CT to Multi-parametric MR Synthesis using CycleGAN

Abstract

Medical images depict organs with different contrasts depending on their measurement techniques. In a clinical setting, patients may undergo multiple types of modalities for certain purposes. However, image acquisition by multiple types of modalities is time-consuming and not cost-effective. In this research, we address image synthesis, i.e. translating images such that they resemble the contrast of target modality. Image synthesis have long required “paired” training data, i.e. images of the same patients acquired with multiple modalities in the same postures, until CycleGAN has recently resolved this deficiency. CycleGAN enables Image Synthesis without paired data, learning synthesis toward each modality. Although CT-MR synthesis methods have been proposed so far, these only take into account MR images of single sequence. However, it is often the case that MR images of multiple sequences in the same posture are available. In this paper, we examine image synthesis between MR images of three types of sequences and CT around hip region using CycleGAN.