In a recent study published in Magnetic Resonance in Medicine, our AI team at the University of Wisconsin invented a novel AI framework for accelerating MR parameter mapping. We demonstrated the AI framework’s efficacy, efficiency, and robustness in resolving the challenges of performing rapid quantitative MR imaging. Among many MR techniques, quantitative mapping of MR parameters has always been shown as a powerful tool for improved assessment of various diseases. In contrast to conventional MRI, parameter mapping can provide increased sensitivity to tissue pathologies with more specific information on tissue composition and microstructure. However, standard approaches for estimating MR parameters usually obtain repeated acquisition of data sets with varying imaging parameters, requiring long scan times. Accelerated methods are highly desirable and remain a hot topic of great interest in the MR community.
To reconstruct MR parameter maps with less data, we invented MANTIS standing for Model‐Augmented Neural neTwork with Incoherent k‐space Sampling. Our MANTIS algorithm combines end‐to‐end convolutional neural network (CNN) mapping, model augmentation promoting data consistency, and incoherent k‐space undersampling as a synergistic framework. The CNN mapping converts a series of undersampled images straight into MR parameter maps, representing an efficient cross-domain transform learning. Signal model fidelity is enforced by connecting a pathway between the undersampled k‐space and estimated parameter maps to ensure that the algorithm constructs efficacious parameter maps consistent with the acquired k-space measurements. A randomized k-space undersampling strategy is tailored to create incoherent sampling patterns that are benign to the reconstruction network and adequate to characterize robust image features.
Our study demonstrated that the proposed MANTIS framework represents a promising approach for rapid T2 mapping in knee imaging with up to 8-fold acceleration. In future work, MANTIS can extend to other types of parameter mapping, such as T1 relaxation time, diffusion, and perfusion, with appropriate models and training data sets. We expect AI methods, including MANTIS, to advance quantitative MR imaging and bring more MR value.