encoding) combined with a regularized spatial-spectral estimation procedure. Theoretical calculations, simulations, and experimental results are used to illustrate the benefits of this approach relative to classical methods. In addition, we demonstrate an initial proof-of-principle application of this kind of approach to in vivo human MRI experiments.
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Affiliation(s)
- Daeun Kim
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, CA, USA
- Signal and Image Processing Institute, University of Southern California, CA, USA
- Correspondence Daeun Kim,
| | - Jessica L. Wisnowski
- Radiology, Children’s Hospital Los Angeles, CA, USA
- Pediatrics, Children’s Hospital Los Angeles, CA, USA
| | - Christopher T. Nguyen
- Harvard Medical School and Cardiovascular Research Center, Massachusetts General Hospital, MA, USA
- Martinos Center for Biomedical Imaging, Radiology, Massachusetts General Hospital, MA, USA
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, CA, USA
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, CA, USA
- Signal and Image Processing Institute, University of Southern California, CA, USA
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26
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Benjamini D, Hutchinson EB, Komlosh ME, Comrie CJ, Schwerin SC, Zhang G, Pierpaoli C, Basser PJ. Direct and specific assessment of axonal injury and spinal cord microenvironments using diffusion correlation imaging. Neuroimage 2020; 221:117195. [PMID: 32726643 PMCID: PMC7805019 DOI: 10.1016/j.neuroimage.2020.117195] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 07/17/2020] [Accepted: 07/21/2020] [Indexed: 12/17/2022] Open
Abstract
We describe a practical two-dimensional (2D) diffusion MRI framework to deliver specificity and improve sensitivity to axonal injury in the spinal cord. This approach provides intravoxel distributions of correlations of water mobilities in orthogonal directions, revealing sub-voxel diffusion components. Here we use it to investigate water diffusivities along axial and radial orientations within spinal cord specimens with confirmed, tract-specific axonal injury. First, we show using transmission electron microscopy and immunohistochemistry that tract-specific axonal beading occurs following Wallerian degeneration in the cortico-spinal tract as direct sequelae to closed head injury. We demonstrate that although some voxel-averaged diffusion tensor imaging (DTI) metrics are sensitive to this axonal injury, they are non-specific, i.e., they do not reveal an underlying biophysical mechanism of injury. Then we employ 2D diffusion correlation imaging (DCI) to improve discrimination of different water microenvironments by measuring and mapping the joint water mobility distributions perpendicular and parallel to the spinal cord axis. We determine six distinct diffusion spectral components that differ according to their microscopic anisotropy and mobility. We show that at the injury site a highly anisotropic diffusion component completely disappears and instead becomes more isotropic. Based on these findings, an injury-specific MR image of the spinal cord was generated, and a radiological-pathological correlation with histological silver staining % area was performed. The resulting strong and significant correlation (r=0.70,p < 0.0001) indicates the high specificity with which DCI detects injury-induced tissue alterations. We predict that the ability to selectively image microstructural changes following axonal injury in the spinal cord can be useful in clinical and research applications by enabling specific detection and increased sensitivity to injury-induced microstructural alterations. These results also encourage us to translate DCI to higher spatial dimensions to enable assessment of traumatic axonal injury, and possibly other diseases and disorders in the brain.
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Affiliation(s)
- Dan Benjamini
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD 20817, USA; The Center for Neuroscience and Regenerative Medicine, Uniformed Service University of the Health Sciences, Bethesda, MD 20814, USA.
| | - Elizabeth B Hutchinson
- The Department of Biomedical Engineering, The University of Arizona, Tucson, Arizona 85721, USA
| | - Michal E Komlosh
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD 20817, USA; The Center for Neuroscience and Regenerative Medicine, Uniformed Service University of the Health Sciences, Bethesda, MD 20814, USA
| | - Courtney J Comrie
- The Department of Biomedical Engineering, The University of Arizona, Tucson, Arizona 85721, USA
| | - Susan C Schwerin
- The Center for Neuroscience and Regenerative Medicine, Uniformed Service University of the Health Sciences, Bethesda, MD 20814, USA; Department of Anatomy, Physiology, and Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
| | - Guofeng Zhang
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD 20817, USA
| | - Carlo Pierpaoli
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD 20817, USA
| | - Peter J Basser
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD 20817, USA
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27
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Novikov DS. The present and the future of microstructure MRI: From a paradigm shift to normal science. J Neurosci Methods 2020; 351:108947. [PMID: 33096152 DOI: 10.1016/j.jneumeth.2020.108947] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/29/2020] [Accepted: 09/10/2020] [Indexed: 12/29/2022]
Abstract
The aspiration of imaging tissue microstructure with MRI is to uncover micrometer-scale tissue features within millimeter-scale imaging voxels, in vivo. This kind of super-resolution has fueled a paradigm shift within the biomedical imaging community. However, what feels like an ongoing revolution in MRI, has been conceptually experienced in physics decades ago; from this point of view, our current developments can be seen as Thomas Kuhn's "normal science" stage of progress. While the concept of model-based quantification below the nominal imaging resolution is not new, its possibilities in neuroscience and neuroradiology are only beginning to be widely appreciated. This disconnect calls for communicating the progress of tissue microstructure MR imaging to its potential users. Here, a number of recent research developments are outlined in terms of the overarching concept of coarse-graining the tissue structure over an increasing diffusion length. A variety of diffusion models and phenomena are summarized on the phase diagram of diffusion MRI, with the unresolved problems and future directions corresponding to its unexplored domains.
