Cas-DiffCom：用于婴儿纵向超分辨率3D医学图像补全的级联扩散模型
Cas-DiffCom: Cascaded diffusion model for infant longitudinal super-resolution 3D medical image completion
AI总结：
本文提出两阶段级联扩散模型Cas-DiffCom，用于解决婴儿纵向3D脑MRI数据缺失问题，在BCP数据集上实现个体一致且高保真的图像补全与超分辨率，并验证了其在脑组织分割和发育轨迹描绘等下游任务中的潜力。
摘要：
婴儿早期是行为和神经认知快速且动态的神经发育时期。纵向磁共振成像（MRI）通过捕捉大脑结构的发育轨迹，是研究这一关键阶段的有效工具。然而，由于参与者退出和扫描失败，纵向MRI采集总是面临严重的数据缺失问题，这使得纵向婴儿脑图谱构建和发育轨迹描绘变得相当具有挑战性。得益于基于AI的生成模型的发展，神经图像补全已成为尽可能保留可用数据的一种强大技术。然而，当前的图像补全方法通常在时间维度上存在每个个体受试者内部不一致的问题，从而损害整体质量。为解决这一问题，本文提出了一种两阶段级联扩散模型Cas-DiffCom，用于密集且纵向的3D婴儿脑MRI补全和超分辨率。我们将所提出的方法应用于Baby Connectome Project（BCP）数据集。实验结果验证了Cas-DiffCom在纵向婴儿脑图像补全中同时实现了个体一致性和高保真度。我们进一步将生成的婴儿脑图像应用于两个下游任务，即脑组织分割和发育轨迹描绘，以证明其在神经科学领域面向任务的潜力。
英文摘要：
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 super-resolution. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
