{"type":"ScholarlyArticle","title":"A Prior-information-guided Residual Diffusion Model for Multi-modal PET Synthesis from MRI","authors":[{"name":"Zaixin Ou","slug":"zaixin-ou","given":"Zaixin","family":"Ou","affiliation":"School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices , ShanghaiTech University , Shanghai , China"},{"name":"Caiwen Jiang","slug":"caiwen-jiang","given":"Caiwen","family":"Jiang","affiliation":"School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices , ShanghaiTech University , Shanghai , China"},{"name":"Yongsheng Pan","slug":"yongsheng-pan","given":"Yongsheng","family":"Pan","affiliation":"School of Computer Science and Engineering , Northwestern Polytechnical University , Xi'an , China"},{"name":"Yuanwang Zhang","slug":"yuanwang-zhang","given":"Yuanwang","family":"Zhang","affiliation":"School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices , ShanghaiTech University , Shanghai , China"},{"name":"Zhiming Cui","slug":"zhiming-cui","given":"Zhiming","family":"Cui","affiliation":"School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices , ShanghaiTech University , Shanghai , China"},{"name":"Dinggang Shen","slug":"dinggang-shen","given":"Dinggang","family":"Shen","affiliation":"School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices , ShanghaiTech University , Shanghai , China; Shanghai United Imaging Intelligence Co., Ltd. , Shanghai , China; Shanghai Clinical Research and Trial Center , Shanghai , China"}],"abstract":"Alzheimer's disease (AD) leads to abnormalities in various biomarkers (i.e., amyloid-β and tau proteins), which makes PET imaging (which can detect these biomarkers) essential in AD diagnosis. However, the high radiation risk of PET imaging limits its scanning number within a short period, presenting challenges to the joint multi-biomarker diagnosis of AD. In this paper, we propose a novel unified model to simultaneously synthesize multi-modal PET images from MRI, to achieve low-cost and time-efficient joint multi-biomarker diagnosis of AD. Specifically, we incorporate residual learning into the diffusion model to emphasize inter-domain differences between PET and MRI, thereby forcing each modality to maximally reconstruct its modality-specific details. Furthermore, we leverage prior information, such as age and gender, to guide the diffusion model in synthesizing PET images with semantic consistency, enhancing their diagnostic value. Additionally, we develop an intra-domain difference loss to ensure that the intra-domain differences among synthesized PET images closely match those among real PET images, promoting more accurate synthesis, especially at the modality-specific information. Extensive experiments conducted on the ADNI dataset demonstrate that our method achieves superior performance both quantitatively and qualitatively compared to the state-of-the-art methods. All codes for this study have been uploaded to GitHub (https://github.com/Ouzaixin/ResDM).","doi":"10.24963/ijcai.2024/527","url":"https://ijcai.org/proceedings/2024/527","pdf":"https://ijcai.org/proceedings/2024/0527.pdf","markdown":"https://ijcai.org/proceedings/2024/527.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/527","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":527,"pages":{"first":4769,"last":4777},"keywords":["Machine Learning: ML: Generative models","Machine Learning: ML: Multi-modal learning","Machine Learning: ML: Supervised Learning","Machine Learning: ML: Deep learning architectures"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2021] Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, Ehsan Adeli, Yan Wang, Le Lu, Alan L Yuille, and Yuyin Zhou. 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