  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.
