Deep Learning-Based MR Image Re-parameterization
This addresses the issue of costly and time-consuming repeated MRI scans for diagnosis, though it appears incremental as it applies existing deep learning methods to a specific medical imaging task.
The paper tackles the problem of generating MR images with new scanning parameters to avoid repeated scans, proposing a deep learning-based convolutional model that shows potential in learning the non-linearities for re-parameterization.
Magnetic resonance (MR) image re-parameterization refers to the process of generating via simulations of an MR image with a new set of MRI scanning parameters. Different parameter values generate distinct contrast between different tissues, helping identify pathologic tissue. Typically, more than one scan is required for diagnosis; however, acquiring repeated scans can be costly, time-consuming, and difficult for patients. Thus, using MR image re-parameterization to predict and estimate the contrast in these imaging scans can be an effective alternative. In this work, we propose a novel deep learning (DL) based convolutional model for MRI re-parameterization. Based on our preliminary results, DL-based techniques hold the potential to learn the non-linearities that govern the re-parameterization.