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Abstract
High spatio-angular resolution diffusion MRI (dMRI) has been shown to provide accurate identification of complex neuronal fiber configurations, albeit, at the cost of long acquisition times. We propose a method to recover intra-voxel fiber configurations at high spatio-angular resolution relying on a 3D kq-space under-sampling scheme to enable accelerated acquisitions. The inverse problem for the reconstruction of the fiber orientation distribution (FOD) is regularized by a structured sparsity prior promoting simultaneously voxel-wise sparsity and spatial smoothness of fiber orientation. Prior knowledge of the spatial distribution of white matter, gray matter, and cerebrospinal fluid is also leveraged. A minimization problem is formulated and solved via a stochastic forward–backward algorithm. Simulations and real data analysis suggest that accurate FOD mapping can be achieved from severe kq-space under-sampling regimes potentially enabling high spatio-angular resolution dMRI in the clinical setting.
Highlights
Diffusion Magnetic Resonance Imaging is a unique non-invasive technique to infer the microscopic architecture of tissues in vivo
A first analysis of the fiber orientation distribution (FOD) reconstruction using the proposed kq-space under-sampling method was performed relying on the fiber configuration of the numerical phantom proposed in the ISMRM Tractography Challenge 2015 [56]
We developed a method to accelerate high angular and spatial resolution Diffusion Magnetic Resonance Imaging (dMRI) acquisition relying on a 3D kq-space under-sampling scheme
Summary
Diffusion Magnetic Resonance Imaging (dMRI) is a unique non-invasive technique to infer the microscopic architecture of tissues in vivo. DMRI has gained a lot of attention in neuroscience since it enables the mapping of the white matter fiber paths, revealing the existing connections between different brain areas [1,2,3]. Diffusion MRI enables to map the restricted diffusion of the water molecules comprising the white matter tissue. The information captured by dMRI is processed in order to infer the connectivity and the integrity of the white matter pathways. A typical approach to trace the complex pathways of the white matter fiber bundles from dMRI signals relies on piecing together local fiber orientation information. Such local information is obtained by processing a multitude of MRI signals generated by applying various diffusion gradients during the acquisition. Each diffusion-weighted (DW) signal is sensitive to diffusion along a specific direction and at a specific intensity, identified by a so-called q-space point defined by the diffusion gradient applied [6]
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