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Affiliation(s)
- Dmitry S Novikov
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA.
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28
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Jelescu IO, Palombo M, Bagnato F, Schilling KG. Challenges for biophysical modeling of microstructure. J Neurosci Methods 2020; 344:108861. [PMID: 32692999 PMCID: PMC10163379 DOI: 10.1016/j.jneumeth.2020.108861] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/10/2020] [Accepted: 07/14/2020] [Indexed: 02/07/2023]
Abstract
The biophysical modeling efforts in diffusion MRI have grown considerably over the past 25 years. In this review, we dwell on the various challenges along the journey of bringing a biophysical model from initial design to clinical implementation, identifying both hurdles that have been already overcome and outstanding issues. First, we describe the critical initial task of selecting which features of tissue microstructure can be estimated using a model and which acquisition protocol needs to be implemented to make the estimation possible. The model performance should necessarily be tested in realistic numerical simulations and in experimental data - adapting the fitting strategy accordingly, and parameter estimates should be validated against complementary techniques, when/if available. Secondly, the model performance and validity should be explored in pathological conditions, and, if appropriate, dedicated models for pathology should be developed. We build on examples from tumors, ischemia and demyelinating diseases. We then discuss the challenges associated with clinical translation and added value. Finally, we single out four major unresolved challenges that are related to: the availability of a microstructural ground truth, the validation of model parameters which cannot be accessed with complementary techniques, the development of a generalized standard model for any brain region and pathology, and the seamless communication between different parties involved in the development and application of biophysical models of diffusion.
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29
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Williamson NH, Ravin R, Cai TX, Benjamini D, Falgairolle M, O'Donovan MJ, Basser PJ. Real-time measurement of diffusion exchange rate in biological tissue. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2020; 317:106782. [PMID: 32679514 PMCID: PMC7427561 DOI: 10.1016/j.jmr.2020.106782] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 06/22/2020] [Accepted: 06/26/2020] [Indexed: 05/05/2023]
Abstract
Diffusion exchange spectroscopy (DEXSY) provides a means to isolate the signal attenuation associated with exchange from other sources of signal loss. With the total diffusion weighting b1+b2=bs held constant, DEXSY signals acquired with b1=0 or b2=0 have no exchange weighting, while a DEXSY signal acquired with b1=b2 has maximal exchange weighting. The exchange rate can be estimated by fitting a diffusion exchange model to signals acquired with variable mixing times. Conventionally, acquired signals are normalized by a signal with b1=0 and b2=0 to remove the decay due to spin-lattice relaxation. Instead, division by a signal with equal bs but b1=0 or b2=0 reduces spin-lattice relaxation weighting of the apparent exchange rate (AXR). Furthermore, apparent diffusion-weighted R1 relaxation rates can be estimated from non-exchange-weighted DEXSY signals. Estimated R1 values are utilized to remove signal decay due to spin-lattice relaxation from exchange-weighted signals, permitting a more precise estimate of AXR with less data. Data reduction methods are proposed and tested with regards to statistical accuracy and precision of AXR estimates on simulated and experimental data. Simulations show that the methods are capable of accurately measuring the ground-truth exchange rate. The methods remain accurate even when the assumption that DEXSY signals attenuate with b is violated, as occurs for restricted diffusion. Experimental data was collected from fixed neonatal mouse spinal cord samples at 25 and 7°C using the strong static magnetic field gradient produced by a single-sided permanent magnet (i.e., an NMR MOUSE). The most rapid method for exchange measurements requires only five data points (an 80 s experiment as implemented) and achieves a similar level of accuracy and precision to the baseline method using 44 data points. This represents a significant improvement in acquisition speed, overcoming a barrier which has limited the use of DEXSY on living specimen.
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Affiliation(s)
- Nathan H Williamson
- National Institute of General Medical Sciences, National Institutes of Health, Bethesda, MD, USA; Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
| | - Rea Ravin
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA; Celoptics, Rockville, MD, USA
| | - Teddy X Cai
- 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; The Center for Neuroscience and Regenerative Medicine, Uniformed Service University of the Health Sciences, Bethesda, MD 20814, USA
| | - Melanie Falgairolle
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Michael J O'Donovan
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Peter J Basser
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
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30
