Accelerating MR parameter mapping using sparsity‐promoting regularization in parametric dimension

Abstract

MR parameter mapping requires sampling along additional (parametric) dimension, which often limits its clinical appeal due to a several-fold increase in scan times compared to conventional anatomic imaging. Data undersampling combined with parallel imaging is an attractive way to reduce scan time in such applications. However, inherent SNR penalties of parallel MRI due to noise amplification often limit its utility even at moderate acceleration factors, requiring regularization by prior knowledge. In this work, we propose a novel regularization strategy, which utilizes smoothness of signal evolution in the parametric dimension within compressed sensing framework (p-CS) to provide accurate and precise estimation of parametric maps from undersampled data. The performance of the method was demonstrated with variable flip angle T1 mapping and compared favorably to two representative reconstruction approaches, image space-based total variation regularization and an analytical model-based reconstruction. The proposed p-CS regularization was found to provide efficient suppression of noise amplification and preservation of parameter mapping accuracy without explicit utilization of analytical signal models. The developed method may facilitate acceleration of quantitative MRI techniques that are not suitable to model-based reconstruction because of complex signal models or when signal deviations from the expected analytical model exist.

T2 shuffling: Sharp, multicontrast, volumetric fast spin‐echo imaging

“…1 The nuclear norm is applied to each matrix with regularization parameter λ and the result is summed. LLR regularization exploits spatial correlations in the temporal image coefficients, providing substantial dimensionality reduction beyond the capabilities of joint wavelet regularization or finite differences (21, 32, 44, 46). In effect, LLR constrains local image patches into a smaller space within the subspace Φ K , which compresses the representation to fewer than K coefficients per voxel.…”

Accelerated High-Dimensional MR Imaging With Sparse Sampling Using Low-Rank Tensors

“…Furthermore, the effects of different trade-offs in bias and variance for different approaches to exploit the tensor structure need to be investigated for particular applications, which is beyond the scope of this paper. Comparison of low-rank tensor imaging to other alternative high-dimensional imaging methods should also be investigated on an application-by-application basis (e.g., comparison to other multiparameter mapping methods [61], [62]).…”

Exploiting parameter sparsity in model-based reconstruction to accelerate proton density and T2 mapping

“…Prior information, mostly the image sparsity [7] and spatiotemporal partial separability [8], has been exploited to constrain the solution space of the desired image function from the undersampled data. Based on similar assumptions (i.e., sparsity and partial separability), a number of sparse reconstruction methods [9][10][11][12][13][14][15] have been developed with various variations regarding the image model, sparsifying transform, regularization, etc. Specifically, authors in literature [9] proposed to learn an overcomplete dictionary to sparsify the signal.…”

“…The approach was verified in T 1 and T 2 mapping in the brain with highly reduced data. The study in literature [10] used the smoothness of signal evolution in the parametric dimension to accelerate variable flip angle T 1 mapping. Similar idea was developed in literature [13] to enable fast T 1 mapping of the mouse heart.…”