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.
|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
|Conference||2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing|
|Abbreviated title||CAMSAP 2019|
|Period||15/12/19 → 18/12/19|
Pesce, M., Repetti, A., & Wiaux, Y. (Accepted/In press). Fast Spatially Coherent Fiber Orientation Estimation in Diffusion MRI from kq-Space Sampling. Paper presented at 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Guadeloupe, Guadeloupe.