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Zhang Z, Wu HH, Priester A, Magyar C, Afshari Mirak S, Shakeri S, Mohammadian Bajgiran A, Hosseiny M, Azadikhah A, Sung K, Reiter RE, Sisk AE, Raman S, Enzmann DR. Prostate Microstructure in Prostate Cancer Using 3-T MRI with Diffusion-Relaxation Correlation Spectrum Imaging: Validation with Whole-Mount Digital Histopathology. Radiology 2020; 296:348-355. [PMID: 32515678 DOI: 10.1148/radiol.2020192330] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background Microstructural MRI has the potential to improve diagnosis and characterization of prostate cancer (PCa), but validation with histopathology is lacking. Purpose To validate ex vivo diffusion-relaxation correlation spectrum imaging (DR-CSI) in the characterization of microstructural tissue compartments in prostate specimens from men with PCa by using registered whole-mount digital histopathology (WMHP) as the reference standard. Materials and Methods Men with PCa who underwent 3-T MRI and robotic-assisted radical prostatectomy between June 2018 and January 2019 were prospectively studied. After prostatectomy, the fresh whole prostate specimens were imaged in patient-specific three-dimensionally printed molds by using 3-T MRI with DR-CSI and were then sliced to create coregistered WMHP slides. The DR-CSI spectral signal component fractions (fA, fB, fC) were compared with epithelial, stromal, and luminal area fractions (fepithelium, fstroma, flumen) quantified in PCa and benign tissue regions. A linear mixed-effects model assessed the correlations between (fA, fB, fC) and (fepithelium, fstroma, flumen), and the strength of correlations was evaluated by using Spearman correlation coefficients. Differences between PCa and benign tissues in terms of DR-CSI signal components and microscopic tissue compartments were assessed using two-sided t tests. Results Prostate specimens from nine men (mean age, 65 years ± 7 [standard deviation]) were evaluated; 20 regions from 17 PCas, along with 20 benign tissue regions of interest, were analyzed. Three DR-CSI spectral signal components (spectral peaks) were consistently identified. The fA, fB, and fC were correlated with fepithelium, fstroma, and flumen (all P < .001), with Spearman correlation coefficients of 0.74 (95% confidence interval [CI]: 0.62, 0.83), 0.80 (95% CI: 0.66, 0.89), and 0.67 (95% CI: 0.51, 0.81), respectively. PCa exhibited differences compared with benign tissues in terms of increased fA (PCa vs benign, 0.37 ± 0.05 vs 0.27 ± 0.06; P < .001), decreased fC (PCa vs benign, 0.18 ± 0.06 vs 0.31 ± 0.13; P = .01), increased fepithelium (PCa vs benign, 0.44 ± 0.13 vs 0.26 ± 0.16; P < .001), and decreased flumen (PCa vs benign, 0.14 ± 0.08 vs 0.27 ± 0.18; P = .004). Conclusion Diffusion-relaxation correlation spectrum imaging signal components correlate with microscopic tissue compartments in the prostate and differ between cancer and benign tissue. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Lee and Hectors in this issue.
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Affiliation(s)
- Zhaohuan Zhang
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Holden H Wu
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Alan Priester
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Clara Magyar
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Sohrab Afshari Mirak
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Sepideh Shakeri
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Amirhossein Mohammadian Bajgiran
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Melina Hosseiny
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Afshin Azadikhah
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Kyunghyun Sung
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Robert E Reiter
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Anthony E Sisk
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Steven Raman
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Dieter R Enzmann
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
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Gong T, Tong Q, He H, Sun Y, Zhong J, Zhang H. MTE-NODDI: Multi-TE NODDI for disentangling non-T2-weighted signal fractions from compartment-specific T2 relaxation times. Neuroimage 2020; 217:116906. [PMID: 32387626 DOI: 10.1016/j.neuroimage.2020.116906] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 05/01/2020] [Accepted: 05/03/2020] [Indexed: 12/28/2022] Open
Abstract
Neurite orientation dispersion and density imaging (NODDI) has become a popular diffusion MRI technique for investigating microstructural alternations during brain development, maturation and aging in health and disease. However, the NODDI model of diffusion does not explicitly account for compartment-specific T2 relaxation and its model parameters are usually estimated from data acquired with a single echo time (TE). Thus, the NODDI-derived measures, such as the intra-neurite signal fraction, also known as the neurite density index, could be T2-weighted and TE-dependent. This may confound the interpretation of studies as one cannot disentangle differences in diffusion from those in T2 relaxation. To address this challenge, we propose a multi-TE NODDI (MTE-NODDI) technique, inspired by recent studies exploiting the synergy between diffusion and T2 relaxation. MTE-NODDI could give robust estimates of the non-T2-weighted signal fractions and compartment-specific T2 values, as demonstrated by both simulation and in vivo data experiments. Results showed that the estimated non-T2 weighted intra-neurite fraction and compartment-specific T2 values in white matter were consistent with previous studies. The T2-weighted intra-neurite fractions from the original NODDI were found to be overestimated compared to their non-T2-weighted estimates; the overestimation increases with TE, consistent with the reported intra-neurite T2 being larger than extra-neurite T2. Finally, the inclusion of the free water compartment reduces the estimation error in intra-neurite T2 in the presence of cerebrospinal fluid contamination. With the ability to disentangle non-T2-weighted signal fractions from compartment-specific T2 relaxation, MTE-NODDI could help improve the interpretability of future neuroimaging studies, especially those in brain development, maturation and aging.
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Affiliation(s)
- Ting Gong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China; Department of Computer Science & Centre for Medical Image Computing, University College London, UK
| | - Qiqi Tong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China.
| | - Yi Sun
- MR Collaboration, Siemens Healthcare, Shanghai, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China; Department of Imaging Sciences, University of Rochester, Rochester, NY, United States.
