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Cai TX, Williamson NH, Ravin R, Basser PJ. The Diffusion Exchange Ratio (DEXR): A minimal sampling of diffusion exchange spectroscopy to probe exchange, restriction, and time-dependence. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2024; 366:107745. [PMID: 39126819 DOI: 10.1016/j.jmr.2024.107745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 07/31/2024] [Accepted: 08/02/2024] [Indexed: 08/12/2024]
Abstract
Water exchange is increasingly recognized as an important biological process that can affect the study of biological tissue using diffusion MR. Methods to measure exchange, however, remain immature as opposed to those used to characterize restriction, with no consensus on the optimal pulse sequence (s) or signal model (s). In general, the trend has been towards data-intensive fitting of highly parameterized models. We take the opposite approach and show that a judicious sub-sample of diffusion exchange spectroscopy (DEXSY) data can be used to robustly quantify exchange, as well as restriction, in a data-efficient manner. This sampling produces a ratio of two points per mixing time: (i) one point with equal diffusion weighting in both encoding periods, which gives maximal exchange contrast, and (ii) one point with the same total diffusion weighting in just the first encoding period, for normalization. We call this quotient the Diffusion EXchange Ratio (DEXR). Furthermore, we show that it can be used to probe time-dependent diffusion by estimating the velocity autocorrelation function (VACF) over intermediate to long times (∼2-500ms). We provide a comprehensive theoretical framework for the design of DEXR experiments in the case of static or constant gradients. Data from Monte Carlo simulations and experiments acquired in fixed and viable ex vivo neonatal mouse spinal cord using a permanent magnet system are presented to test and validate this approach. In viable spinal cord, we report the following apparent parameters from just 6 data points: τk=17±4ms, fNG=0.72±0.01, Reff=1.05±0.01μm, and κeff=0.19±0.04μm/ms, which correspond to the exchange time, restricted or non-Gaussian signal fraction, an effective spherical radius, and permeability, respectively. For the VACF, we report a long-time, power-law scaling with ≈t-2.4, which is approximately consistent with disordered domains in 3-D. Overall, the DEXR method is shown to be highly efficient, capable of providing valuable quantitative diffusion metrics using minimal MR data.
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Affiliation(s)
- Teddy X Cai
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, 20892, MD, USA
| | - Nathan H Williamson
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, 20892, MD, USA
| | - Rea Ravin
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, 20892, MD, USA; Celoptics, Inc., Rockville, 20850, MD, USA
| | - Peter J Basser
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, 20892, MD, USA.
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2
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Zong F, Wang L, Liu H, Xue B, Bai R, Liu Y. A genetic optimisation and iterative reconstruction framework for sparse multi-dimensional diffusion-relaxation correlation MRI. Comput Biol Med 2024; 175:108508. [PMID: 38678941 DOI: 10.1016/j.compbiomed.2024.108508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 04/11/2024] [Accepted: 04/21/2024] [Indexed: 05/01/2024]
Abstract
Multi-dimensional diffusion-relaxation correlation (DRC) magnetic resonance imaging (MRI) techniques have recently been developed to investigate tissue microstructures. Sub-voxel tissue heterogeneity is resolved from the local correlation distributions of relaxation time and molecular diffusivity. However, the implementation of these techniques considerably increases the total acquisition time, and simply reducing the scan time may be at the expense of detailed structural resolution. To overcome these limitations, an optimised framework was proposed for acquiring microstructural maps of the human brain on a clinically feasible timescale. First, the acquisition parameters of the multi-dimensional DRC MRI method were sparsely optimised using a genetic algorithm with a fitness function according to the spectral resolution of the correlation map, hardware requirements, and total scan time. Next, the acquired DRC MRI data were processed using a proposed numerical algorithm based on the dynamic inverse Laplace transform (ILT). Prior knowledge from one-dimensional data was then utilised in the iterative procedure to improve the spectral resolution. Finally, the proposed framework was validated using Monte Carlo simulations and experimental data acquired from healthy participants on an MRI scanner. The results demonstrated that the suggested approach is feasible for offering high-resolution DRC maps that correspond to distinct microstructures with a limited amount of optimised acquisition data from two-dimensional DRC sampling space. By significantly reducing scan time while retaining structural resolution, this approach may enable multi-dimensional DRC MRI to be more widely used for quantitative evaluation in biological and medical settings.
