Q-Space Modeling in Diffusion-Weighted MRI

Aganj, Iman; Sapiro, Guillermo; Harel, Noam

doi:10.1016/b978-0-12-397025-1.00293-1

Cited by 3 publications

(1 citation statement)

References 91 publications

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“…With these types of models it is possible to increase the angular resolution of the orientational information and describe crossing fibers within a voxel. In these cases, diffusion-encoding images can be represented as sampling points on a spherical shell (Aganj et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Down-sampling in diffusion MRI: a bundle-specific DTI and NODDI study

Spagnolo,

Gobbi,

Zsoldos

et al. 2024

Front. Neuroimaging

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IntroductionMulti-shell diffusion Magnetic Resonance Imaging (dMRI) data has been widely used to characterise white matter microstructure in several neurodegenerative diseases. The lack of standardised dMRI protocols often implies the acquisition of redundant measurements, resulting in prolonged acquisition times. In this study, we investigate the impact of the number of gradient directions on Diffusion Tensor Imaging (DTI) and on Neurite Orientation Dispersion and Density Imaging (NODDI) metrics.MethodsData from 124 healthy controls collected in three different longitudinal studies were included. Using an in-house algorithm, we reduced the number of gradient directions in each data shell. We estimated DTI and NODDI measures on six white matter bundles clinically relevant for neurodegenerative diseases.ResultsFractional Anisotropy (FA) measures on bundles where data were sampled at the 30% rate, showed a median L1 distance of up to 3.92% and a 95% CI of (1.74, 8.97)% when compared to those obtained at reference sampling. Mean Diffusivity (MD) reached up to 4.31% and a 95% CI of (1.60, 16.98)% on the same premises. At a sampling rate of 50%, we obtained a median of 3.90% and a 95% CI of (1.99, 16.65)% in FA, and 5.49% with a 95% CI of (2.14, 21.68)% in MD. The Intra-Cellular volume fraction (ICvf) median L1 distance was up to 2.83% with a 95% CI of (1.98, 4.82)% at a 30% sampling rate and 3.95% with a 95% CI of (2.39, 7.81)% at a 50% sampling rate. The volume difference of the reconstructed white matter at reference and 50% sampling reached a maximum of (2.09 ± 0.81)%.DiscussionIn conclusion, DTI and NODDI measures reported at reference sampling were comparable to those obtained when the number of dMRI volumes was reduced by up to 30%. Close to reference DTI and NODDI metrics were estimated with a significant reduction in acquisition time using three shells, respectively with: 4 directions at a b value of 700 s/mm2, 14 at 1000 s/mm2, and 32 at 2000 s/mm2. The study revealed aspects that can be important for large-scale clinical studies on bundle-specific diffusion MRI.

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Section: Introductionmentioning

confidence: 99%

Down-sampling in diffusion MRI: a bundle-specific DTI and NODDI study

Spagnolo,

Gobbi,

Zsoldos

et al. 2024

Front. Neuroimaging

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Automatic Verification of the Gradient Table in Diffusion-Weighted MRI Based on Fiber Continuity

Aganj

2018

Sci Rep

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In diffusion-weighted magnetic resonance imaging (dMRI), the coordinate systems where the image and the diffusion gradients are represented may be inconsistent, thus impacting the quality of subsequent fiber tracking and connectivity analysis. Empirical verification of the reconstructed fiber orientations and subsequent correction of the gradient table (by permutation and flipping), both time-consuming tasks, are therefore often necessary. To save manual labor in studies involving dMRI, we introduce a new automatic gradient-table verification approach, which we propose to include in the dMRI processing pipeline. To that end, we exploit the concept of fiber continuity – the assumption that, in the fibrous tissue (such as the brain white matter), fiber bundles vary smoothly along their own orientations. Our tractography-free method tries all possible permutation and flip configurations of the gradient table, and in each case, assesses the consistency of the reconstructed fiber orientations with fiber continuity. Our algorithm then suggests the correct gradient table by choosing the configuration with the most consistent fiber orientations. We validated our method in 185 experiments on human brain dMRI data form three public data sources. The proposed algorithm identified the correct permutation and flip configuration for the gradient table in all the experiments.

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Quantification of Structural Brain Connectivity via a Conductance Model

Frau-Pascual

Fogarty

Fischl

et al. 2018

Preprint

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Connectomics has proved promising in quantifying and understanding the effects of development, aging and an array of diseases on the brain. In this work, we propose a new structural connectivity measure from diffusion MRI that allows us to incorporate direct brain connections, as well as indirect ones that would not be otherwise accounted for by standard techniques and that may be key for the better understanding of function from structure. From our experiments on the Human Connectome Project dataset, we find that our measure of structural connectivity better correlates with functional connectivity than streamline tractography does, meaning that it provides new structural information related to function. Through additional experiments on the ADNI-2 dataset, we demonstrate the ability of this new measure to better discriminate different stages of Alzheimer's disease. Our findings suggest that this measure is useful in the study of the normal brain structure, and for quantifying the effects of disease on the brain structure.

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Q-Space Modeling in Diffusion-Weighted MRI

Cited by 3 publications

References 91 publications

Down-sampling in diffusion MRI: a bundle-specific DTI and NODDI study

Down-sampling in diffusion MRI: a bundle-specific DTI and NODDI study

Automatic Verification of the Gradient Table in Diffusion-Weighted MRI Based on Fiber Continuity

Quantification of Structural Brain Connectivity via a Conductance Model

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