| | - Hui Zhang
- Department of Computer Science & Centre for Medical Image Computing, University College London, UK
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32
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Shtangel O, Mezer AA. A phantom system for assessing the effects of membrane lipids on water proton relaxation. NMR IN BIOMEDICINE 2020; 33:e4209. [PMID: 31899589 DOI: 10.1002/nbm.4209] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 09/24/2019] [Accepted: 10/13/2019] [Indexed: 06/10/2023]
Abstract
Quantitative MRI (qMRI) is a method for the non-invasive study of brain-structure-associated changes expressed with measurable units. The qMRI-derived parameters have been shown to reflect brain tissue composition such as myelin content. Nevertheless, it remains a major challenge to identify and quantify the contributions of specific molecular components to the MRI signal. Here, we describe a phantom system that can be used to evaluate the contribution of membrane lipids to qMRI-derived parameters. We used a hydration-dehydration dry film technique to formulate liposomes that can be used as a model of the bilayer lipid membrane. The liposomes were comprised of the most abundant types of lipid found in the human brain. We then applied clinically available qMRI techniques with adjusted bias corrections in order to test the ability of the phantom system to estimate multiple qMRI parameters such as proton density (PD), T1 , T2 , T2 * and magnetization transfer. In addition, we accurately measured the phantom sample water fraction (normalized PD). A similar protocol was also applied to the human brain in vivo. The phantom system allows for a reliable estimation of qMRI parameters for phantoms composed of various lipid types using a clinical MRI scanner. We also found a comparable reproducibility between the phantom and in vivo human brain qMRI estimations. To conclude, we have successfully created a biologically relevant liposome phantom system whose lipid composition can be fully controlled. Our lipid system and analysis can be used to measure the contributions to qMRI parameters of membrane lipids found in the human brain under scanning conditions that are relevant to in vivo human brain scans. Such a model system can be used to test the contributions of lipidomic changes in normal and pathological brain states.
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Affiliation(s)
- Oshrat Shtangel
- Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Israel
| | - Aviv A Mezer
- Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Israel
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33
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Fricke SN, Seymour JD, Battistel MD, Freedberg DI, Eads CD, Augustine MP. Data processing in NMR relaxometry using the matrix pencil. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2020; 313:106704. [PMID: 32179433 DOI: 10.1016/j.jmr.2020.106704] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 02/12/2020] [Accepted: 02/28/2020] [Indexed: 06/10/2023]
Abstract
The matrix pencil method (MPM) is explored for stable, reproducible data processing in nuclear magnetic resonance (NMR) relaxometry. Data from one-dimensional and two-dimensional relaxometry experiments designed to measure transverse relaxation T2, longitudinal relaxation T1, diffusion coefficient D values, and their correlations in a standard olive oil/water mixture serve as a platform available to any NMR spectroscopist to compare the performance of the MPM to the benchmark inverse Laplace transform (ILT). The data from two practical examples, including the drying of a solvent polymer system and the enzymatic digestion of polysialic acid, were also explored with the MPM and ILT. In the cases considered here, the MPM appears to outperform the ILT in terms of resolution and stability in the determination of fundamental constants for complex materials and mixtures.
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Affiliation(s)
- S N Fricke
- Department of Chemistry, 69 Chemistry Building, University of California, Davis, CA 95616, USA
| | - J D Seymour
- Department of Chemical and Biological Engineering, 306 Cobleigh Hall, Montana State University, Bozeman, MT 59717, USA
| | - M D Battistel
- Laboratory of Bacterial Polysaccharides, Center for Biologics Evaluation and Research, United States Food and Drug Administration, 10903 New Hampshire Ave., Silver Spring, MD 20993, USA
| | - D I Freedberg
- Laboratory of Bacterial Polysaccharides, Center for Biologics Evaluation and Research, United States Food and Drug Administration, 10903 New Hampshire Ave., Silver Spring, MD 20993, USA
| | - C D Eads
- The Procter & Gamble Company, 8700 S. Mason-Montgomery Road, Mason, OH 45040, USA
| | - M P Augustine
- Department of Chemistry, 69 Chemistry Building, University of California, Davis, CA 95616, USA.
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34
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de Almeida Martins J, Tax C, Szczepankiewicz F, Jones D, Westin CF, Topgaard D. Transferring principles of solid-state and Laplace NMR to the field of in vivo brain MRI. MAGNETIC RESONANCE (GOTTINGEN, GERMANY) 2020; 1:27-43. [PMID: 37904884 PMCID: PMC10500744 DOI: 10.5194/mr-1-27-2020] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 02/18/2020] [Indexed: 11/01/2023]
Abstract
Magnetic resonance imaging (MRI) is the primary method for noninvasive investigations of the human brain in health, disease, and development but yields data that are difficult to interpret whenever the millimeter-scale voxels contain multiple microscopic tissue environments with different chemical and structural properties. We propose a novel MRI framework to quantify the microscopic heterogeneity of the living human brain as spatially resolved five-dimensional relaxation-diffusion distributions by augmenting a conventional diffusion-weighted imaging sequence with signal encoding principles from multidimensional solid-state nuclear magnetic resonance (NMR) spectroscopy, relaxation-diffusion correlation methods from Laplace NMR of porous media, and Monte Carlo data inversion. The high dimensionality of the distribution space allows resolution of multiple microscopic environments within each heterogeneous voxel as well as their individual characterization with novel statistical measures that combine the chemical sensitivity of the relaxation rates with the link between microstructure and the anisotropic diffusivity of tissue water. The proposed framework is demonstrated on a healthy volunteer using both exhaustive and clinically viable acquisition protocols.