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Affiliation(s)
- Fangrong Zong
- School of Artificial Intelligence, Beijing University of Post and Telecommunication, Beijing, 100876, China.
| | - Lixian Wang
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Huabing Liu
- Beijing Limecho Technology Co., Ltd., Beijing, 102200, China
| | - Bing Xue
- School of Engineering and Computer Science, Victoria University of Wellington, Victoria, 6140, New Zealand
| | - Ruiliang Bai
- Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, 310020, China; MOE Frontier Science Center for Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, 310030, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Post and Telecommunication, Beijing, 100876, China.
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Non-destructive classification of unlabeled cells: Combining an automated benchtop magnetic resonance scanner and artificial intelligence. PLoS Comput Biol 2023; 19:e1010842. [PMID: 36802391 PMCID: PMC9983908 DOI: 10.1371/journal.pcbi.1010842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 03/03/2023] [Accepted: 12/26/2022] [Indexed: 02/23/2023] Open
Abstract
In order to treat degenerative diseases, the importance of advanced therapy medicinal products has increased in recent years. The newly developed treatment strategies require a rethinking of the appropriate analytical methods. Current standards are missing the complete and sterile analysis of the product of interest to make the drug manufacturing effort worthwhile. They only consider partial areas of the sample or product while also irreversibly damaging the investigated specimen. Two-dimensional T1 / T2 MR relaxometry meets these requirements and is therefore a promising in-process control during the manufacturing and classification process of cell-based treatments. In this study a tabletop MR scanner was used to perform two-dimensional MR relaxometry. Throughput was increased by developing an automation platform based on a low-cost robotic arm, resulting in the acquisition of a large dataset of cell-based measurements. Two-dimensional inverse Laplace transformation was used for post-processing, followed by data classification performed with support vector machines (SVM) as well as optimized artificial neural networks (ANN). The trained networks were able to distinguish non-differentiated from differentiated MSCs with a prediction accuracy of 85%. To increase versatility, an ANN was trained on 354 independent, biological replicates distributed across ten different cell lines, resulting in a prediction accuracy of up to 98% depending on data composition. The present study provides a proof of principle for the application of T1 / T2 relaxometry as a non-destructive cell classification method. It does not require labeling of cells and can perform whole mount analysis of each sample. Since all measurements can be performed under sterile conditions, it can be used as an in-process control for cellular differentiation. This distinguishes it from other characterization techniques, as most are destructive or require some type of cell labeling. These advantages highlight the technique's potential for preclinical screening of patient-specific cell-based transplants and drugs.
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4
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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] [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,*Correspondence: Alexandru V. Avram
| | - 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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Cai TX, Williamson NH, Ravin R, Basser PJ. Disentangling the effects of restriction and exchange with diffusion exchange spectroscopy. FRONTIERS IN PHYSICS 2022; 10:805793. [PMID: 37063496 PMCID: PMC10104504 DOI: 10.3389/fphy.2022.805793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Diffusion exchange spectroscopy (DEXSY) is a multidimensional NMR technique that can reveal how water molecules exchange between compartments within heterogeneous media, such as biological tissue. Data from DEXSY experiments is typically processed using numerical inverse Laplace transforms (ILTs) to produce a diffusion-diffusion spectrum. A tacit assumption of this ILT approach is that the signal behavior is Gaussian - i.e., the spin echo intensity decays exponentially with the degree of diffusion weighting. The assumptions that underlie Gaussian signal behavior may be violated, however, depending on the gradient strength applied and the sample under study. We argue that non-Gaussian signal behavior due to restrictions is to be expected in the study of biological tissue using diffusion NMR. Further, we argue that this signal behavior can produce confounding features in the diffusion-diffusion spectra obtained from numerical ILTs of DEXSY data - entangling the effects of restriction and exchange. Specifically, restricted signal behavior can result in broadening of peaks and in the appearance of illusory exchanging compartments with distributed diffusivities, which pearl into multiple peaks if not highly regularized. We demonstrate these effects on simulated data. That said, we suggest the use of features in the signal acquisition domain that can be used to rapidly probe exchange without employing an ILT. We also propose a means to characterize the non-Gaussian signal behavior due to restrictions within a sample using DEXSY measurements with a near zero mixing time or storage interval. We propose a combined acquisition scheme to independently characterize restriction and exchange with various DEXSY measurements, which we term Restriction and Exchange from Equally-weighted Double and Single Diffusion Encodings (REEDS-DE). We test this method on ex vivo neonatal mouse spinal cord - a sample consisting primarily of gray matter - using a low-field, static gradient NMR system. In sum, we highlight critical shortcomings of prevailing DEXSY analysis methods that conflate the effects of restriction and exchange, and suggest a viable experimental approach to disentangle them.
