Image-to-image translation consists in translating one image from one input domain to another, (e.g. from grayscale to colour). Many AI-based approaches have been published in the literature, mostly based on generative adversarial networks (GANs). In medical imaging, one of the investigated applications is modality translation (e.g. from MR to CT images [Nie et al., IEEE Trans Biomed Eng. 2018, 65(12): 2720-2730]). Other studies have focused on multi-input image translation, where for instance a set of brain MR images acquired with different modalities are translated into a different one [Joyce et al., MICCAI 2017, Part III, LNCS 10435, 347-355]. However, it is still unclear if these techniques are reliable enough for clinical applications.
In this project, the application of GAN-based image translation in cardiac MR perfusion imaging will be explored. Perfusion MR imaging, specifically first-pass perfusion and late Gadolinium enhancement (LGE), are currently the reference techniques to assess the presence of cardiac perfusion abnormalities (i.e. ischemic and infarcted myocardial regions). However, they require the injection of an external contrast medium, which has been associated with rare but life-threatening side effects. Recent studies have suggested that T1 mapping (a contrast-free MR modality) can produce images sensitive to perfusion defects [Liu et al., JACC Cardiovasc Imaging. 2016, 9(1): 27-36]. The aim of this project is to use deep generative models to perform image translation from T1 mapping to LGE images (potentially in a multi-input setting taking into account other modalities like cardiac tagging or cine) for contrast-free detection of cardiac perfusion defects. A successful model will have a high impact within the field of cardiac imaging, and potentially lead to a change in clinical perfusion imaging.