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Affiliation(s)
- João P. de Almeida Martins
- Division of Physical Chemistry, Department of Chemistry, Lund
University, Lund, Sweden
- Random Walk Imaging AB, Lund, Sweden
| | - Chantal M. W. Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff
University, Cardiff, UK
| | - Filip Szczepankiewicz
- Harvard Medical School, Boston, MA, USA
- Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff
University, Cardiff, UK
- Mary MacKillop Institute for Health Research, Australian Catholic
University, Melbourne, Australia
| | - Carl-Fredrik Westin
- Harvard Medical School, Boston, MA, USA
- Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Daniel Topgaard
- Division of Physical Chemistry, Department of Chemistry, Lund
University, Lund, Sweden
- Random Walk Imaging AB, Lund, Sweden
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35
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Pas K, Komlosh ME, Perl DP, Basser PJ, Benjamini D. Retaining information from multidimensional correlation MRI using a spectral regions of interest generator. Sci Rep 2020; 10:3246. [PMID: 32094400 PMCID: PMC7040019 DOI: 10.1038/s41598-020-60092-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Accepted: 02/07/2020] [Indexed: 11/09/2022] Open
Abstract
Multidimensional correlation magnetic resonance imaging (MRI) is an emerging imaging modality that is capable of disentangling highly heterogeneous and opaque systems according to chemical and physical interactions of water within them. Using this approach, the conventional three dimensional MR scalar images are replaced with spatially resolved multidimensional spectra. The ensuing abundance in microstructural and chemical information is a blessing that incorporates a real challenge: how does one distill and refine it into images while retaining its significant components? In this paper we introduce a general framework that preserves the spectral information from spatially resolved multidimensional data. Equal weight is given to significant spectral components at the single voxel level, resulting in a summarized image spectrum. This spectrum is then used to define spectral regions of interest that are utilized to reconstruct images of sub-voxel components. Using numerical simulations we first show that, contrary to the conventional approach, the proposed framework preserves spectral resolution, and in turn, sensitivity and specificity of the reconstructed images. The retained spectral resolution allows, for the first time, to observe an array of distinct [Formula: see text]-[Formula: see text]-[Formula: see text] components images of the human brain. The robustly generated images of sub-voxel components overcome the limited spatial resolution of MRI, thus advancing multidimensional correlation MRI to fulfilling its full potential.
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Affiliation(s)
- Kristofor Pas
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, 20817, USA
- The Department of Biomedical Engineering, University of Texas at Arlington, Arlington, TX, 76010, USA
| | - Michal E Komlosh
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20817, USA
- The Center for Neuroscience and Regenerative Medicine, Uniformed Service University of the Health Sciences, Bethesda, MD, 20814, USA
| | - Daniel P Perl
- The Center for Neuroscience and Regenerative Medicine, Uniformed Service University of the Health Sciences, Bethesda, MD, 20814, USA
| | - Peter J Basser
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20817, USA
| | - Dan Benjamini
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20817, USA.
- The Center for Neuroscience and Regenerative Medicine, Uniformed Service University of the Health Sciences, Bethesda, MD, 20814, USA.
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36
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Williamson NH, Ravin R, Benjamini D, Merkle H, Falgairolle M, O'Donovan MJ, Blivis D, Ide D, Cai TX, Ghorashi NS, Bai R, Basser PJ. Magnetic resonance measurements of cellular and sub-cellular membrane structures in live and fixed neural tissue. eLife 2019; 8:51101. [PMID: 31829935 PMCID: PMC6977971 DOI: 10.7554/elife.51101] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 12/11/2019] [Indexed: 12/21/2022] Open
Abstract
We develop magnetic resonance (MR) methods for real-time measurement of tissue microstructure and membrane permeability of live and fixed excised neonatal mouse spinal cords. Diffusion and exchange MR measurements are performed using the strong static gradient produced by a single-sided permanent magnet. Using tissue delipidation methods, we show that water diffusion is restricted solely by lipid membranes. Most of the diffusion signal can be assigned to water in tissue which is far from membranes. The remaining 25% can be assigned to water restricted on length scales of roughly a micron or less, near or within membrane structures at the cellular, organelle, and vesicle levels. Diffusion exchange spectroscopy measures water exchanging between membrane structures and free environments at 100 s-1.
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Affiliation(s)
- Nathan H Williamson
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, United States
| | - Rea Ravin
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, United States.,Celoptics, Rockville, United States
| | - Dan Benjamini
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, United States.,Center for Neuroscience and Regenerative Medicine, Henry Jackson Foundation, Bethesda, United States
| | - Hellmut Merkle
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
| | - Melanie Falgairolle
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
| | - Michael James O'Donovan
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
| | - Dvir Blivis
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States
| | - Dave Ide
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States.,National Institute of Mental Health, National Institutes of Health, Bethesda, United States
| | - Teddy X Cai
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, United States
| | - Nima S Ghorashi
- Cardiovascular Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, United States
| | - Ruiliang Bai
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, United States.,Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
| | - Peter J Basser
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, United States
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37
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Slator PJ, Hutter J, Palombo M, Jackson LH, Ho A, Panagiotaki E, Chappell LC, Rutherford MA, Hajnal JV, Alexander DC. Combined diffusion-relaxometry MRI to identify dysfunction in the human placenta. Magn Reson Med 2019; 82:95-106. [PMID: 30883915 PMCID: PMC6519240 DOI: 10.1002/mrm.27733] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 01/04/2019] [Accepted: 01/27/2019] [Indexed: 12/25/2022]
Abstract
PURPOSE A combined diffusion-relaxometry MR acquisition and analysis pipeline for in vivo human placenta, which allows for exploration of coupling between T 2 * and apparent diffusion coefficient (ADC) measurements in a sub 10-minute scan time. METHODS We present a novel acquisition combining a diffusion prepared spin echo with subsequent gradient echoes. The placentas of 17 pregnant women were scanned in vivo, including both healthy controls and participants with various pregnancy complications. We estimate the joint T 2 * -ADC spectra using an inverse Laplace transform. RESULTS T 2 * -ADC spectra demonstrate clear quantitative separation between normal and dysfunctional placentas. CONCLUSIONS Combined T 2 * -diffusivity MRI is promising for assessing fetal and maternal health during pregnancy. The T 2 * -ADC spectrum potentially provides additional information on tissue microstructure, compared to measuring these two contrasts separately. The presented method is immediately applicable to the study of other organs.