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Affiliation(s)
- Teddy X. Cai
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Nathan H. Williamson
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA
- National Institute of General Medical Sciences, National Institutes of Health, Bethesda, Maryland, USA
| | - Rea Ravin
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA
- Celoptics, Rockville, Maryland, USA
| | - Peter J. Basser
- Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA
- Correspondence: Peter J. Basser, Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Building 13, Room 3W16, 13 South Drive, Bethesda, Maryland 20892-5772, USA,
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6
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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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7
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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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8
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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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9
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Magdoom KN, Pajevic S, Dario G, Basser PJ. A new framework for MR diffusion tensor distribution. Sci Rep 2021; 11:2766. [PMID: 33531530 PMCID: PMC7854653 DOI: 10.1038/s41598-021-81264-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 11/19/2020] [Indexed: 12/25/2022] Open
Abstract
The ability to characterize heterogeneous and anisotropic water diffusion processes within macroscopic MRI voxels non-invasively and in vivo is a desideratum in biology, neuroscience, and medicine. While an MRI voxel may contain approximately a microliter of tissue, our goal is to examine intravoxel diffusion processes on the order of picoliters. Here we propose a new theoretical framework and efficient experimental design to describe and measure such intravoxel structural heterogeneity and anisotropy. We assume that a constrained normal tensor-variate distribution (CNTVD) describes the variability of positive definite diffusion tensors within a voxel which extends its applicability to a wide range of b-values while preserving the richness of diffusion tensor distribution (DTD) paradigm unlike existing models. We introduce a new Monte Carlo (MC) scheme to synthesize realistic 6D DTD numerical phantoms and invert the MR signal. We show that the signal inversion is well-posed and estimate the CNTVD parameters parsimoniously by exploiting the different symmetries of the mean and covariance tensors of CNTVD. The robustness of the estimation pipeline is assessed by adding noise to calculated MR signals and compared with the ground truth. A family of invariant parameters and glyphs which characterize microscopic shape, size and orientation heterogeneity within a voxel are also presented.
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Affiliation(s)
- Kulam Najmudeen Magdoom
- Division on Translational Imaging and Genomic Integrity, Eunice Kennedy Shriver, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Sinisa Pajevic
- Division on Translational Imaging and Genomic Integrity, Eunice Kennedy Shriver, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Gasbarra Dario
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Peter J Basser
- Division on Translational Imaging and Genomic Integrity, Eunice Kennedy Shriver, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
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10
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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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11
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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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12
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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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13
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Curtis AD, Cheng HM. Primer and Historical Review on Rapid Cardiac
CINE MRI. J Magn Reson Imaging 2020; 55:373-388. [DOI: 10.1002/jmri.27436] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 10/26/2020] [Accepted: 10/27/2020] [Indexed: 12/14/2022] Open
Affiliation(s)
- Aaron D. Curtis
- The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Toronto Ontario Canada
- Ted Rogers Centre for Heart Research, Translational Biology & Engineering Program Toronto Ontario Canada
| | - Hai‐Ling M. Cheng
- The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Toronto Ontario Canada
- Ted Rogers Centre for Heart Research, Translational Biology & Engineering Program Toronto Ontario Canada
- Institute of Biomedical Engineering, University of Toronto Toronto Ontario Canada
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14
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Ning L, Gagoski B, Szczepankiewicz F, Westin CF, Rathi Y. Joint RElaxation-Diffusion Imaging Moments to Probe Neurite Microstructure. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:668-677. [PMID: 31398113 PMCID: PMC7164590 DOI: 10.1109/tmi.2019.2933982] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Joint relaxation-diffusion measurements can provide new insight about the tissue microstructural properties. Most recent methods have focused on inverting the Laplace transform to recover the joint distribution of relaxation-diffusion. However, as is well-known, this problem is notoriously ill-posed and numerically unstable. In this work, we address this issue by directly computing the joint moments of transverse relaxation rate and diffusivity, which can be robustly estimated. To zoom into different parts of the joint distribution, we further enhance our method by applying multiplicative filters to the joint probability density function of relaxation and diffusion and compute the corresponding moments. We propose an approach to use these moments to compute several novel scalar indices to characterize specific properties of the underlying tissue microstructure. Furthermore, for the first time, we propose an algorithm to estimate diffusion signals that are independent of echo time based on the moments of the marginal probability density function of diffusion. We demonstrate its utility in extracting tissue information not contaminated with multiple intra-voxel relaxation rates. We compare the performance of four types of filters that zoom into tissue components with different relaxation and diffusion properties and demonstrate it on an in-vivo human dataset. Experimental results show that these filters are able to characterize heterogeneous tissue microstructure. Moreover, the filtered diffusion signals are also able to distinguish fiber bundles with similar orientations but different relaxation rates. The proposed method thus allows to characterize the neural microstructure information in a robust and unique manner not possible using existing techniques.
