Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models

• URL: http://arxiv.org/abs/2209.12104v1
• Date: Sat, 24 Sep 2022 23:50:54 GMT
• Title: Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models
• Authors: Qing Lyu and Ge Wang
• Abstract summary: We propose to use an emerging deep learning framework called diffusion and score-matching models. Our results show that the diffusion and score-matching models generate better synthetic CT images than the CNN and GAN models. Our study suggests that diffusion and score-matching models are powerful to generate high quality images conditioned on an image obtained using a complementary imaging modality.
• Score: 7.745729132928934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: MRI and CT are most widely used medical imaging modalities. It is often necessary to acquire multi-modality images for diagnosis and treatment such as radiotherapy planning. However, multi-modality imaging is not only costly but also introduces misalignment between MRI and CT images. To address this challenge, computational conversion is a viable approach between MRI and CT images, especially from MRI to CT images. In this paper, we propose to use an emerging deep learning framework called diffusion and score-matching models in this context. Specifically, we adapt denoising diffusion probabilistic and score-matching models, use four different sampling strategies, and compare their performance metrics with that using a convolutional neural network and a generative adversarial network model. Our results show that the diffusion and score-matching models generate better synthetic CT images than the CNN and GAN models. Furthermore, we investigate the uncertainties associated with the diffusion and score-matching networks using the Monte-Carlo method, and improve the results by averaging their Monte-Carlo outputs. Our study suggests that diffusion and score-matching models are powerful to generate high quality images conditioned on an image obtained using a complementary imaging modality, analytically rigorous with clear explainability, and highly competitive with CNNs and GANs for image synthesis.

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