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Affiliation(s)
- Paddy J. Slator
- Centre for Medical Image Computing and Department of Computer ScienceUniversity College LondonLondonUnited Kingdom
| | - Jana Hutter
- Biomedical Engineering DepartmentKing’s College LondonLondonUnited Kingdom
- Centre for the Developing BrainKing’s College LondonLondonUnited Kingdom
| | - Marco Palombo
- Centre for Medical Image Computing and Department of Computer ScienceUniversity College LondonLondonUnited Kingdom
| | - Laurence H. Jackson
- Biomedical Engineering DepartmentKing’s College LondonLondonUnited Kingdom
- Centre for the Developing BrainKing’s College LondonLondonUnited Kingdom
| | - Alison Ho
- Women’s Health DepartmentKing’s College LondonLondonUnited Kingdom
| | - Eleftheria Panagiotaki
- Centre for Medical Image Computing and Department of Computer ScienceUniversity College LondonLondonUnited Kingdom
| | - Lucy C. Chappell
- Women’s Health DepartmentKing’s College LondonLondonUnited Kingdom
| | - Mary A. Rutherford
- Centre for the Developing BrainKing’s College LondonLondonUnited Kingdom
| | - Joseph V. Hajnal
- Biomedical Engineering DepartmentKing’s College LondonLondonUnited Kingdom
- Centre for the Developing BrainKing’s College LondonLondonUnited Kingdom
| | - Daniel C. Alexander
- Centre for Medical Image Computing and Department of Computer ScienceUniversity College LondonLondonUnited Kingdom
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Liu C, Özarslan E. Multimodal integration of diffusion MRI for better characterization of tissue biology. NMR IN BIOMEDICINE 2019; 32:e3939. [PMID: 30011138 DOI: 10.1002/nbm.3939] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 04/01/2018] [Accepted: 04/09/2018] [Indexed: 06/08/2023]
Abstract
The contrast in diffusion-weighted MR images is due to variations of diffusion properties within the examined specimen. Certain microstructural information on the underlying tissues can be inferred through quantitative analyses of the diffusion-sensitized MR signals. In the first part of the paper, we review two types of approach for characterizing diffusion MRI signals: Bloch's equations with diffusion terms, and statistical descriptions. Specifically, we discuss expansions in terms of cumulants and orthogonal basis functions, the confinement tensor formalism and tensor distribution models. Further insights into the tissue properties may be obtained by integrating diffusion MRI with other techniques, which is the subject of the second part of the paper. We review examples involving magnetic susceptibility, structural tensors, internal field gradients, transverse relaxation and functional MRI. Integrating information provided by other imaging modalities (MR based or otherwise) could be a key to improve our understanding of how diffusion MRI relates to physiology and biology.
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Affiliation(s)
- Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Evren Özarslan
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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Benjamini D, Komlosh ME, Williamson NH, Basser PJ. Generalized Mean Apparent Propagator MRI to Measure and Image Advective and Dispersive Flows in Medicine and Biology. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:11-20. [PMID: 30010549 PMCID: PMC6345276 DOI: 10.1109/tmi.2018.2852259] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Water transport in biological systems spans different regimes with distinct physical behaviors: diffusion, advection, and dispersion. Identifying these regimes is of paramount importance in many in vivo applications, among them, measuring microcirculation of blood in capillary networks and cerebrospinal fluid transport in the glymphatic system. Diffusion magnetic resonance imaging (dMRI) can be used to encode water displacements, and a Fourier transform of the acquired signal furnishes a displacement probability density function known as the propagator. This transformation normally requires the use of a fast Fourier transform (FFT), which presents major feasibility challenges when scanning in vivo, mainly because of dense signal sampling, resulting in long acquisition times. A second approach to reconstruct the propagator is by using analytical representation of the signal, overcoming many of the FFT's limitations. In all analytical implementations of dMRI to date, the translational motion of water has been assumed to be exclusively diffusive, which is the case only in the absence of flow. However, retaining the phase information from the diffusion signal provides the ability to measure both mean coherent velocity and random diffusion from a single experiment. We implement and extend an analytical framework, mean apparent propagator (MAP), which can account for non-zero flow conditions. We call this method generalized MAP or GMAP. We describe a numerical optimization scheme and implement it on data from an MRI flow phantom constructed from a pack of 10- [Formula: see text] beads. The advantages of GMAP over the FFT-based method in the context of sampling density and low-flow detection were demonstrated, and analytically derived propagator moments were shown to agree with theoretical values even after data subsampling. GMAP would enable the detection of microflow in vivo that could help elucidate many important biological processes.