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15
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Terenzi C, Sederman AJ, Mantle MD, Gladden LF. Spatially-resolved 1H NMR relaxation-exchange measurements in heterogeneous media. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2019; 299:101-108. [PMID: 30593999 DOI: 10.1016/j.jmr.2018.12.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 12/07/2018] [Accepted: 12/13/2018] [Indexed: 06/09/2023]
Abstract
In the last decades, the 1H NMR T2-T2 relaxation-exchange (REXSY) technique has become an essential tool for the molecular investigation of simple and complex fluids in heterogeneous porous solids and soft matter, where the mixing-time-evolution of cross-correlated T2-T2 peaks enables a quantitative study of diffusive exchange kinetics in multi-component systems. Here, we present a spatially-resolved implementation of the T2-T2 correlation technique, named z-T2-T2, based on one-dimensional spatial mapping along z using a rapid frequency-encode imaging scheme. Compared to other phase-encoding methods, the adopted MRI technique has two distinct advantages: (i) is has the same experimental duration of a standard (bulk) T2-T2 measurement, and (ii) it provides a high spatial resolution. The proposed z-T2-T2 method is first validated against bulk T2-T2 measurements on homogeneous phantom consisting of cyclohexane uniformly imbibed in finely-sized α-Al2O3 particles at a spatial resolution of 0.47 mm; thereafter, its performance is demonstrated, on a layered bed of multi-sized α-Al2O3 particles, for revealing spatially-dependent molecular exchange kinetics properties of intra- and inter-particle cyclohexane as a function of particle size. It is found that localised z-T2-T2 spectra provide well resolved cross peaks whilst such resolution is lost in standard bulk T2-T2 data. Future prospective applications of the method lie, in particular, in the local characterisation of mass transport phenomena in multi-component porous media, such as rock cores and heterogeneous catalysts.
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Affiliation(s)
- Camilla Terenzi
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK
| | - Andrew J Sederman
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK
| | - Michael D Mantle
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK.
| | - Lynn F Gladden
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK
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16
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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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17
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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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18
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Arrigoni F, De Luca A, Velardo D, Magri F, Gandossini S, Russo A, Froeling M, Bertoldo A, Leemans A, Bresolin N, D'angelo G. Multiparametric quantitative MRI assessment of thigh muscles in limb-girdle muscular dystrophy 2A and 2B. Muscle Nerve 2018; 58:550-558. [PMID: 30028523 DOI: 10.1002/mus.26189] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 05/29/2018] [Accepted: 06/03/2018] [Indexed: 12/15/2022]
Abstract
INTRODUCTION The aim of this study was to apply quantitative MRI (qMRI) to assess structural modifications in thigh muscles of subjects with limb girdle muscular dystrophy (LGMD) 2A and 2B with long disease duration. METHODS Eleven LGMD2A, 9 LGMD2B patients and 11 healthy controls underwent a multi-parametric 3T MRI examination of the thigh. The protocol included structural T1-weighted images, DIXON sequences for fat fraction calculation, T2 values quantification and diffusion MRI. Region of interest analysis was performed on 4 different compartments (anterior compartment, posterior compartment, gracilis, sartorius). RESULTS Patients showed high levels of fat infiltration as measured by DIXON sequences. Sartorius and anterior compartment were more infiltrated in LGMD2B than LGMD2A patients. T2 values were mildly reduced in both disorders. Correlations between clinical scores and qMRI were found. CONCLUSIONS qMRI measures may help to quantify muscular degeneration, but careful interpretation is needed when fat infiltration is massive. Muscle Nerve 58: 550-558, 2018.
