Diffusion-relaxation correlation spectroscopic imaging (DR-CSI) is a novel multidimensional magnetic resonance (MR) imaging approach that we recently introduced for probing microstructure. DR-CSI uses high-dimensional MR data that is acquired with spatial encoding and non-separable diffusion-relaxation contrast encoding, and uses constrained image reconstruction to estimate a spectroscopic image with a multidimensional spectrum for each voxel. The spectral peaks correspond to distinct compartmental microenvironments that coexist within each voxel. This paper reviews DR-CSI and describes and demonstrates its generalization to other contrast mechanisms (i.e., we demonstrate multidimensional relaxation (T1-T2) correlation spectroscopic imaging).
Low-rank modeling of local k-space neighborhoods (LORAKS) is a recent novel framework for constrained MRI reconstruction. LORAKS relies on embedding MRI data into carefully-constructed matrices, which will have low-rank structure when the MRI image has sparse support or slowly-varying phase. Low-rank matrix representation allows MRI images to be reconstructed from undersampled data using modern low-rank matrix techniques, and enables data acquisition strategies that are incompatible with more traditional representations. This paper reviews LORAKS, and describes extensions that allow LORAKS to additionally impose structured transform-domain sparsity constraints (e.g., structured sparsity of the image derivatives or wavelet coefficients).
In this paper, we present a fast iterative magnetic resonance imaging (MRI) reconstruction algorithm taking advantage of
the prevailing GPGPU programming paradigm. In clinical environment, MRI reconstruction is usually performed via
fast Fourier transform (FFT). However, imaging artifacts (i.e. signal loss) resulting from susceptibility-induced magnetic
field inhomogeneities degrade the quality of reconstructed images. These artifacts must be addressed using accurate
modeling of the physics of the system coupled with iterative reconstruction. We have developed a reconstruction
algorithm with improved image quality at the expense of computation time and hence an implementation on GPUs
achieving significant speedup. In this work, we extend our previous work on GPU implementation by adding several
new features. First, we enable Sensitivity Encoding for Fast MRI (SENSE) reconstruction (from data acquired using a
multi-receiver coil array) which can reduce the acquisition time. Besides, we have implemented a GPU-based total
variation regularization in our SENSE reconstruction framework. In this paper, we describe the different optimizations
employed from levels of algorithm, program code structures, and specific architecture performance tuning, featuring
both our MRI reconstruction algorithm and GPU hardware specifics. Results show that the current GPU implementation produces accurate image estimates while significantly accelerating the reconstruction.