Recurrent Inference Machines as inverse problem solvers for MR relaxometry
This work addresses the need for faster and more accurate relaxometry mapping in medical imaging, offering a flexible alternative for researchers and clinicians, though it is incremental as it extends RIMs from linear to non-linear inverse problems.
The paper tackles the problem of estimating T1 and T2 relaxometry maps in quantitative MRI by proposing Recurrent Inference Machines (RIMs) as a solution, resulting in improved accuracy and precision compared to Maximum Likelihood Estimator and ResNet methods, with inference speeds 150 times faster than MLE.
In this paper, we propose the use of Recurrent Inference Machines (RIMs) to perform T1 and T2 mapping. The RIM is a neural network framework that learns an iterative inference process based on the signal model, similar to conventional statistical methods for quantitative MRI (QMRI), such as the Maximum Likelihood Estimator (MLE). This framework combines the advantages of both data-driven and model-based methods, and, we hypothesize, is a promising tool for QMRI. Previously, RIMs were used to solve linear inverse reconstruction problems. Here, we show that they can also be used to optimize non-linear problems and estimate relaxometry maps with high precision and accuracy. The developed RIM framework is evaluated in terms of accuracy and precision and compared to an MLE method and an implementation of the ResNet. The results show that the RIM improves the quality of estimates compared to the other techniques in Monte Carlo experiments with simulated data, test-retest analysis of a system phantom, and in-vivo scans. Additionally, inference with the RIM is 150 times faster than the MLE, and robustness to (slight) variations of scanning parameters is demonstrated. Hence, the RIM is a promising and flexible method for QMRI. Coupled with an open-source training data generation tool, it presents a compelling alternative to previous methods.