Quantification of Tissue Microstructure Using Tensor-Valued Diffusion Encoding: Brain and Body

Afzali, Maryam; Mueller, Lars; Szczepankiewicz, Filip; Jones, Derek K.; Schneider, Jürgen

doi:10.3389/fphy.2022.809133

Cited by 4 publications

(5 citation statements)

References 163 publications

(255 reference statements)

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“…DTI metrics are sensitive but not specific. We expected features exclusive to B-Tensor encoding to improve the classification algorithm by providing additional information, given their specificity to certain properties of microstructure [8]. For instance, we expected C c to capture the increased axonal dispersion typical of WM degeneration, as previous studies using standard DW-MRI acquisitions had suggested [32].…”

Section: Discussionmentioning

confidence: 99%

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Differentiation of white matter histopathology using b-tensor encoding and machine learning

Rios-Carrillo

Ramírez-Manzanares

Luna-Munguía

et al. 2023

Preprint

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Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is a non-invasive technique that is sensitive to microstructural geometry in neural tissue and is useful for the detection of neuropathology in research and clinical settings. Tensor valued diffusion encoding schemes (b-tensor) have been developed to enrich the microstructural data that can be obtained through DW-MRI. These advanced methods have proven to be more specific to microstructural properties than conventional DW-MRI acquisitions. Additionally, machine learning methods are particularly useful for the study of multidimensional data sets. In this work, we have tested the reach of b-tensor encoding data analyses with machine learning in different histopathological scenarios. We achieved this in three steps: 1) We induced different forms of white matter damage in rodent optic nerves. 2) We obtained ex-vivo DW-MRI with b-tensor encoding schemes and calculated quantitative metrics using Q-space Trajectory Imaging. 3) We used a machine learning model to identify the main contributing features and built a voxel-wise probabilistic classification map of histological damage. Our results show that this model is sensitive to characteristics of microstructural damage. In conclusion, b-tensor encoded DW-MRI analyzed with machine learning methods, have the potential to be further developed for the detection of histopathology and neurodegeneration.

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Section: Discussionmentioning

confidence: 99%

“…Thus, MDE DW-MRI can potentially characterize certain neuropathological changes in detail. However, relatively few studies have used this technique for this purpose [8].…”

Section: Introductionmentioning

confidence: 99%

Differentiation of white matter histopathology using b-tensor encoding and machine learning

Rios-Carrillo

Ramírez-Manzanares

Luna-Munguía

et al. 2023

Preprint

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“…However, there has been interest in going beyond SDE with double (planar) or triple (spherical) diffusion encoding, which can help disentangle features of various microstructure compartments (like orientation dispersion) within a voxel (Afzali et al, 2022). (Basser et al, 1994), Diffusion Kurtosis Imaging (DKI) (Jensen et al, 2005), and the Q-ball method (Tuch, 2004).…”

Section: Diffusion Mri Modelsmentioning

confidence: 99%

Diffusion MRI of the Hippocampus

Karat,

Khler,

Khan

2024

J. Neurosci.

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The hippocampus is a brain structure that plays key roles in a variety of cognitive processes. Critically, a wide range of neurological disorders are associated with degeneration of the hippocampal microstructure, defined as neurons, dendrites, glial cells, and more. Thus, the hippocampus is a key target for methods that are sensitive to these microscale properties. Diffusion MRI is one such method, which can noninvasively probe neural architecture. Here we review the extensive use of diffusion MRI to capture hippocampal microstructure in both health and disease. The results of these studies indicate that (1) diffusion tensor imaging is sensitive but not specific to the hippocampal microstructure; (2) biophysical modeling of diffusion MRI signals is a promising avenue to capture more specific aspects of the hippocampal microstructure; (3) use of ultra-short diffusion times have shown unique laminar-specific microstructure and response to hippocampal injury; (4) dispersion of microstructure is likely abundant in the hippocampus; and (5) the angular richness of the diffusion MRI signal can be leveraged to improve delineation of the internal hippocampal circuitry. Overall, extant findings suggest that diffusion MRI offers a promising avenue for characterizing hippocampal microstructure.

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“…Hence, DTI metrics are sensitive but not specific. We expected features exclusive to b-tensor encoding to improve the classification algorithm by providing additional information, given their specificity to certain properties of microstructure [11]. In this light, C c should capture the increased axonal dispersion characteristic of white matter degeneration [54], while μFA would inform of the reduction of microscopic anisotropy.…”

Section: Plos Onementioning

confidence: 99%

“…Thus, MDE DW-MRI potentially describes neuropathological changes in detail. However, relatively few studies have used this technique for this purpose [11,12].…”

Section: Introductionmentioning

confidence: 99%

Differentiation of white matter histopathology using b-tensor encoding and machine learning

et al. 2023

View full text Add to dashboard Cite

Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive technique that is sensitive to microstructural geometry in neural tissue and is useful for the detection of neuropathology in research and clinical settings. Tensor-valued diffusion encoding schemes (b-tensor) have been developed to enrich the microstructural data that can be obtained through DW-MRI. These advanced methods have proven to be more specific to microstructural properties than conventional DW-MRI acquisitions. Additionally, machine learning methods are particularly useful for the study of multidimensional data sets. In this work, we have tested the reach of b-tensor encoding data analyses with machine learning in different histopathological scenarios. We achieved this in three steps: 1) We induced different levels of white matter damage in rodent optic nerves. 2) We obtained ex vivo DW-MRI data with b-tensor encoding schemes and calculated quantitative metrics using Q-space trajectory imaging. 3) We used a machine learning model to identify the main contributing features and built a voxel-wise probabilistic classification map of histological damage. Our results show that this model is sensitive to characteristics of microstructural damage. In conclusion, b-tensor encoded DW-MRI data analyzed with machine learning methods, have the potential to be further developed for the detection of histopathology and neurodegeneration.

show abstract

Quantification of Tissue Microstructure Using Tensor-Valued Diffusion Encoding: Brain and Body

Cited by 4 publications

References 163 publications

Differentiation of white matter histopathology using b-tensor encoding and machine learning

Differentiation of white matter histopathology using b-tensor encoding and machine learning

Diffusion MRI of the Hippocampus

Differentiation of white matter histopathology using b-tensor encoding and machine learning

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