A computational diffusion MRI and parametric dictionary learning framework for modeling the diffusion signal and its features

• We develop an parametric dictionary learning algorithm to recover the dMRI signal with a reduced number of measurements.
• We propose a computational framework to model continuous dMRI signals and analytically recover the EAP, the ODF.
• This approach indicates a much better accuracy in terms of reconstruction error compared to state-of-the-art approaches.

In this work, we first propose an original and efficient computational framework to model continuous diffusion MRI (dMRI) signals and analytically recover important diffusion features such as the Ensemble Average Propagator (EAP) and the Orientation Distribution Function (ODF). Then, we develop an efficient parametric dictionary learning algorithm and exploit the sparse property of a well-designed dictionary to recover the diffusion signal and its features with a reduced number of measurements. The properties and potentials of the technique are demonstrated using various simulations on synthetic data and on human brain data acquired from 7T and 3T scanners. It is shown that the technique can clearly recover the dMRI signal and its features with a much better accuracy compared to state-of-the-art approaches, even with a small and reduced number of measurements. In particular, we can accurately recover the ODF in regions of multiple fiber crossing, which could open new perspectives for some dMRI applications such as fiber tractography.

Figure optionsDownload high-quality image (301 K)Download as PowerPoint slide