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.

翻译：磁共振（MR）图像重参数化是指通过模拟生成具有新扫描参数的MR图像的过程。不同参数值会在不同组织间产生对比度差异，有助于识别病变组织。通常，诊断需要多次扫描；然而，重复采集扫描既昂贵又耗时，且给患者带来不便。因此，利用MR图像重参数化来预测和估计这些成像扫描中的对比度可成为一种有效的替代方案。在本研究中，我们提出了一种新颖的基于深度学习（DL）的卷积模型用于MRI重参数化。基于初步结果，深度学习技术具有学习控制重参数化的非线性关系的潜力。