Toward nonparametric diffusion‐ characterization of crossing fibers in the human brain

Reymbaut, Alexis; Critchley, Jeffrey; Durighel, Giuliana; Sprenger, Tim; Sughrue, Michael E.; Bryskhe, Karin; Topgaard, Daniel

doi:10.1002/mrm.28604

Cited by 31 publications

(51 citation statements)

References 113 publications

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“…Recently, frameworks that combine diffusion tensor MRI and relaxometry have been introduced to improve their specificity for the quantification of myelin and axonal attributes irrespective of the complexity of fiber organization within the voxel, even in the presence of crossing fibers [ 150 , 151 ]. A growing body of evidence also suggests the role of excess iron deposition in the pathology of neurodegenerative diseases.…”

Section: Discussionmentioning

confidence: 99%

Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases

Kamagata

Andica

Kato

et al. 2021

IJMS

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There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.

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Section: Discussionmentioning

confidence: 99%

Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases

Kamagata

Andica

Kato

et al. 2021

IJMS

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“…Multidimensional MRI is an emerging approach [37] that is now being applied to address a range of medical conditions such as prediction of pregnancy complications via placenta characterization [9], spinal cord injury [6,38], prostate cancer [39], breast cancer [40], and axonal injury due to TBI [22]. Recent in vivo proof-of-concept applications of subvoxel T 1 -T 2 correlation spectra using 105 images [41] and of subvoxel diffusion-T 1 correlation spectra using 363 [11] and 304 [42] images are promising. Here we showed that accurate and robust subvoxel T 1 -T 2 and MD-T 2 correlation spectra can be obtained using only 36 and 122 images, respectively, by using a constrained optimization data processing framework (i.e., MADCO [13]) in conjunction with applying the NESMA filter to reduce noise.…”

Section: Discussionmentioning

confidence: 99%

Multidimensional MRI for characterization of subtle axonal injury accelerated using an adaptive nonlocal multispectral filter

Benjamini

Bouhrara

Komlosh

et al. 2021

Preprint

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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 MD-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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“… 40 An alternative approach avoids inversion recovery altogether by only varying TR to yield T1 sensitivity. 41 …”

Section: Acquisitionmentioning

confidence: 99%

“…For example, a sequence combining tensor‐valued diffusion encoding with multiple TR weighting was introduced as a means to establish correlations between

and diffusion properties and thus characterize complex fiber orientation layouts. 41 However, the combined relaxation and b‐tensor protocols developed thus far have utilized a rather straightforward design, particularly from the relaxation encoding perspective, with suboptimal echo and recovery time samplings. In this regard, there is potential for improvement through combining arbitrary b‐tensor shape with acquisition strategies that encode relaxation information more efficiently.…”

Section: Acquisitionmentioning

confidence: 99%

Combined diffusion‐relaxometry microstructure imaging: Current status and future prospects

Slator

Palombo

Miller

et al. 2021

Magnetic Resonance in Med

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Toward nonparametric diffusion‐ characterization of crossing fibers in the human brain

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 31 publications

References 113 publications

Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases

Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases

Multidimensional MRI for characterization of subtle axonal injury accelerated using an adaptive nonlocal multispectral filter

Combined diffusion‐relaxometry microstructure imaging: Current status and future prospects

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