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Affiliation(s)
- Filippo Arrigoni
- Neuroimaging Lab, Scientific Institute, IRCCS E. Medea, Via don L. Monza 20, Bosisio Parini, Italy
| | - Alberto De Luca
- Image Sciences Institute, University Medical Center Utrecht and University Utrecht, Utrecht, The Netherlands
| | - Daniele Velardo
- NeuroMuscular Unit, Scientific Institute, IRCCS E. Medea, Bosisio Parini, Italy
| | - Francesca Magri
- Neurology Unit, IRCCS Foundation Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Sandra Gandossini
- NeuroMuscular Unit, Scientific Institute, IRCCS E. Medea, Bosisio Parini, Italy
| | - Annamaria Russo
- NeuroMuscular Unit, Scientific Institute, IRCCS E. Medea, Bosisio Parini, Italy
| | - Martijn Froeling
- NeuroMuscular Unit, Scientific Institute, IRCCS E. Medea, Bosisio Parini, Italy
| | | | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht and University Utrecht, Utrecht, The Netherlands
| | - Nereo Bresolin
- Neurology Unit, IRCCS Foundation Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Grazia D'angelo
- NeuroMuscular Unit, Scientific Institute, IRCCS E. Medea, Bosisio Parini, Italy
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19
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Kim D, Wisnowski JL, Nguyen CT, Haldar JP. PROBING IN VIVO MICROSTRUCTURE WITH T 1- T 2 RELAXATION CORRELATION SPECTROSCOPIC IMAGING. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2018; 2018:675-678. [PMID: 30854157 DOI: 10.1109/isbi.2018.8363664] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Quantitative MR relaxometry can provide unique subvoxel information about the microscopic tissue compartments that are present in a large imaging voxel. However, unambiguously distinguishing between these tissue compartments continues to be challenging with conventional methods due to the illposedness of the inverse problem. This paper describes a new imaging approach, which we call T 1 Relaxation-T 2 Relaxation Correlation Spectroscopic Imaging (RR-CSI), that uses two-dimensional relaxation encoding combined with spatially-constrained reconstruction to help overcome illposedness. Results are shown with real data, including what we believe to be the first in vivo demonstration of multidimensional relaxation correlation spectroscopic imaging.
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Affiliation(s)
- Daeun Kim
- Electrical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Jessica L Wisnowski
- Radiology and Pediatrics, Division of Neonatology, Children's Hospital Los Angeles, CA, USA
| | | | - Justin P Haldar
- Electrical Engineering, University of Southern California, Los Angeles, CA, USA
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20
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Kim D, Doyle EK, Wisnowski JL, Kim JH, Haldar JP. Diffusion-relaxation correlation spectroscopic imaging: A multidimensional approach for probing microstructure. Magn Reson Med 2017; 78:2236-2249. [PMID: 28317261 PMCID: PMC5605406 DOI: 10.1002/mrm.26629] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Revised: 12/19/2016] [Accepted: 01/10/2017] [Indexed: 12/16/2022]
Abstract
PURPOSE To propose and evaluate a novel multidimensional approach for imaging subvoxel tissue compartments called Diffusion-Relaxation Correlation Spectroscopic Imaging. THEORY AND METHODS Multiexponential modeling of MR diffusion or relaxation data is commonly used to infer the many different microscopic tissue compartments that contribute signal to macroscopic MR imaging voxels. However, multiexponential estimation is known to be difficult and ill-posed. Observing that this ill-posedness is theoretically reduced in higher dimensions, diffusion-relaxation correlation spectroscopic imaging uses a novel multidimensional imaging experiment that jointly encodes diffusion and relaxation information, and then uses a novel constrained reconstruction technique to generate a multidimensional diffusion-relaxation correlation spectrum for every voxel. The peaks of the multidimensional spectrum are expected to correspond to the distinct tissue microenvironments that are present within each macroscopic imaging voxel. RESULTS Using numerical simulations, experiment data from a custom-built phantom, and experiment data from a mouse model of traumatic spinal cord injury, diffusion-relaxation correlation spectroscopic imaging is demonstrated to provide substantially better multicompartment resolving power compared to conventional diffusion- and relaxation-based methods. CONCLUSION The diffusion-relaxation correlation spectroscopic imaging approach provides powerful new capabilities for resolving the different components of multicompartment tissue models, and can be leveraged to significantly expand the insights provided by MRI in studies of tissue microstructure. Magn Reson Med 78:2236-2249, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Daeun Kim
