{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T14:05:36Z","timestamp":1768485936772,"version":"3.49.0"},"reference-count":36,"publisher":"IOP Publishing","issue":"2","license":[{"start":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T00:00:00Z","timestamp":1743984000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T00:00:00Z","timestamp":1743984000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"crossref","award":["R01 CA240808"],"award-info":[{"award-number":["R01 CA240808"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>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 Fr\u00e9chet 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.<\/jats:p>","DOI":"10.1088\/2632-2153\/adc656","type":"journal-article","created":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T23:46:48Z","timestamp":1743119208000},"page":"025007","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["FDDM: unsupervised medical image translation with a frequency-decoupled diffusion model"],"prefix":"10.1088","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0622-4710","authenticated-orcid":true,"given":"Yunxiang","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7664-7257","authenticated-orcid":true,"given":"Hua-Chieh","family":"Shao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6185-1074","authenticated-orcid":true,"given":"Xiaoxue","family":"Qian","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8033-2755","authenticated-orcid":true,"given":"You","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2025,4,7]]},"reference":[{"key":"mlstadc656bib1","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.radonc.2019.03.026","article-title":"MRI-only brain radiotherapy: assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach","volume":"136","author":"Kazemifar","year":"2019","journal-title":"Radiother. Oncol."},{"key":"mlstadc656bib2","doi-asserted-by":"publisher","first-page":"S16","DOI":"10.1259\/bjr\/84072695","article-title":"Applications of magnetic resonance spectroscopy in radiotherapy treatment planning","volume":"79","author":"Payne","year":"2006","journal-title":"Br. J. Radiol."},{"key":"mlstadc656bib3","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1016\/0360-3016(92)90727-Y","article-title":"The clinical utility of magnetic resonance imaging in 3-dimensional treatment planning of brain neoplasms","volume":"24","author":"Thornton","year":"1992","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"mlstadc656bib4","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.1016\/0360-3016(79)90562-5","article-title":"The value of CT scanning in radiation therapy treatment planning: a prospective study","volume":"5","author":"Goitein","year":"1979","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"mlstadc656bib5","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.meddos.2005.05.001","article-title":"Determination of CT-to-density conversion relationship for image-based treatment planning systems","volume":"30","author":"Saw","year":"2005","journal-title":"Med. Dosim."},{"key":"mlstadc656bib6","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/acb889","article-title":"Real-time liver tumor localization via combined surface imaging and a single x-ray projection","volume":"68","author":"Shao","year":"2023","journal-title":"Phys. Med. Biol."},{"key":"mlstadc656bib7","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1038\/s41597-023-02237-5","article-title":"Children\u2019s dental panoramic radiographs dataset for caries segmentation and dental disease detection","volume":"10","author":"Zhang","year":"2023","journal-title":"Sci. Data"},{"key":"mlstadc656bib8","doi-asserted-by":"publisher","first-page":"6649","DOI":"10.1002\/mp.16691","article-title":"Real-time liver motion estimation via deep learning-based angle-agnostic x-ray imaging","volume":"50","author":"Shao","year":"2023","journal-title":"Med. Phys."},{"key":"mlstadc656bib9","doi-asserted-by":"publisher","first-page":"1468","DOI":"10.1002\/jmri.26271","article-title":"Emerging role of MRI in radiation therapy","volume":"48","author":"Chandarana","year":"2018","journal-title":"J. Magn. Reson. Imaging"},{"key":"mlstadc656bib10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13014-016-0747-y","article-title":"A review of substitute CT generation for mri-only radiation therapy","volume":"12","author":"Edmund","year":"2017","journal-title":"Radiat. Oncol."},{"key":"mlstadc656bib11","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1007\/978-3-031-27420-6_15","article-title":"Recurrence-free survival prediction under the guidance of automatic gross tumor volume segmentation for head and neck cancers","author":"Wang","year":"2022"},{"key":"mlstadc656bib12","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"mlstadc656bib13","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1561\/2200000056","article-title":"An introduction to variational autoencoders","volume":"vol 12","author":"Kingma","year":"2019"},{"key":"mlstadc656bib14","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295408","article-title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium","volume":"vol 30","author":"Heusel","year":"2017"},{"key":"mlstadc656bib15","doi-asserted-by":"publisher","first-page":"8780","DOI":"10.5555\/3540261.3540933","article-title":"Diffusion models beat gans on image synthesis","volume":"vol 34","author":"Dhariwal","year":"2021"},{"key":"mlstadc656bib16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3626235","article-title":"Diffusion models: a comprehensive survey of methods and applications","volume":"56","author":"Yang","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"mlstadc656bib17","article-title":"Score-based generative modeling through stochastic differential equations","author":"Song","year":"2021"},{"key":"mlstadc656bib18","article-title":"Denoising diffusion implicit models","author":"Song","year":"2021"},{"key":"mlstadc656bib19","doi-asserted-by":"publisher","first-page":"6840","DOI":"10.5555\/3495724.3496298","article-title":"Denoising diffusion probabilistic models","volume":"vol 33","author":"Ho","year":"2020"},{"key":"mlstadc656bib20","article-title":"Sdedit: image synthesis and editing with stochastic differential equations","author":"Meng","year":"2022"},{"key":"mlstadc656bib21","doi-asserted-by":"publisher","first-page":"3524","DOI":"10.1109\/TMI.2023.3290149","article-title":"Unsupervised medical image translation with adversarial diffusion models","author":"\u00d6zbey","year":"2023","journal-title":"IEEE Trans. Med. Imaging."