Bi-modality medical images synthesis by a bi-directional discrete process matching method
This work addresses a computational bottleneck in medical image synthesis for clinical and research applications, though it appears incremental as it builds on existing flow-based models.
The paper tackles the computational inefficiency of flow-based models for medical image synthesis by proposing Bi-DPM, a bi-directional discrete process matching method that reduces time iterations while maintaining high-quality generation. Experiments on MRI T1/T2 and CT/MRI datasets show it outperforms state-of-the-art flow-based methods in image quality and anatomical accuracy.
Recently, medical image synthesis gains more and more popularity, along with the rapid development of generative models. Medical image synthesis aims to generate an unacquired image modality, often from other observed data modalities. Synthesized images can be used for clinical diagnostic assistance, data augmentation for model training and validation or image quality improving. In the meanwhile, the flow-based models are among the successful generative models for the ability of generating realistic and high-quality synthetic images. However, most flow-based models require to calculate flow ordinary different equation (ODE) evolution steps in synthesis process, for which the performances are significantly limited by heavy computation time due to a large number of time iterations. In this paper, we propose a novel flow-based model, namely bi-directional Discrete Process Matching (Bi-DPM) to accomplish the bi-modality image synthesis tasks. Different to other flow matching based models, we propose to utilize both forward and backward ODE flows and enhance the consistency on the intermediate images over a few discrete time steps, resulting in a synthesis process maintaining high-quality generations for both modalities under the guidance of paired data. Our experiments on three datasets of MRI T1/T2 and CT/MRI demonstrate that Bi-DPM outperforms other state-of-the-art flow-based methods for bi-modality image synthesis, delivering higher image quality with accurate anatomical regions.