- Electrical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Eamon K. Doyle
- Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
- Cardiology, Children’s Hospital Los Angeles, Los Angeles, CA, USA
| | | | - Joong Hee Kim
- Neurology and Radiology, Washington University, St. Louis, MO, USA
| | - Justin P. Haldar
- Electrical Engineering, University of Southern California, Los Angeles, CA, USA
- Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
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21
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Benjamini D, Basser PJ. Magnetic resonance microdynamic imaging reveals distinct tissue microenvironments. Neuroimage 2017; 163:183-196. [PMID: 28943412 DOI: 10.1016/j.neuroimage.2017.09.033] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2017] [Revised: 09/12/2017] [Accepted: 09/18/2017] [Indexed: 10/18/2022] Open
Abstract
Magnetic resonance imaging (MRI) provides a powerful set of tools with which to investigate biological tissues noninvasively and in vivo. Tissues are heterogeneous in nature; an imaging voxel contains an ensemble of different cells and extracellular matrix components. A long-standing challenge has been to infer the content of and interactions among these microscopic tissue components within a macroscopic imaging voxel. Spatially resolved multidimensional relaxation-diffusion correlation (REDCO) spectroscopy holds the potential to deliver such microdynamic information. However, to date, vast data requirements have mostly relegated these type of measurements to nuclear magnetic resonance applications and prevented them from being widely and successfully used in conjunction with imaging. By using a novel data acquisition and processing strategy in this study, spatially resolved REDCO could be performed in reasonable scanning times with excellent prospects for clinical applications. This new MR imaging framework-which we term "magnetic resonance microdynamic imaging (MRMI)"-permits the simultaneous noninvasive and model-free quantification of multiple subcellular, cellular, and interstitial tissue microenvironments within a voxel. MRMI is demonstrated with a fixed spinal cord specimen, enabling the quantification of microscopic tissue components with unprecedented specificity. Tissue components, such as axons, neuronal and glial soma, and myelin were identified on the basis of their multispectral signature within individual imaging voxels. These tissue elements could then be composed into images and be correlated with immunohistochemistry findings. MRMI provides novel image contrasts of tissue components and a new family of microdynamic biomarkers that could lead to new diagnostic imaging approaches to probe biological tissue alterations accompanied by pathological or developmental changes.
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Affiliation(s)
- Dan Benjamini
- Section on Quantitative Imaging and Tissue Sciences, NICHD, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Peter J Basser
- Section on Quantitative Imaging and Tissue Sciences, NICHD, National Institutes of Health, Bethesda, MD 20892, USA
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22
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Zhou Z, Bai R, Munasinghe J, Nie L, Chen X. T 1-T 2 Dual-Modal Magnetic Resonance Imaging: From Molecular Basis to Contrast Agents. ACS NANO 2017; 11:5227-5232. [PMID: 28613821 PMCID: PMC9617470 DOI: 10.1021/acsnano.7b03075] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Multimodal imaging strategies integrating manifold images have improved our ability to diagnose, to guide therapy, and to predict outcomes. Magnetic resonance imaging (MRI) is among the most widely used imaging technique in the clinic and can enable multiparameter anatomical demonstration of diagnosis. Due to the inherent black-and-white production of MR images, however, MRI detection is largely hampered by the occurrence of false-positive diagnoses. In this Perspective, we introduce the paradigm of manipulating the multiparameter MRI, T1-T2 dual-modal MRI, along with enhancement by specific contrast agents. We hope this discussion will promote emerging research interest in T1-T2 dual-modal MRI and provoke the rational design of contrast agents for sophisticated MRI applications.