},{"key":"mlstadc656bib22","doi-asserted-by":"publisher","first-page":"2223","DOI":"10.1109\/ICCV.2017.244","article-title":"Unpaired image-to-image translation using cycle-consistent adversarial networks","author":"Zhu","year":"2017"},{"key":"mlstadc656bib23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2019.101684","article-title":"MedGAN: medical image translation using gans","volume":"79","author":"Armanious","year":"2020","journal-title":"Comput. Med. Imaging Graph."},{"key":"mlstadc656bib24","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s12194-019-00520-y","article-title":"Overview of image-to-image translation by use of deep neural networks: denoising, super-resolution, modality conversion and reconstruction in medical imaging","volume":"12","author":"Kaji","year":"2019","journal-title":"Radiol. Phys. Technol."},{"key":"mlstadc656bib25","doi-asserted-by":"publisher","first-page":"2427","DOI":"10.1109\/CVPR.2019.00253","article-title":"Geometry-consistent generative adversarial networks for one-sided unsupervised domain mapping","author":"Fu","year":"2019"},{"key":"mlstadc656bib26","doi-asserted-by":"publisher","first-page":"1964","DOI":"10.5555\/3540261.3540412","article-title":"Breaking the dilemma of medical image-to-image translation","volume":"vol 34","author":"Kong","year":"2021"},{"key":"mlstadc656bib27","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294838","article-title":"Unsupervised image-to-image translation networks","volume":"vol 30","author":"Liu","year":"2017"},{"key":"mlstadc656bib28","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1007\/978-3-030-01219-9_11","article-title":"Multimodal unsupervised image-to-image translation","author":"Huang","year":"2018"},{"key":"mlstadc656bib29","doi-asserted-by":"publisher","first-page":"980","DOI":"10.1109\/TMI.2023.3325703","article-title":"Zero-shot medical image translation via frequency-guided diffusion models","volume":"43","author":"Li","year":"2023"},{"key":"mlstadc656bib30","doi-asserted-by":"publisher","first-page":"2256","DOI":"10.5555\/3045118.3045358","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","volume":"vol 37","author":"Sohl-Dickstein","year":"2015"},{"key":"mlstadc656bib31","doi-asserted-by":"publisher","first-page":"24174","DOI":"10.1109\/CVPR52733.2024.02282","article-title":"Analyzing and improving the training dynamics of diffusion models","author":"Karras","year":"2024"},{"key":"mlstadc656bib32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103823","article-title":"Multi-modal medical image fusion by Laplacian pyramid and adaptive sparse representation","volume":"123","author":"Wang","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"mlstadc656bib33","doi-asserted-by":"publisher","first-page":"4664","DOI":"10.1002\/mp.16529","article-title":"SynthRAD2023 grand challenge dataset: Generating synthetic CT for radiotherapy","volume":"50","author":"Thummerer","year":"2023","journal-title":"Med. Phys."},{"key":"mlstadc656bib34","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103276","article-title":"Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report","volume":"97","author":"Huijben","year":"2024","journal-title":"Med. Image Anal."},{"key":"mlstadc656bib35","first-page":"290","article-title":"Quantitative evaluation of bone density using the Hounsfield index","volume":"21","author":"Shapurian","year":"2006","journal-title":"Int. J. Oral Maxillofac. Implants"},{"key":"mlstadc656bib36","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1016\/j.mspro.2014.07.065","article-title":"Analysis of hounsfield unit of human bones for strength evaluation","volume":"6","author":"Khan","year":"2014","journal-title":"Proc. Mater. Sci."}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/adc656","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/adc656\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/adc656","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/adc656\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/adc656\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/adc656\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/adc656\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/adc656\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T11:53:49Z","timestamp":1744026829000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/adc656"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,7]]},"references-count":36,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,4,7]]},"published-print":{"date-parts":[[2025,6,30]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/adc656","relation":{},"ISSN":["2632-2153"],"issn-type":[{"value":"2632-2153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,7]]},"assertion":[{"value":"FDDM: unsupervised medical image translation with a frequency-decoupled diffusion model","name":"article_title","label":"Article Title"},{"value":"Machine Learning: Science and Technology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2025 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2024-11-22","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-03-27","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-04-07","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}