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| Cas-DiffCom: Cascaded Diffusion Model for Infant Longitudinal Super-Resolution 3D Medical Image Completion | |
| 2024-05-30 | |
| 会议录名称 | 2024 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI)
 |
| ISSN | 1945-7928 |
| 发表状态 | 已发表 |
| DOI | 10.1109/ISBI56570.2024.10635663 |
| 摘要 | Early infancy is a rapid and dynamic neurodevelopmental period for behavior and neurocognition. Longitudinal magnetic resonance imaging (MRI) is an effective tool to investigate such a crucial stage by capturing the developmental trajectories of the brain structures. However, longitudinal MRI acquisition always meets a serious data-missing problem due to participant dropout and failed scans, making longitudinal infant brain atlas construction and developmental trajectory delineation quite challenging. Thanks to the development of an AI-based generative model, neuroimage completion has become a powerful technique to retain as much available data as possible. However, current image completion methods usually suffer from inconsistency within each individual subject in the time dimension, compromising the overall quality. To solve this problem, our paper proposed a two-stage cascaded diffusion model, Cas-DiffCom, for dense and longitudinal 3D infant brain MRI completion and superresolution. We applied our proposed method to the Baby Connectome Project (BCP) dataset. The experiment results validate that Cas-DiffCom achieves both individual consistency and high fidelity in longitudinal infant brain image completion. We further applied the generated infant brain images to two downstream tasks, brain tissue segmentation and developmental trajectory delineation, to declare its task-oriented potential in the neuroscience field. |
| 会议录编者/会议主办者 | AI2D Center ; et al. ; Therapanacea ; Thermo Fisher Scientific ; United Imaging Intelligence ; Verasonics |
| 关键词 | Brain mapping Diffusion tensor imaging Image segmentation 3D medical image Brain images Brain structure Diffusion model Effective tool Image completion Infant development Medical imaging completion Neurocognition Superresolution |
| 会议名称 | 21st IEEE International Symposium on Biomedical Imaging, ISBI 2024 |
| 会议地点 | Athens, Greece |
| 会议日期 | 27-30 May 2024 |
| URL | 查看原文 |
| 收录类别 | EI |
| 语种 | 英语 |
| 出版者 | IEEE Computer Society |
| EI入藏号 | 20243717024220 |
| EI主题词 | Magnetic resonance imaging |
| EISSN | 1945-8452 |
| EI分类号 | 101.1 ; 1106.3.1 ; 709 Electrical Engineering, General ; 746 Imaging Techniques |
| 原始文献类型 | Conference article (CA) |
| 来源库 | IEEE |
| 文献类型 | 会议论文 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/414234 |
| 专题 | 信息科学与技术学院_硕士生 生物医学工程学院_PI研究组_沈定刚组 生物医学工程学院_公共科研平台_智能医学科研平台 生物医学工程学院_PI研究组_王乾组 生物医学工程学院_PI研究组_张寒组 生物医学工程学院_硕士生 |
| 作者单位 | 1.School of Biomedical Engineering & State Key Laboratory of Advanced Materials and Devices, ShanghaiTech University, Shanghai, China 2.Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China 3.Shanghai Clinical Research and Trial Center, Shanghai, China |
| 第一作者单位 | 上海科技大学 |
| 第一作者的第一单位 | 上海科技大学 |
| 推荐引用方式 GB/T 7714 | Lianghu Guo,Tianli Tao,Xinyi Cai,et al. Cas-DiffCom: Cascaded Diffusion Model for Infant Longitudinal Super-Resolution 3D Medical Image Completion[C]//AI2D Center, et al., Therapanacea, Thermo Fisher Scientific, United Imaging Intelligence, Verasonics:IEEE Computer Society,2024. |
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