{"title":"低剂量CT图像去噪的泊松流一致性模型","authors":"Dennis Hein;Grant Stevens;Adam Wang;Ge Wang","doi":"10.1109/TMI.2025.3558019","DOIUrl":null,"url":null,"abstract":"X-ray computed tomography (CT) is widely used for medical diagnosis and treatment planning; however, concerns about ionizing radiation exposure drive efforts to optimize image quality at lower doses. This study introduces Poisson Flow Consistency Models (PFCM), a novel family of deep generative models that combines the robustness of PFGM++ with the efficient single-step sampling of consistency models. PFCM are derived by generalizing consistency distillation to PFGM++ through a change-of-variables and an updated noise distribution. As a distilled version of PFGM++, PFCM inherit the ability to trade off robustness for rigidity via the hyperparameter <inline-formula> <tex-math>$\\text {D} \\in \\text {(}{0},\\infty \\text {)}$ </tex-math></inline-formula>. A fact that we exploit to adapt this novel generative model for the task of low-dose CT image denoising, via a “task-specific” sampler that “hijacks” the generative process by replacing an intermediate state with the low-dose CT image. While this “hijacking” introduces a severe mismatch—the noise characteristics of low-dose CT images are different from that of intermediate states in the Poisson flow process—we show that the inherent robustness of PFCM at small D effectively mitigates this issue. The resulting sampler achieves excellent performance in terms of LPIPS, SSIM, and PSNR on the Mayo low-dose CT dataset. By contrast, an analogous sampler based on standard consistency models is found to be significantly less robust under the same conditions, highlighting the importance of a tunable D afforded by our novel framework. To highlight generalizability, we show effective denoising of clinical images from a prototype photon-counting system reconstructed using a sharper kernel and at a range of energy levels.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 7","pages":"2989-3001"},"PeriodicalIF":0.0000,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"PFCM: Poisson Flow Consistency Models for Low-Dose CT Image Denoising\",\"authors\":\"Dennis Hein;Grant Stevens;Adam Wang;Ge Wang\",\"doi\":\"10.1109/TMI.2025.3558019\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"X-ray computed tomography (CT) is widely used for medical diagnosis and treatment planning; however, concerns about ionizing radiation exposure drive efforts to optimize image quality at lower doses. This study introduces Poisson Flow Consistency Models (PFCM), a novel family of deep generative models that combines the robustness of PFGM++ with the efficient single-step sampling of consistency models. PFCM are derived by generalizing consistency distillation to PFGM++ through a change-of-variables and an updated noise distribution. As a distilled version of PFGM++, PFCM inherit the ability to trade off robustness for rigidity via the hyperparameter <inline-formula> <tex-math>$\\\\text {D} \\\\in \\\\text {(}{0},\\\\infty \\\\text {)}$ </tex-math></inline-formula>. A fact that we exploit to adapt this novel generative model for the task of low-dose CT image denoising, via a “task-specific” sampler that “hijacks” the generative process by replacing an intermediate state with the low-dose CT image. While this “hijacking” introduces a severe mismatch—the noise characteristics of low-dose CT images are different from that of intermediate states in the Poisson flow process—we show that the inherent robustness of PFCM at small D effectively mitigates this issue. The resulting sampler achieves excellent performance in terms of LPIPS, SSIM, and PSNR on the Mayo low-dose CT dataset. By contrast, an analogous sampler based on standard consistency models is found to be significantly less robust under the same conditions, highlighting the importance of a tunable D afforded by our novel framework. To highlight generalizability, we show effective denoising of clinical images from a prototype photon-counting system reconstructed using a sharper kernel and at a range of energy levels.\",\"PeriodicalId\":94033,\"journal\":{\"name\":\"IEEE transactions on medical imaging\",\"volume\":\"44 7\",\"pages\":\"2989-3001\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE transactions on medical imaging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10963840/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on medical imaging","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10963840/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PFCM: Poisson Flow Consistency Models for Low-Dose CT Image Denoising
X-ray computed tomography (CT) is widely used for medical diagnosis and treatment planning; however, concerns about ionizing radiation exposure drive efforts to optimize image quality at lower doses. This study introduces Poisson Flow Consistency Models (PFCM), a novel family of deep generative models that combines the robustness of PFGM++ with the efficient single-step sampling of consistency models. PFCM are derived by generalizing consistency distillation to PFGM++ through a change-of-variables and an updated noise distribution. As a distilled version of PFGM++, PFCM inherit the ability to trade off robustness for rigidity via the hyperparameter $\text {D} \in \text {(}{0},\infty \text {)}$ . A fact that we exploit to adapt this novel generative model for the task of low-dose CT image denoising, via a “task-specific” sampler that “hijacks” the generative process by replacing an intermediate state with the low-dose CT image. While this “hijacking” introduces a severe mismatch—the noise characteristics of low-dose CT images are different from that of intermediate states in the Poisson flow process—we show that the inherent robustness of PFCM at small D effectively mitigates this issue. The resulting sampler achieves excellent performance in terms of LPIPS, SSIM, and PSNR on the Mayo low-dose CT dataset. By contrast, an analogous sampler based on standard consistency models is found to be significantly less robust under the same conditions, highlighting the importance of a tunable D afforded by our novel framework. To highlight generalizability, we show effective denoising of clinical images from a prototype photon-counting system reconstructed using a sharper kernel and at a range of energy levels.