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

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Abstract

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 T 2 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 T 2 analysis for the cartilage and meniscus were performed to demonstrate the reconstruction performance of MANTIS. Results MANTIS achieved high‐quality T 2 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 T 2 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.

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