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40
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Slator PJ, Hutter J, Marinescu RV, Palombo M, Young AL, Jackson LH, Ho A, Chappell LC, Rutherford M, Hajnal JV, Alexander DC. InSpect: INtegrated SPECTral Component Estimation and Mapping for Multi-contrast Microstructural MRI. LECTURE NOTES IN COMPUTER SCIENCE 2019. [DOI: 10.1007/978-3-030-20351-1_59] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Water mobility spectral imaging of the spinal cord: Parametrization of model-free Laplace MRI. Magn Reson Imaging 2018; 56:187-193. [PMID: 30584915 DOI: 10.1016/j.mri.2018.12.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Revised: 12/06/2018] [Accepted: 12/06/2018] [Indexed: 11/22/2022]
Abstract
Diffusion magnetic resonance imaging (dMRI) of biological systems most often results in non-monoexponential signal, due to their complexity and heterogeneity. One approach to interpreting dMRI data without imposing tissue microstructural models is to fit the signal to a multiexponential function, which is sometimes referred to as an inverse Laplace transformation, and to display the coefficients as a distribution of the diffusivities, or water mobility spectra. Until recently, this method has not been used in a voxelwise manner, mainly because of heavy data requirements. With recent advancements in processing and experimental design, voxelwise Laplace MRI approaches are becoming feasible and attractive. The rich spectral information, combined with a three-dimensional image, presents a challenge because it tremendously increases the dimensionality of the data and requires a robust method for interpretation and analysis. In this work, we suggest parameterizing the empirically measured water mobility spectra using a bimodal lognormal function. This approach allows for a compact representation of the spectrum, and it also resolves overlapping spectral peaks, which allows for a robust extraction of their signal fraction. We apply the method on a fixed spinal cord sample and use it to generate robust intensity images of slow- and fast-diffusion components. Using the parametric variables, we create novel image contrasts, among them the information entropy of the water mobility spectrum, which pack unique features of the individual diffusion regimes in the investigated system.
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42
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Nachmani A, Schurr R, Joskowicz L, Mezer AA. The effect of motion correction interpolation on quantitative T1 mapping with MRI. Med Image Anal 2018; 52:119-127. [PMID: 30529225 DOI: 10.1016/j.media.2018.11.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2018] [Revised: 11/29/2018] [Accepted: 11/30/2018] [Indexed: 11/16/2022]
Abstract
Quantitative magnetic resonance imaging (qMRI) is a technique for mapping the physical properties of the underlying tissue using several MR images with different contrasts. To overcome subject motion between the acquired images, it is necessary to register the images to a common reference frame. A drawback of registration is the use of interpolation and resampling techniques, which can introduce artifacts into the interpolated data. These artifacts could have unfavorable effects on the accuracy of the estimated tissue's physical properties. Here, we quantified the error of interpolation and resampling on T1-weighted images and studied its effects on the mapping of the longitudinal relaxation time (T1) using variable flip angles. We simulated T1-weighted images and calculated the transformation error resulting from interpolation and resampling. We found that the error is a function of the image contrast (i.e., flip angle) and of the translation and rotation of the image. Furthermore, we found that the error in the T1-weighted images has a substantial effect on the T1 estimation, of the order of 10% of the signal in the brain's gray and white matter. Hence, minimizing the registration error can enable more accurate in vivo modeling of brain microstructure.
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Affiliation(s)
- Amitay Nachmani
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Israel; The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel
| | - Roey Schurr
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Israel
| | - Leo Joskowicz
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Israel; The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel
| | - Aviv A Mezer
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Israel.
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Cai TX, Benjamini D, Komlosh ME, Basser PJ, Williamson NH. Rapid detection of the presence of diffusion exchange. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2018; 297:17-22. [PMID: 30340203 PMCID: PMC6289744 DOI: 10.1016/j.jmr.2018.10.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 10/07/2018] [Accepted: 10/08/2018] [Indexed: 05/08/2023]
Abstract
Diffusion exchange spectroscopy (DEXSY) provides a detailed picture of how fluids in different microenvironments communicate with one another but requires a large amount of data. For DEXSY MRI, a simple measure of apparent exchanging fractions may suffice to characterize and differentiate materials and tissues. Reparameterizing signal intensity from a PGSE-storage-PGSE experiment as a function of the sum, bs=b1+b2, and difference bd=b2-b1 of the diffusion encodings separates diffusion weighting from exchange weighting. Exchange leads to upward curvature along a slice of constant bs. Exchanging fractions can be measured rapidly by a finite difference approximation of the curvature using four data points. The method is generalized for non-steady-state and multi-site exchange. We apply the method to image exchanging fractions and calculate exchange rates of water diffusing across the bulk water interface of a glass capillary array.
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Affiliation(s)
- Teddy X Cai
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA; National Institute of Biomedical Imaging and Bioengineering (BESIP), 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
| | - Michal E Komlosh
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA; The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, USA
| | - Peter J Basser
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Nathan H Williamson
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
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Jones DK, Alexander DC, Bowtell R, Cercignani M, Dell'Acqua F, McHugh DJ, Miller KL, Palombo M, Parker GJM, Rudrapatna US, Tax CMW. Microstructural imaging of the human brain with a 'super-scanner': 10 key advantages of ultra-strong gradients for diffusion MRI. Neuroimage 2018; 182:8-38. [PMID: 29793061 DOI: 10.1016/j.neuroimage.2018.05.047] [Citation(s) in RCA: 105] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Revised: 05/17/2018] [Accepted: 05/18/2018] [Indexed: 12/13/2022] Open
Abstract
The key component of a microstructural diffusion MRI 'super-scanner' is a dedicated high-strength gradient system that enables stronger diffusion weightings per unit time compared to conventional gradient designs. This can, in turn, drastically shorten the time needed for diffusion encoding, increase the signal-to-noise ratio, and facilitate measurements at shorter diffusion times. This review, written from the perspective of the UK National Facility for In Vivo MR Imaging of Human Tissue Microstructure, an initiative to establish a shared 300 mT/m-gradient facility amongst the microstructural imaging community, describes ten advantages of ultra-strong gradients for microstructural imaging. Specifically, we will discuss how the increase of the accessible measurement space compared to a lower-gradient systems (in terms of Δ, b-value, and TE) can accelerate developments in the areas of 1) axon diameter distribution mapping; 2) microstructural parameter estimation; 3) mapping micro-vs macroscopic anisotropy features with gradient waveforms beyond a single pair of pulsed-gradients; 4) multi-contrast experiments, e.g. diffusion-relaxometry; 5) tractography and high-resolution imaging in vivo and 6) post mortem; 7) diffusion-weighted spectroscopy of metabolites other than water; 8) tumour characterisation; 9) functional diffusion MRI; and 10) quality enhancement of images acquired on lower-gradient systems. We finally discuss practical barriers in the use of ultra-strong gradients, and provide an outlook on the next generation of 'super-scanners'.
