Multi-Spherical Diffusion MRI: Exploring Diffusion Time Using Signal Sparsity

Fick, Rutger; Petiet, Alexandra; Santin, Mathieu; Philippe, Anne-Charlotte; Lehericy, Stéphane; Deriche, Rachid; Wassermann, Demián

doi:10.1007/978-3-319-54130-3_6

Cited by 4 publications

(5 citation statements)

References 22 publications

(37 reference statements)

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“…Expansions have been proposed such that all the acquired samples lie on different non-collinear directions [10]. This multi-shell design can be extended to τ -shells, called qτ acquisitions [13] in order to exploit different values for both q and τ . In that case, a complete q-shell scheme is acquired for each desired diffusion time.…”

Section: Acquisition Strategiesmentioning

confidence: 99%

Single Encoding Diffusion MRI: A Probe to Brain Anisotropy

Jallais

Wassermann

2021

Mathematics and Visualization

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This chapter covers anisotropy in the context of probing microstructure of the human brain using single encoded diffusion MRI. We will start by illustrating how diffusion MRI is a perfectly adapted technique to measure anisotropy in the human brain using water motion, followed by a biological presentation of human brain. The non-invasive imaging technique based on water motions known as diffusion MRI will be further presented, along with the difficulties that come with it. Within this context, we will first review and discuss methods based on signal representation that enable us to get an insight into microstructure anisotropy. We will then outline methods based on modeling, which are state-of-the-art methods to get parameter estimations of the human brain tissue.

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Section: Acquisition Strategiesmentioning

confidence: 99%

Single Encoding Diffusion MRI: A Probe to Brain Anisotropy

Jallais

Wassermann

2021

Mathematics and Visualization

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“…We reconstruct the continuous EAP from a finite set of DWIs by representing the discretely measured attenuation E ( q , τ) in terms of the basis coefficients c of a “Multi‐Spherical” 4D q τ‐Fourier basis . The q τ‐basis is formed by the cross‐product of a 3D q‐space basis Φ i ( q ) and 1D diffusion time basis T j (τ) .…”

Section: Theorymentioning

confidence: 99%

“…We reconstruct the continuous EAP from a finite set of DWIs by representing the discretely measured attenuation E(q, τ) in terms of the basis coefficients c of a "Multi-Spherical" 4D qτ-Fourier basis. 45 The qτ-basis is formed by the crossproduct of a 3D q-space basis Φ i (q) 36 and 1D diffusion time basis T j (τ). 14 The approximated signal attenuation Ê (q, τ, c) is given as where N q and N τ are the maximum expansion orders of spatial and temporal bases, respectively, and c ij are the weights of the contribution of the ij th basis function to Ê (q, τ,c).…”

Section: Graphnet Regularizationmentioning

confidence: 99%

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Reducing the number of samples in spatiotemporal dMRI acquisition design

Filipiak

Fick

Petiet³

et al. 2018

Magnetic Resonance in Med

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Purpose Acquisition time is a major limitation in recovering brain white matter microstructure with diffusion magnetic resonance imaging. The aim of this paper is to bridge the gap between growing demands on spatiotemporal resolution of diffusion signal and the real‐world time limitations. The authors introduce an acquisition scheme that reduces the number of samples under adjustable quality loss. Methods Finding a sampling scheme that maximizes signal quality and satisfies given time constraints is NP‐hard. Therefore, a heuristic method based on genetic algorithm is proposed in order to find suboptimal solutions in acceptable time. The analyzed diffusion signal representation is defined in the qτ space, so that it captures both spacial and temporal phenomena. Results The experiments on synthetic data and in vivo diffusion images of the C57Bl6 wild‐type mouse corpus callosum reveal superiority of the proposed approach over random sampling and even distribution in the qτ space. Conclusions The use of genetic algorithm allows to find acquisition parameters that guarantee high signal reconstruction accuracy under given time constraints. In practice, the proposed approach helps to accelerate the acquisition for the use of qτ‐dMRI signal representation.

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“…This concept can be expanded among shells such that all of the acquired samples lie on different non-collinear directions [Caruyer et al, 2013]. The multi-shell concept can be extended to τ-shells, called a qτ-acquisition [Fick et al, 2016b], since nowadays there exist signal representations that exploit different value for both q and τ. In this case, a complete q-shell scheme -with samples distributed along different gradient directions and with different diffusion-weightings -is acquired for each desired diffusion time.…”

Section: Measurements Of Diffusion With Diffusion-weighted Mrimentioning

confidence: 99%

Diffusion MRI Anisotropy: Modeling, Analysis and Interpretation

Fick

Pizzolato

Wassermann

et al. 2017

Mathematics and Visualization

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The micro-architecture of brain tissue obstructs the movement of diffusing water molecules, causing tissue-dependent, often anisotropic diffusion profiles. In diffusion MRI (dMRI), the relation between brain tissue structure and diffusion anisotropy is studied using oriented diffusion gradients, resulting in tissueand orientation-dependent diffusion-weighted images (DWIs). Over time, various methods have been proposed that summarize these DWIs, that can be measured at different orientations, gradient strengths and diffusion times into one "diffusion anisotropy" measure. This book chapter is dedicated to understanding the similarities and differences between the diffusion anisotropy metrics that different methods estimate. We first discuss the physical interpretation of diffusion anisotropy in terms of the diffusion properties around nervous tissue. We then explain how DWIs are influenced by diffusion anisotropy and the parameters of the dMRI acquisition itself. We then go through the state-of-the-art of signal-based and multi-compartmentbased dMRI methods that estimate diffusion anisotropy-related methods, focusing on their limitations and applications. We finally discuss confounding factors in the estimation of diffusion anisotropy and current challenges.

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Multi-Spherical Diffusion MRI: Exploring Diffusion Time Using Signal Sparsity

Cited by 4 publications

References 22 publications

Single Encoding Diffusion MRI: A Probe to Brain Anisotropy

Single Encoding Diffusion MRI: A Probe to Brain Anisotropy

Reducing the number of samples in spatiotemporal dMRI acquisition design

Diffusion MRI Anisotropy: Modeling, Analysis and Interpretation

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