Bloch simulator–driven deep recurrent neural network for magnetization transfer contrast MR fingerprinting and CEST imaging

Abstract

Purpose To develop a unified deep‐learning framework by combining an ultrafast Bloch simulator and a semisolid macromolecular magnetization transfer contrast (MTC) MR fingerprinting (MRF) reconstruction for estimation of MTC effects. Methods The Bloch simulator and MRF reconstruction architectures were designed with recurrent neural networks and convolutional neural networks, evaluated with numerical phantoms with known ground truths and cross‐linked bovine serum albumin phantoms, and demonstrated in the brain of healthy volunteers at 3 T. In addition, the inherent magnetization‐transfer ratio asymmetry effect was evaluated in MTC‐MRF, CEST, and relayed nuclear Overhauser enhancement imaging. A test–retest study was performed to evaluate the repeatability of MTC parameters, CEST, and relayed nuclear Overhauser enhancement signals estimated by the unified deep‐learning framework. Results Compared with a conventional Bloch simulation, the deep Bloch simulator for generation of the MTC‐MRF dictionary or a training data set reduced the computation time by 181‐fold, without compromising MRF profile accuracy. The recurrent neural network–based MRF reconstruction outperformed existing methods in terms of reconstruction accuracy and noise robustness. Using the proposed MTC‐MRF framework for tissue‐parameter quantification, the test–retest study showed a high degree of repeatability in which the coefficients of variance were less than 7% for all tissue parameters. Conclusion Bloch simulator–driven, deep‐learning MTC‐MRF can provide robust and repeatable multiple‐tissue parameter quantification in a clinically feasible scan time on a 3T scanner.

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“…Thus methods including artificial intelligence/machine learning will become increasingly important [54] . AI/ML approaches for designing appropriate data collection strategies [83] and assessing large numbers (>4) of tissue properties from multi-dimensional data [74] , [84] , [85] have already been demonstrated. Specialized analysis, e.g., through artificial intelligence, may be capable of extracting such nuanced information for enhanced diagnosis, risk prediction, and therapy monitoring.…”

Highly‐accelerated CEST MRI using frequency‐offset‐dependent k‐space sampling and deep‐learning reconstruction

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