Fast Spatially Coherent Fiber Orientation Estimation in Diffusion MRI from kq-Space Sampling

Marica Pesce; Audrey Repetti; Yves Wiaux

Fast Spatially Coherent Fiber Orientation Estimation in Diffusion MRI from kq-Space Sampling

Marica Pesce, Audrey Repetti, Yves Wiaux

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Abstract

Diffusion Magnetic Resonance Imaging is a state-of-the-art technique that can provide accurate identification of complex neuronal fiber configurations in the human brain. Typical acquisition times are however too long for the clinical application. We propose a method to recover the fiber orientation distribution (FOD) at high spatio-angular resolution via practical kq-space under-sampling patterns that enable both acceleration and super-resolution. The inverse problem for FOD reconstruction is regularized by a structured sparsity prior promoting simultaneously voxelwise sparsity and spatial smoothness of fiber orientations. A convex minimization problem is formulated and solved via a forward-backward algorithm. Real data analysis suggest that high spatio-angular resolution FOD mapping can be achieved from severe kq-space acceleration.

Original language	English
Number of pages	5
Publication status	Accepted/In press - 16 Sep 2019
Event	2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Le Gosier, Guadeloupe, Guadeloupe Duration: 15 Dec 2019 → 18 Dec 2019 https://camsap19.ig.umons.ac.be/

Conference

Conference	2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Abbreviated title	CAMSAP 2019
Country	Guadeloupe
City	Guadeloupe
Period	15/12/19 → 18/12/19
Internet address	https://camsap19.ig.umons.ac.be/

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Accepted author manuscript, 4.81 MB

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title = "Fast Spatially Coherent Fiber Orientation Estimation in Diffusion MRI from kq-Space Sampling",

abstract = "Diffusion Magnetic Resonance Imaging is a state-of-the-art technique that can provide accurate identification of complex neuronal fiber configurations in the human brain. Typical acquisition times are however too long for the clinical application. We propose a method to recover the fiber orientation distribution (FOD) at high spatio-angular resolution via practical kq-space under-sampling patterns that enable both acceleration and super-resolution. The inverse problem for FOD reconstruction is regularized by a structured sparsity prior promoting simultaneously voxelwise sparsity and spatial smoothness of fiber orientations. A convex minimization problem is formulated and solved via a forward-backward algorithm. Real data analysis suggest that high spatio-angular resolution FOD mapping can be achieved from severe kq-space acceleration.",

author = "Marica Pesce and Audrey Repetti and Yves Wiaux",

year = "2019",

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day = "16",

language = "English",

note = "2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 ; Conference date: 15-12-2019 Through 18-12-2019",

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T1 - Fast Spatially Coherent Fiber Orientation Estimation in Diffusion MRI from kq-Space Sampling

AU - Pesce, Marica

AU - Repetti, Audrey

AU - Wiaux, Yves

PY - 2019/9/16

Y1 - 2019/9/16

N2 - Diffusion Magnetic Resonance Imaging is a state-of-the-art technique that can provide accurate identification of complex neuronal fiber configurations in the human brain. Typical acquisition times are however too long for the clinical application. We propose a method to recover the fiber orientation distribution (FOD) at high spatio-angular resolution via practical kq-space under-sampling patterns that enable both acceleration and super-resolution. The inverse problem for FOD reconstruction is regularized by a structured sparsity prior promoting simultaneously voxelwise sparsity and spatial smoothness of fiber orientations. A convex minimization problem is formulated and solved via a forward-backward algorithm. Real data analysis suggest that high spatio-angular resolution FOD mapping can be achieved from severe kq-space acceleration.

AB - Diffusion Magnetic Resonance Imaging is a state-of-the-art technique that can provide accurate identification of complex neuronal fiber configurations in the human brain. Typical acquisition times are however too long for the clinical application. We propose a method to recover the fiber orientation distribution (FOD) at high spatio-angular resolution via practical kq-space under-sampling patterns that enable both acceleration and super-resolution. The inverse problem for FOD reconstruction is regularized by a structured sparsity prior promoting simultaneously voxelwise sparsity and spatial smoothness of fiber orientations. A convex minimization problem is formulated and solved via a forward-backward algorithm. Real data analysis suggest that high spatio-angular resolution FOD mapping can be achieved from severe kq-space acceleration.

M3 - Paper

T2 - 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing

Y2 - 15 December 2019 through 18 December 2019

ER -