Joint total variation‐based reconstruction of multiparametric magnetic resonance images for mapping tissue types

Multispectral analysis of coregistered multiparametric magnetic resonance (MR) images provides a powerful method for tissue phenotyping and segmentation. Acquisition of a sufficiently varied set of multicontrast MR images and parameter maps to objectively define multiple normal and pathologic tissue types can require long scan times. Accelerated MRI on clinical scanners with multichannel receivers exploits techniques such as parallel imaging, while accelerated preclinical MRI scanning must rely on alternate approaches. In this work, tumor‐bearing mice were imaged at 7 T to acquire k‐space data corresponding to a series of images with varying T1‐, T2‐ and T2*‐weighting. A joint reconstruction framework is proposed to reconstruct a series of T1‐weighted images and corresponding T1 maps simultaneously from undersampled Cartesian k‐space data. The ambiguity introduced by undersampling was resolved by using model‐based constraints and structural information from a reference fully sampled image as the joint total variation prior. This process was repeated to reconstruct T2‐weighted and T2*‐weighted images and corresponding maps of T2 and T2* from undersampled Cartesian k‐space data. Validation of the reconstructed images and parameter maps was carried out by computing tissue‐type maps, as well as maps of the proton density fat fraction (PDFF), proton density water fraction (PDwF), fat relaxation rate ( R2f*) and water relaxation rate ( R2w*) from the reconstructed data, and comparing them with ground truth (GT) equivalents. Tissue‐type maps computed using 18% k‐space data were visually similar to GT tissue‐type maps, with dice coefficients ranging from 0.43 to 0.73 for tumor, fluid adipose and muscle tissue types. The mean T1 and T2 values within each tissue type computed using only 18% k‐space data were within 8%–10% of the GT values from fully sampled data. The PDFF and PDwF maps computed using 27% k‐space data were within 3%–15% of GT values and showed good agreement with the expected values for the four tissue types. A joint reconstruction framework comprising joint total variation and model‐based constraints deriving structural information from a fully‐sampled reference image is proposed for reconstructing a series of T1‐weighted (w) images and corresponding T1 maps simultaneously from undersampled Cartesian k‐space data. The process is analogously repeated to reconstruct T2‐w and T2*‐w images and maps of T2 and T2*. Tissue‐type maps and values of PDFF and