A Path Towards Clinical Adaptation of Accelerated MRI
This work addresses practical challenges for deploying accelerated MRI in clinical settings, but it is incremental as it builds on existing deep learning methods.
The authors tackled the problem of making accelerated MRI reconstruction more clinically relevant by addressing issues like artifact detection, variable acceleration factors, multi-anatomy learning, and limited data. They achieved a 79.1% F2 score for artifact detection and up to a 2% performance improvement in clinical scans.
Accelerated MRI reconstructs images of clinical anatomies from sparsely sampled signal data to reduce patient scan times. While recent works have leveraged deep learning to accomplish this task, such approaches have often only been explored in simulated environments where there is no signal corruption or resource limitations. In this work, we explore augmentations to neural network MRI image reconstructors to enhance their clinical relevancy. Namely, we propose a ConvNet model for detecting sources of image artifacts that achieves a classifier $F_2$ score of 79.1%. We also demonstrate that training reconstructors on MR signal data with variable acceleration factors can improve their average performance during a clinical patient scan by up to 2%. We offer a loss function to overcome catastrophic forgetting when models learn to reconstruct MR images of multiple anatomies and orientations. Finally, we propose a method for using simulated phantom data to pre-train reconstructors in situations with limited clinically acquired datasets and compute capabilities. Our results provide a potential path forward for clinical adaptation of accelerated MRI.