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Affiliation(s)
- Zijian Zhou
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen 361102, China
- Laboratory of Molecular Imaging and Nanomedicine, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health
| | - Ruiliang Bai
- Section on Quantitative Imaging and Tissue Science, National Institute of Child Health and Human Development, National Institutes of Health
| | - Jeeva Munasinghe
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Liming Nie
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen 361102, China
- Corresponding Authors: Xiaoyuan Chen: , Liming Nie:
| | - Xiaoyuan Chen
- Laboratory of Molecular Imaging and Nanomedicine, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health
- Corresponding Authors: Xiaoyuan Chen: , Liming Nie:
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23
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Bai R, Benjamini D, Cheng J, Basser PJ. Fast, accurate 2D-MR relaxation exchange spectroscopy (REXSY): Beyond compressed sensing. J Chem Phys 2016; 145:154202. [PMID: 27782473 PMCID: PMC5074998 DOI: 10.1063/1.4964144] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Accepted: 09/19/2016] [Indexed: 11/14/2022] Open
Abstract
Previously, we showed that compressive or compressed sensing (CS) can be used to reduce significantly the data required to obtain 2D-NMR relaxation and diffusion spectra when they are sparse or well localized. In some cases, an order of magnitude fewer uniformly sampled data were required to reconstruct 2D-MR spectra of comparable quality. Nonetheless, this acceleration may still not be sufficient to make 2D-MR spectroscopy practicable for many important applications, such as studying time-varying exchange processes in swelling gels or drying paints, in living tissue in response to various biological or biochemical challenges, and particularly for in vivo MRI applications. A recently introduced framework, marginal distributions constrained optimization (MADCO), tremendously accelerates such 2D acquisitions by using a priori obtained 1D marginal distribution as powerful constraints when 2D spectra are reconstructed. Here we exploit one important intrinsic property of the 2D-MR relaxation exchange spectra: the fact that the 1D marginal distributions of each 2D-MR relaxation exchange spectrum in both dimensions are equal and can be rapidly estimated from a single Carr-Purcell-Meiboom-Gill (CPMG) or inversion recovery prepared CPMG measurement. We extend the MADCO framework by further proposing to use the 1D marginal distributions to inform the subsequent 2D data-sampling scheme, concentrating measurements where spectral peaks are present and reducing them where they are not. In this way we achieve compression or acceleration that is an order of magnitude greater than that in our previous CS method while providing data in reconstructed 2D-MR spectral maps of comparable quality, demonstrated using several simulated and real 2D T2 - T2 experimental data. This method, which can be called "informed compressed sensing," is extendable to other 2D- and even ND-MR exchange spectroscopy.
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Affiliation(s)
- Ruiliang Bai
- Section on Quantitative Imaging and Tissue Sciences, DIBGI, NICHD, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Dan Benjamini
- Section on Quantitative Imaging and Tissue Sciences, DIBGI, NICHD, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Jian Cheng
- Section on Quantitative Imaging and Tissue Sciences, DIBGI, NICHD, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Peter J Basser
- Section on Quantitative Imaging and Tissue Sciences, DIBGI, NICHD, National Institutes of Health, Bethesda, Maryland 20892, USA
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24
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Benjamini D, Basser PJ. Use of marginal distributions constrained optimization (MADCO) for accelerated 2D MRI relaxometry and diffusometry. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2016; 271:40-5. [PMID: 27543810 PMCID: PMC5026962 DOI: 10.1016/j.jmr.2016.08.004] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Revised: 08/08/2016] [Accepted: 08/10/2016] [Indexed: 05/02/2023]
Abstract
Measuring multidimensional (e.g., 2D) relaxation spectra in NMR and MRI clinical applications is a holy grail of the porous media and biomedical MR communities. The main bottleneck is the inversion of Fredholm integrals of the first kind, an ill-conditioned problem requiring large amounts of data to stabilize a solution. We suggest a novel experimental design and processing framework to accelerate and improve the reconstruction of such 2D spectra that uses a priori information from the 1D projections of spectra, or marginal distributions. These 1D marginal distributions provide powerful constraints when 2D spectra are reconstructed, and their estimation requires an order of magnitude less data than a conventional 2D approach. This marginal distributions constrained optimization (MADCO) methodology is demonstrated here with a polyvinylpyrrolidone-water phantom that has 3 distinct peaks in the 2D D-T1 space. The stability, sensitivity to experimental parameters, and accuracy of this new approach are compared with conventional methods by serially subsampling the full data set. While the conventional, unconstrained approach performed poorly, the new method had proven to be highly accurate and robust, only requiring a fraction of the data. Additionally, synthetic T1-T2 data are presented to explore the effects of noise on the estimations, and the performance of the proposed method with a smooth and realistic 2D spectrum. The proposed framework is quite general and can also be used with a variety of 2D MRI experiments (D-T2,T1-T2,D-D, etc.), making these potentially feasible for preclinical and even clinical applications for the first time.
