FDDM: unsupervised medical image translation with a frequency-decoupled diffusion model
Diffusion models have demonstrated significant potential in producing high-quality images in medical image translation to aid disease diagnosis, localization, and treatment. Nevertheless, current diffusion models often fall short when it comes to faithfully translating medical images. They struggle...
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/adc656 |
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| author | Yunxiang Li Hua-Chieh Shao Xiaoxue Qian You Zhang |
| author_facet | Yunxiang Li Hua-Chieh Shao Xiaoxue Qian You Zhang |
| author_sort | Yunxiang Li |
| collection | DOAJ |
| description | Diffusion models have demonstrated significant potential in producing high-quality images in medical image translation to aid disease diagnosis, localization, and treatment. Nevertheless, current diffusion models often fall short when it comes to faithfully translating medical images. They struggle to accurately preserve anatomical structures, especially when working with unpaired datasets. In this study, we introduce the frequency decoupled diffusion model (FDDM) for magnetic resonance (MR)-to-computed tomography (CT) conversion. The differences between MR and CT images lie in both anatomical structures (e.g. the outlines of organs or bones) and the data distribution (e.g. intensity values and contrast within). Therefore, FDDM first converts anatomical information using an initial conversion module. Then, the converted anatomical information guides a subsequent diffusion model to generate high-quality CT images. Our diffusion model uses a dual-path reverse diffusion process for low-frequency and high-frequency information, achieving a better balance between image quality and anatomical accuracy. We extensively evaluated FDDM using two public datasets for brain MR-to-CT and pelvis MR-to-CT translations. The results show that FDDM outperforms generative adversarial network (GAN)-based, variational autoencoder (VAE)-based, and diffusion-based models. The evaluation metrics included Fréchet inception distance (FID), mean absolute error, mean squared error, structural similarity index measure, and Dice similarity coefficient (DICE). FDDM achieved the best scores on all metrics for both datasets, particularly excelling in FID, with scores of 25.9 for brain data and 29.2 for pelvis data, significantly outperforming the other methods. These results demonstrate that FDDM can generate high-quality target domain images while maintaining the accuracy of translated anatomical structures, thereby facilitating more precise/accurate downstream tasks including anatomy segmentation and radiotherapy planning. |
| format | Article |
| id | doaj-art-8fd7fde79b4f43f88d3aaa5e71ce2a0e |
| institution | OA Journals |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-8fd7fde79b4f43f88d3aaa5e71ce2a0e2025-08-20T02:08:12ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016202500710.1088/2632-2153/adc656FDDM: unsupervised medical image translation with a frequency-decoupled diffusion modelYunxiang Li0https://orcid.org/0000-0003-0622-4710Hua-Chieh Shao1https://orcid.org/0009-0003-7664-7257Xiaoxue Qian2https://orcid.org/0000-0002-6185-1074You Zhang3https://orcid.org/0000-0002-8033-2755Department of Radiation Oncology, UT Southwestern Medical Center , Dallas, TX 75390, United States of AmericaDepartment of Radiation Oncology, UT Southwestern Medical Center , Dallas, TX 75390, United States of AmericaDepartment of Radiation Oncology, UT Southwestern Medical Center , Dallas, TX 75390, United States of AmericaDepartment of Radiation Oncology, UT Southwestern Medical Center , Dallas, TX 75390, United States of AmericaDiffusion models have demonstrated significant potential in producing high-quality images in medical image translation to aid disease diagnosis, localization, and treatment. Nevertheless, current diffusion models often fall short when it comes to faithfully translating medical images. They struggle to accurately preserve anatomical structures, especially when working with unpaired datasets. In this study, we introduce the frequency decoupled diffusion model (FDDM) for magnetic resonance (MR)-to-computed tomography (CT) conversion. The differences between MR and CT images lie in both anatomical structures (e.g. the outlines of organs or bones) and the data distribution (e.g. intensity values and contrast within). Therefore, FDDM first converts anatomical information using an initial conversion module. Then, the converted anatomical information guides a subsequent diffusion model to generate high-quality CT images. Our diffusion model uses a dual-path reverse diffusion process for low-frequency and high-frequency information, achieving a better balance between image quality and anatomical accuracy. We extensively evaluated FDDM using two public datasets for brain MR-to-CT and pelvis MR-to-CT translations. The results show that FDDM outperforms generative adversarial network (GAN)-based, variational autoencoder (VAE)-based, and diffusion-based models. The evaluation metrics included Fréchet inception distance (FID), mean absolute error, mean squared error, structural similarity index measure, and Dice similarity coefficient (DICE). FDDM achieved the best scores on all metrics for both datasets, particularly excelling in FID, with scores of 25.9 for brain data and 29.2 for pelvis data, significantly outperforming the other methods. These results demonstrate that FDDM can generate high-quality target domain images while maintaining the accuracy of translated anatomical structures, thereby facilitating more precise/accurate downstream tasks including anatomy segmentation and radiotherapy planning.https://doi.org/10.1088/2632-2153/adc656medical image translationdiffusion modelgenerative model |
| spellingShingle | Yunxiang Li Hua-Chieh Shao Xiaoxue Qian You Zhang FDDM: unsupervised medical image translation with a frequency-decoupled diffusion model Machine Learning: Science and Technology medical image translation diffusion model generative model |
| title | FDDM: unsupervised medical image translation with a frequency-decoupled diffusion model |
| title_full | FDDM: unsupervised medical image translation with a frequency-decoupled diffusion model |
| title_fullStr | FDDM: unsupervised medical image translation with a frequency-decoupled diffusion model |
| title_full_unstemmed | FDDM: unsupervised medical image translation with a frequency-decoupled diffusion model |
| title_short | FDDM: unsupervised medical image translation with a frequency-decoupled diffusion model |
| title_sort | fddm unsupervised medical image translation with a frequency decoupled diffusion model |
| topic | medical image translation diffusion model generative model |
| url | https://doi.org/10.1088/2632-2153/adc656 |
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