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GPDM: generation-prior diffusion model for accelerated direct attenuation and scatter correction of whole-body18F-FDG PET.

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Τίτλος: GPDM: generation-prior diffusion model for accelerated direct attenuation and scatter correction of whole-body18F-FDG PET.
Συγγραφείς: Jeong Cho M; Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Republic of Korea.; Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul, Republic of Korea.; Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea., Seok Shim H; Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Republic of Korea.; Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul, Republic of Korea.; Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea., Kim S; Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea., Sung Lee J; Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Republic of Korea.; Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul, Republic of Korea.; Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.; Brightonix Imaging Inc., Seoul, Republic of Korea.
Πηγή: Physics in medicine and biology [Phys Med Biol] 2026 Jun 19; Vol. 71 (12). Date of Electronic Publication: 2026 Jun 19.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: IOP Publishing Country of Publication: England NLM ID: 0401220 Publication Model: Electronic Cited Medium: Internet ISSN: 1361-6560 (Electronic) Linking ISSN: 00319155 NLM ISO Abbreviation: Phys Med Biol Subsets: MEDLINE
Imprint Name(s): Original Publication: Bristol : IOP Publishing
Ιατρικοί όροι (MeSH): Image Processing, Computer-Assisted*/methods , Fluorodeoxyglucose F18* , Positron-Emission Tomography* , Scattering, Radiation* , Whole Body Imaging*, Diffusion ; Generative Adversarial Networks ; Humans
Περίληψη: Objective.Accurate attenuation and scatter correction (ASC) is essential in positron emission tomography (PET) for reliable visual interpretation and quantitative analysis. Conventional correction methods based on computed tomography or magnetic resonance imaging have limitations related to accuracy, radiation exposure, and practical applicability. Although deep neural networks, particularly generative adversarial network (GAN)-based approaches, have shown promise for generating attenuation- and scatter-corrected PET (ASC PET) images from non-attenuation and non-scatter-corrected PET (NASC PET) images, their performance is still limited by instability during training and mode collapse. This study proposes a novel framework to generate high-quality ASC PET images from NASC PET images.Approach.We developed a generation-prior diffusion model (GPDM) for ASC PET image generation. The proposed framework is based on the denoising diffusion probabilistic model, but instead of initiating sampling from a completely unrelated image distribution, it starts from a distribution similar to the target ASC PET images. This prior distribution, termed thegeneration-prior(GP), guides the sampling process toward the desired image domain. By leveraging this prior information, GPDM reduces the number of required sampling steps and improves the refinement of the generated ASC PET images.Main results.Experimental results demonstrated that GPDM outperformed existing methods for generating ASC PET images from NASC PET images. The proposed framework achieved higher image generation accuracy while substantially reducing sampling time. These results indicate that incorporating a GP into the diffusion process improves both efficiency and image quality compared with conventional approaches.Significance.The proposed GPDM framework addresses key limitations of conventional GAN-based and diffusion-based methods for ASC in PET imaging. By enabling more efficient and accurate generation of ASC PET images from NASC PET images, GPDM has the potential to provide a robust alternative to conventional correction strategies and to advance practical PET imaging workflows.
(Creative Commons Attribution license.)
Contributed Indexing: Keywords: PET; diffusion model; generation-prior; joint attenuation and scatter correction
Substance Nomenclature: 0Z5B2CJX4D (Fluorodeoxyglucose F18)
Entry Date(s): Date Created: 20260603 Date Completed: 20260619 Latest Revision: 20260625
Update Code: 20260625
DOI: 10.1088/1361-6560/ae7798
PMID: 42235583
Βάση Δεδομένων: MEDLINE
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  Data: GPDM: generation-prior diffusion model for accelerated direct attenuation and scatter correction of whole-body18F-FDG PET.
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  Data: <searchLink fieldCode="AU" term="%22Jeong+Cho+M%22">Jeong Cho M</searchLink>; Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Republic of Korea.; Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul, Republic of Korea.; Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Seok+Shim+H%22">Seok Shim H</searchLink>; Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Republic of Korea.; Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul, Republic of Korea.; Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Kim+S%22">Kim S</searchLink>; Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Sung+Lee+J%22">Sung Lee J</searchLink>; Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Republic of Korea.; Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul, Republic of Korea.; Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.; Brightonix Imaging Inc., Seoul, Republic of Korea.
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  Data: <searchLink fieldCode="JN" term="%220401220%22">Physics in medicine and biology</searchLink> [Phys Med Biol] 2026 Jun 19; Vol. 71 (12). <i>Date of Electronic Publication: </i>2026 Jun 19.
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  Data: <i>Original Publication</i>: Bristol : IOP Publishing
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  Data: <searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted%22">Image Processing, Computer-Assisted*</searchLink>/<searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Fluorodeoxyglucose+F18%22">Fluorodeoxyglucose F18*</searchLink> <br /><searchLink fieldCode="MM" term="%22Positron-Emission+Tomography%22">Positron-Emission Tomography*</searchLink> <br /><searchLink fieldCode="MM" term="%22Scattering%2C+Radiation%22">Scattering, Radiation*</searchLink> <br /><searchLink fieldCode="MM" term="%22Whole+Body+Imaging%22">Whole Body Imaging*</searchLink><br /><searchLink fieldCode="MH" term="%22Diffusion%22">Diffusion</searchLink> ; <searchLink fieldCode="MH" term="%22Generative+Adversarial+Networks%22">Generative Adversarial Networks</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Objective.Accurate attenuation and scatter correction (ASC) is essential in positron emission tomography (PET) for reliable visual interpretation and quantitative analysis. Conventional correction methods based on computed tomography or magnetic resonance imaging have limitations related to accuracy, radiation exposure, and practical applicability. Although deep neural networks, particularly generative adversarial network (GAN)-based approaches, have shown promise for generating attenuation- and scatter-corrected PET (ASC PET) images from non-attenuation and non-scatter-corrected PET (NASC PET) images, their performance is still limited by instability during training and mode collapse. This study proposes a novel framework to generate high-quality ASC PET images from NASC PET images.Approach.We developed a generation-prior diffusion model (GPDM) for ASC PET image generation. The proposed framework is based on the denoising diffusion probabilistic model, but instead of initiating sampling from a completely unrelated image distribution, it starts from a distribution similar to the target ASC PET images. This prior distribution, termed thegeneration-prior(GP), guides the sampling process toward the desired image domain. By leveraging this prior information, GPDM reduces the number of required sampling steps and improves the refinement of the generated ASC PET images.Main results.Experimental results demonstrated that GPDM outperformed existing methods for generating ASC PET images from NASC PET images. The proposed framework achieved higher image generation accuracy while substantially reducing sampling time. These results indicate that incorporating a GP into the diffusion process improves both efficiency and image quality compared with conventional approaches.Significance.The proposed GPDM framework addresses key limitations of conventional GAN-based and diffusion-based methods for ASC in PET imaging. By enabling more efficient and accurate generation of ASC PET images from NASC PET images, GPDM has the potential to provide a robust alternative to conventional correction strategies and to advance practical PET imaging workflows.<br /> (Creative Commons Attribution license.)
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  Data: <i>Keywords: </i>PET; diffusion model; generation-prior; joint attenuation and scatter correction
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        Text: English
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        Type: general
      – SubjectFull: Generative Adversarial Networks
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      – SubjectFull: Whole Body Imaging
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      – TitleFull: GPDM: generation-prior diffusion model for accelerated direct attenuation and scatter correction of whole-body18F-FDG PET.
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              Text: 2026 Jun 19
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