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

Abstract

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 (R Ã 2f Þ and water relaxation rate (R Ã 2w Þ from the reconstructed data, and comparing them with ground truth (GT) equivalents.

Systematic review of reconstruction techniques for accelerated quantitative MRI

“…Solving the combined optimization of data fidelity and regularizer terms can be difficult and often a more relaxed version of the cost function has been used which is computationally easier to optimize. One of the commonly used algorithms in optimization of the regularized cost functions is the Alternate Direction Method of Multipliers 52,53,57,69,93,95,100,101,103 . Other techniques include the gradient descent algorithm, 72‐74 the Fast Composite Splitting Algorithm 61,82 and majorization–minimization‐based algorithms such as the fast iterative shrinkage‐thresholding algorithm 106 …”

“…Different transform operators 𝜙 and priors have been proposed to find a sparse representation of the images. This includes the wavelet transform, 27,[47][48][49][50][51][52] the total variation transform, 26,50,[52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68] group sparsity where images are divided into multiple sparse regions, 69 weighted quadratic prior that aims to suppress the noise and reconstruction artifacts based on the intensity differences between neighboring voxels, 56 gradient across the contrast dimension, 53,[70][71][72][73][74] second-order discrete derivative in the contrast dimension, 75,76 principal component analysis-based transform, 75,[77][78][79][80] image ratio constraints, where the ratio between a low-resolution image and the reconstructed image is used as a constraint, 50 and learned sparsifying transform 𝜙 from the measurements. 81 Apart from these, alternative ways to use regularizers and transform domains have been proposed.…”

“…One of the commonly used algorithms in optimization of the regularized cost functions is the Alternate Direction Method of Multipliers. 52,53,57,69,93,95,100,101,103 Other techniques include the gradient descent algorithm, [72][73][74] the Fast Composite Splitting Algorithm 61,82 and majorization-minimization-based algorithms such as the fast iterative shrinkage-thresholding algorithm. 106…”