A model‐based reconstruction for undersampled radial spin‐echo DTI with variational penalties on the diffusion tensor

Abstract

Radial spin echo diffusion imaging allows motion-robust imaging of tissues with very low T2 values like articular cartilage with high spatial resolution and signal-to-noise ratio (SNR). However, in vivo measurements are challenging due to the significantly slower data acquisition speed of spin-echo sequences and the less efficient k-space coverage of radial sampling, which raises the demand for accelerated protocols by means of undersampling. This work introduces a new reconstruction approach for undersampled DTI. A model-based reconstruction implicitly exploits redundancies in the diffusion weighted images by reducing the number of unknowns in the optimization problem and compressed sensing is performed directly in the target quantitative domain by imposing a Total Variation (TV) constraint on the elements of the diffusion tensor. Experiments were performed for an anisotropic phantom and the knee and brain of healthy volunteers (3 and 2 volunteers, respectively). Evaluation of the new approach was conducted by comparing the results to reconstructions performed with gridding, combined parallel imaging and compressed sensing, and a recently proposed model-based approach. The experiments demonstrated improvement in terms of reduction of noise and streaking artifacts in the quantitative parameter maps as well as a reduction of angular dispersion of the primary eigenvector when using the proposed method, without introducing systematic errors into the maps. This may enable an essential reduction of the acquisition time in radial spin echo diffusion tensor imaging without degrading parameter quantification and/or SNR.

Compressed sensing for body MRI

“…Moreover, motion models, either rigid or non-rigid, can also be integrated to guide the image reconstruction and to determine motion fields for certain applications (73). Other examples include T 1 and T 2 encoding (101), diffusion encoding (102), and flow encoding (103). These models are expected to improve the sparsity conditions and the performance of sparsity-enforcing reconstructions.…”

Direct diffusion tensor estimation using a model‐based method with spatial and parametric constraints

“…subsequently proposed a regularized MB method with a total variance constraint (MB‐TV) on the DW images, and Knoll et al. directly added variational penalties on the diffusion tensor components . The latter approach has been demonstrated to be superior in performance to the former for preserving fine details in MD maps.…”

Spatially resolved kinetics of skeletal muscle exercise response and recovery with multiple echo diffusion tensor imaging (MEDITI): a feasibility study

“…Prior studies in the literature can help guide choice of regularization parameters [44,34]. While we did not incorporate the diffusion tensor solution itself into the compressed sensing reconstruction process, prescriptions exist in the literature for performing such an integration and potentially benefitting from higher dimensional sparsity [45]. The novelty of the present workflow lies in achieving a novel combination of accelerated diffusion encoding and compressed sensing image reconstruction.…”