Accelerated brain tumor dynamic contrast-enhanced MRI using Adaptive Pharmaco-Kinetic Model Constrained method

In brain tumor, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) spatiotemporally resolved high-quality reconstruction which is required for quantitative analysis of some physiological characteristics of brain tissue. By exploiting some kind of sparsity priori, compressed sensing methods can achieve high spatiotemporal DCE-MRI image reconstruction from undersampled k-space data. Recently, as a kind of priori information about the contrast agent (CA) concentration dynamics, Pharmacokinetic (PK) models have been explored for undersampled DCE-MRI reconstruction. This paper presents a novel dictionary learning-based reconstruction method with Adaptive Pharmaco-Kinetic Model Constraints (APKMC). In APKMC, the priori knowledge about CA dynamics is incorporated into a novel dictionary, which consists of PK model-based atoms and adaptive atoms. The PK atoms are constructed based on Patlak model and K-SVD dimension reduction algorithm, and the adaptive ones are used to resolve PK model inconsistencies. To solve APKMC, an optimization algorithm based on variable splitting and alternating iterative optimization is presented. The proposed method has been validated on three brain tumor DCE-MRI data sets by comparing with two state-of-the-art methods. As demonstrated by the quantitative and qualitative analysis of results, APKMC achieved substantially better quality in the reconstruction of brain DCE-MRI images, as well as in the reconstruction of PK model parameter maps.