MICCAI 2022 Daily – Tuesday

The framework combines four neural networks with a forward projection and a filtered back projection, using bidirectional mapping with multiple closed cycles. These cycles can further serve as cycle- consistent constraints to keep the anatomical structures consistent in the synthesis process for better performance. “ Synthesising CT image from PET image is challenging because some anatomical structures are not visible in PET image, ” Jiadong tells us. “ When using other medical image synthesis methods, such as CycleGAN, the anatomical structure in dual-domain is inconsistent, negatively impacting the results. Our work addresses this issue. ” Jiadong points out that they have designed a general framework that could be used for many other applications. “ So far, we have only explored our framework on PET-CT synthesis, ” he says. “ Next, we want to explore the performance of this method on other tasks, such as low-dose PET reconstruction, low-dose CT reconstruction, and multimodal MR synthesis. We will report related results in our future journal paper. ” Or a paper at MICCAI next year? 15 DAILY MICCAI Tuesday Jiadong Zhang