Abstract -Positron emission tomography (PET) has been substantially used recently. To minimize the potential health risk caused by the tracer radiation inherent to PET scans, it is of great interest to synthesize the high-quality PET image from the low-dose one to reduce the radiation exposure. In this paper, we propose a 3D auto-context-based locality adaptive multi-modality generative adversarial networks model (LA-GANs) to synthesize the high-quality FDG PET image from the low-dose one with the accompanying MRI images that provide anatomical information. Our work has four contributions. First, different from the traditional methods that treat each image modality as an input channel and apply the same kernel to convolve the whole image, we argue that the contributions of different modalities could vary at different image locations, and therefore a unified kernel for a whole image is not optimal. To address this issue, we propose a locality adaptive strategy for multimodality fusion. Second, we utilize 1 × 1 × 1 kernel to learn this locality adaptive fusion so that the number of additional parameters incurred by our method is kept minimum. Third, the proposed locality adaptive fusion mechanism is learned jointly with the PET image synthesis in a 3D conditional GANs model, which generates high-quality PET images by employing large-sized image patches and hierarchical Index Terms-Image synthesis, positron emission topography (PET), generative adversarial networks (GANs), locality adaptive fusion, multi-modality.
Wang et al. REF also demonstrated promising results when using a GAN to estimate high-dose PET images from low-dose ones.
3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis
IEEE Transactions on Medical Imaging
IEEE Transactions on Medical Imaging
Computer Science Mathematics Medicine