MRI reconstruction of multi-image acquisitions using a rank regularizer with data reordering.

PURPOSE: To improve rank constrained reconstructions for undersampled multi-image MRI acquisitions. METHODS: Motivated by the recent developments in low-rank matrix completion theory and its applicability to rapid dynamic MRI, a new reordering-based rank constrained reconstruction of undersampled multi-image data that uses prior image information is proposed. Instead of directly minimizing the nuclear norm of a matrix of estimated images, the nuclear norm of reordered matrix values is minimized. The reordering is based on the prior image estimates. The method is tested on brain diffusion imaging data and dynamic contrast enhanced myocardial perfusion data. RESULTS: Good quality images from data undersampled by a factor of three for diffusion imaging and by a factor of 3.5 for dynamic cardiac perfusion imaging with respiratory motion were obtained. Reordering gave visually improved image quality over standard nuclear norm minimization reconstructions. Root mean squared errors with respect to ground truth images were improved by ∼18% and ∼16% with reordering for diffusion and perfusion applications, respectively. CONCLUSIONS: The reordered low-rank constraint is a way to inject prior image information that offers improvements over a standard low-rank constraint for undersampled multi-image MRI reconstructions.

Assessment of white matter microstructure in stroke patients using NODDI.

Diffusion weighted imaging (DWI) is widely used to study changes in white matter following stroke. In various studies employing diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI) modalities, it has been shown that fractional anisotropy (FA), mean diffusivity (MD), and generalized FA (GFA) can be used as measures of white matter tract integrity in stroke patients. However, these measures may be non-specific, as they do not directly delineate changes in tissue microstructure. Multi-compartment models overcome this limitation by modeling DWI data using a set of indices that are directly related to white matter microstructure. One of these models which is gaining popularity, is neurite orientation dispersion and density imaging (NODDI). This model uses conventional single or multi-shell HARDI data to describe fiber orientation dispersion as well as densities of different tissue types in the imaging voxel. In this paper, we apply for the first time the NODDI model to 4-shell HARDI stroke data. By computing NODDI indices over the entire brain in two stroke patients, and comparing tissue regions in ipsilesional and contralesional hemispheres, we demonstrate that NODDI modeling provides specific information on tissue microstructural changes. We also introduce an information theoretic analysis framework to investigate the non-local effects of stroke in the white matter. Our initial results suggest that the NODDI indices might be more specific markers of white matter reorganization following stroke than other measures previously used in studies of stroke recovery.