Magic DIAMOND: Multi-fascicle diffusion compartment imaging with tensor distribution modeling and tensor-valued diffusion encoding

Reymbaut, Alexis; Caron, Alex Valcourt; Gilbert, Guillaume; Szczepankiewicz, Filip; Nilsson, Markus; Warfield, Simon K.; Descoteaux, Maxime; Scherrer, Benoît

doi:10.1016/j.media.2021.101988

Cited by 11 publications

(11 citation statements)

References 112 publications

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“…Tensor-valued diffusion acquisition schemes have since been used to further investigate signal representations 50,[57][58][59] and models. 32,[60][61][62][63][64] The advent of tensor-valued diffusion measurements has resulted in the development of nonparametric Monte Carlo signal inversion algorithms of the 2D diffusion 65 and 6D diffusion-T 1 -T 2 12 NMR signals in porous media, and of the 4D diffusion 66 and 5D diffusion-T 2 13 MRI signals in the in vivo brain. Although noise sensitive, 36 these algorithms do not rely on any compartmental/functional assumption regarding the voxel content, nor on any regularization [67][68][69][70] narrowing the space of suitable solutions to the inverse problem.…”

Section: Introductionmentioning

confidence: 99%

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

Reymbaut

Critchley²,

Durighel³

et al. 2020

Magnetic Resonance in Med

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Section: Introductionmentioning

confidence: 99%

“…[3,55,56]. Tensor‐valued diffusion acquisition schemes have since been used to further investigate signal representations 50,57‐59 and models 32,60‐64 …”

Section: Introductionmentioning

confidence: 99%

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

Reymbaut

Critchley²,

Durighel³

et al. 2020

Magnetic Resonance in Med

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“…Tensor-valued diffusion encoding has been used to study the tissue microstructure in healthy brain [43,88,91,97,98,[111][112][113][114][115][116]. Because of fiber crossings and the orientation dispersion, the FA measure extracted from conventional diffusion MRI is not able to show the changes in the microscopic level properly.…”

Section: Healthy Brainmentioning

confidence: 99%

“…All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication. [88]; [43,111]; [112,113]; [91]; [97,98,114]; [115]; [116]…”

Section: Author Contributionsmentioning

confidence: 99%

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

et al. 2022

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Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive technique to probe tissue microstructure. Conventional Stejskal–Tanner diffusion encoding (i.e., encoding along a single axis), is unable to disentangle different microstructural features within a voxel; If a voxel contains microcompartments that vary in more than one attribute (e.g., size, shape, orientation), it can be difficult to quantify one of those attributes in isolation using Stejskal–Tanner diffusion encoding. Multidimensional diffusion encoding, in which the water diffusion is encoded along multiple directions in q-space (characterized by the so-called “b-tensor”) has been proposed previously to solve this problem. The shape of the b-tensor can be used as an additional encoding dimension and provides sensitivity to microscopic anisotropy. This has been applied in multiple organs, including brain, heart, breast, kidney and prostate. In this work, we discuss the advantages of using b-tensor encoding in different organs.

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“…Several studies have accounted for sub-voxel WM fascicle orientations while employing tensor-valued diffusion encoding to capture the aforementioned diffusion measures [Cottaar et al, 2020;Reymbaut et al, 2020aReymbaut et al, ,b, 2021de Almeida Martins et al, 2021]. However, much remains to be done in evaluating the effects of various diffusion encodings on fODF reconstruction with CSD.…”

Section: Introductionmentioning

confidence: 99%

Enabling constrained spherical deconvolution and diffusional variance decomposition with tensor-valued diffusion MRI

Karan

Reymbaut

Gilbert

et al. 2021

Preprint

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Diffusion tensor imaging (DTI) is widely used to extract valuable tissue measurements and white matter (WM) fiber orientations, even though its lack of specificity is now well-known, especially for WM fiber crossings. Models such as constrained spherical deconvolution (CSD) take advantage of HARDI data to compute fiber orientation distribution functions (fODF) and tackle the orientational part of the DTI limitations. Furthermore, the recent introduction of tensor-valued diffusion MRI allows for diffusional variance decomposition (DIVIDE), opening the door to the computation of measures more specific to microstructure than DTI measures, such as microscopic fractional anisotropy (μFA). However, tensor-valued diffusion MRI data is not compatible with latest versions of CSD and the impacts of such atypical data on fODF reconstruction with CSD are yet to be studied. In this work, we lay down the mathematical and computational foundations of a tensor-valued CSD and use simulated data to explore the effects of various combinations of diffusion encodings on the angular resolution of extracted fOFDs. We also compare the combinations with regards to their performance at producing accurate and precise μFA with DIVIDE, and present an optimised protocol for both methods. We show that our proposed protocol enables the reconstruction of both fODFs and μFA on in vivo data.

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Magic DIAMOND: Multi-fascicle diffusion compartment imaging with tensor distribution modeling and tensor-valued diffusion encoding

Cited by 11 publications

References 112 publications

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

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

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

Enabling constrained spherical deconvolution and diffusional variance decomposition with tensor-valued diffusion MRI

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