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Affiliation(s)
- Dan Benjamini
- Quantitative Imaging and Tissue Sciences, NICHD, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Peter J Basser
- Quantitative Imaging and Tissue Sciences, NICHD, National Institutes of Health, Bethesda, MD 20892, USA
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Guay MD, Czaja W, Aronova MA, Leapman RD. Compressed Sensing Electron Tomography for Determining Biological Structure. Sci Rep 2016; 6:27614. [PMID: 27291259 PMCID: PMC4904377 DOI: 10.1038/srep27614] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2016] [Accepted: 05/20/2016] [Indexed: 12/22/2022] Open
Abstract
There has been growing interest in applying compressed sensing (CS) theory and practice to reconstruct 3D volumes at the nanoscale from electron tomography datasets of inorganic materials, based on known sparsity in the structure of interest. Here we explore the application of CS for visualizing the 3D structure of biological specimens from tomographic tilt series acquired in the scanning transmission electron microscope (STEM). CS-ET reconstructions match or outperform commonly used alternative methods in full and undersampled tomogram recovery, but with less significant performance gains than observed for the imaging of inorganic materials. We propose that this disparity stems from the increased structural complexity of biological systems, as supported by theoretical CS sampling considerations and numerical results in simulated phantom datasets. A detailed analysis of the efficacy of CS-ET for undersampled recovery is therefore complicated by the structure of the object being imaged. The numerical nonlinear decoding process of CS shares strong connections with popular regularized least-squares methods, and the use of such numerical recovery techniques for mitigating artifacts and denoising in reconstructions of fully sampled datasets remains advantageous. This article provides a link to the software that has been developed for CS-ET reconstruction of electron tomographic data sets.
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Affiliation(s)
- Matthew D. Guay
- University of Maryland, Department of Applied Mathematics and Scientific Computation, College Park, MD 20742, USA
| | - Wojciech Czaja
- University of Maryland, Department of Mathematics, College Park, MD 20742, USA
| | - Maria A. Aronova
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD 20892, USA
| | - Richard D. Leapman
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD 20892, USA
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Song R, Song YQ, Vembusubramanian M, Paulsen JL. The robust identification of exchange from T2-T2 time-domain features. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2016; 265:164-171. [PMID: 26905815 DOI: 10.1016/j.jmr.2016.02.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Revised: 01/30/2016] [Accepted: 02/01/2016] [Indexed: 06/05/2023]
Abstract
Two-dimensional spin-spin relaxation (T2-T2) techniques have been developed to probe coupling between different environments such as diffusive coupling between small and large pores or chemical exchange with clays. In these studies, Numerical Laplace Inversion (NLI) is used to obtain two-dimensional T2-T2 relaxation distribution spectrum from the T2-T2 signal decays, and the off-diagonal peaks characterize coupling. Often, these coupling peaks are both weak and close to the diagonal and combined with the inherently ill-conditioned nature of the inversion, their presence is difficult to differentiate from inversion related artifacts and blurring. This manuscript presents a time domain based analysis to identify the presence of coupling that avoids the ambiguities present in T2-T2 spectra. The approach utilizes the symmetric nature of the two-dimensional time domain data, where the presence of curvature along one of these symmetries gives an unambiguous indicator of coupling. Measurements on porous glass beads are used to verify the technique.
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Affiliation(s)
- Ruobing Song
- Schlumberger Doll, 1 Hampshire St., Cambridge, MA 02139, United States.
| | - Yi-Qiao Song
- Schlumberger Doll, 1 Hampshire St., Cambridge, MA 02139, United States.
| | | | - Jeffrey L Paulsen
- Schlumberger Doll, 1 Hampshire St., Cambridge, MA 02139, United States.
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