MANTIS: Model‐Augmented Neural neTwork with Incoherent k‐space Sampling for efficient MR parameter mapping

Purpose To develop and evaluate a novel deep learning‐based image reconstruction approach called MANTIS (Model‐Augmented Neural neTwork with Incoherent k‐space Sampling) for efficient MR parameter mapping. Methods MANTIS combines end‐to‐end convolutional neural network (CNN) mapping, incoherent k‐space undersampling, and a physical model as a synergistic framework. The CNN mapping directly converts a series of undersampled images straight into MR parameter maps using supervised training. Signal model fidelity is enforced by adding a pathway between the undersampled k‐space and estimated parameter maps to ensure that the parameter maps produced synthesized k‐space consistent with the acquired undersampling measurements. The MANTIS framework was evaluated on the T2 mapping of the knee at different acceleration rates and was compared with 2 other CNN mapping methods and conventional sparsity‐based iterative reconstruction approaches. Global quantitative assessment and regional T2 analysis for the cartilage and meniscus were performed to demonstrate the reconstruction performance of MANTIS. Results MANTIS achieved high‐quality T2 mapping at both moderate (R = 5) and high (R = 8) acceleration rates. Compared to conventional reconstruction approaches that exploited image sparsity, MANTIS yielded lower errors (normalized root mean square error of 6.1% for R = 5 and 7.1% for R = 8) and higher similarity (structural similarity index of 86.2% at R = 5 and 82.1% at R = 8) to the reference in the T2 estimation. MANTIS also achieved superior performance compared to direct CNN mapping and a 2‐step CNN method. Conclusion The MANTIS framework, with a combination of end‐to‐end CNN mapping, signal model‐augmented data consistency, and incoherent k‐space sampling, is a promising approach for efficient and robust estimation of quantitative MR parameters.