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Affiliation(s)
- D K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK; School of Psychology, Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, 3065, Australia.
| | - D C Alexander
- Centre for Medical Image Computing (CMIC), Department of Computer Science, UCL (University College London), Gower Street, London, UK; Clinical Imaging Research Centre, National University of Singapore, Singapore
| | - R Bowtell
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - M Cercignani
- Department of Psychiatry, Brighton and Sussex Medical School, Brighton, UK
| | - F Dell'Acqua
- Natbrainlab, Department of Neuroimaging, King's College London, London, UK
| | - D J McHugh
- Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK; CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge and Manchester, UK
| | - K L Miller
- Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK
| | - M Palombo
- Centre for Medical Image Computing (CMIC), Department of Computer Science, UCL (University College London), Gower Street, London, UK
| | - G J M Parker
- Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK; CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge and Manchester, UK; Bioxydyn Ltd., Manchester, UK
| | - U S Rudrapatna
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - C M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
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Avram AV, Sarlls JE, Basser PJ. Measuring non-parametric distributions of intravoxel mean diffusivities using a clinical MRI scanner. Neuroimage 2018; 185:255-262. [PMID: 30326294 DOI: 10.1016/j.neuroimage.2018.10.030] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2018] [Revised: 09/19/2018] [Accepted: 10/09/2018] [Indexed: 11/17/2022] Open
Abstract
We measure spectra of water mobilities (i.e., mean diffusivities) from intravoxel pools in brain tissues of healthy subjects with a non-parametric approach. Using a single-shot isotropic diffusion encoding (IDE) preparation, we eliminate signal confounds caused by anisotropic diffusion, including microscopic anisotropy, and acquire in vivo diffusion-weighted images (DWIs) over a wide range of diffusion sensitizations. We analyze the measured IDE signal decays using a regularized inverse laplace transform (ILT) to derive a probability distribution of mean diffusivities of tissue water in each voxel. Based on numerical simulations we assess the sensitivity and accuracy of our ILT analysis and optimize an experimental protocol for use with clinical MRI scanners. In vivo spectra of intravoxel mean diffusivities measured in healthy subjects generally show single-peak distributions throughout the brain parenchyma, with small differences in peak location and shape among white matter, cortical and subcortical gray matter, and cerebrospinal fluid. Mean diffusivity distributions (MDDs) with multiple peaks are observed primarily in voxels at tissue interfaces and are likely due to partial volume contributions. To quantify tissue-specific MDDs with improved statistical power, we average voxel-wise normalized MDDs in corresponding regions-of-interest (ROIs). This non-parametric, rotation-invariant assessment of isotropic diffusivities of tissue water may reflect important microstructural information, such as cell packing and cell size, and active physiological processes, such as water transport and exchange, which may enhance biological specificity in the clinical diagnosis and characterization of ischemic stroke, cancer, neuroinflammation, and neurodegenerative disorders and diseases.
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Affiliation(s)
- Alexandru V Avram
- National Institute of Biomedical Imaging and Bioengineering, National Institute of Health, Bethesda, MD, 20892, USA.
| | - Joelle E Sarlls
- National Institute of Neurological Disorders and Stroke, National Institute of Health, Bethesda, MD, 20892, USA
| | - Peter J Basser
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institute of Health, Bethesda, MD, 20892, USA
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46
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Abstract
The emergence of multiparametric diffusion models combining diffusion and relaxometry measurements provides powerful new ways to explore tissue microstructure, with the potential to provide new insights into tissue structure and function. However, their ability to provide rich analyses and the potential for clinical translation critically depends on the availability of efficient, integrated, multi-dimensional acquisitions. We propose a fully integrated sequence simultaneously sampling the acquisition parameter spaces required for T1 and T2* relaxometry and diffusion MRI. Slice-level interleaved diffusion encoding, multiple spin/gradient echoes and slice-shuffling are combined for higher efficiency, sampling flexibility and enhanced internal consistency. In-vivo data was successfully acquired on healthy adult brains. Obtained parametric maps as well as clustering results demonstrate the potential of the technique to provide eloquent data with an acceleration of roughly 20 compared to conventionally used approaches. The proposed integrated acquisition, which we call ZEBRA, offers significant acceleration and flexibility compared to existing diffusion-relaxometry studies, and thus facilitates wider use of these techniques both for research-driven and clinical applications.
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