Title: DLBayesian: An Alternative Bayesian Reconstruction of Limited-view CT by Optimizing Deep Learning Parameters Show Chinese title

Title: DLBayesian：通过优化深度学习参数的有限视图CT的替代贝叶斯重建

Abstract: Limited-view computed tomography (CT) presents significant potential for reducing radiation exposure and expediting the scanning process. While deep learning (DL) methods have exhibited promising results in mitigating streaking artifacts caused by a reduced number of projection views, their generalization remains challenging. In this work, we proposed a DL-driven alternative Bayesian reconstruction method (DLBayesian) that efficiently integrates data-driven priors and data consistency constraints. DLBayesian comprises three stages: group-level embedding, significance evaluation, and individual-level consistency adaptation. Firstly, DL network parameters are optimized to learn how to eliminate the general limited-view artifacts on a large-scale paired dataset. Then, we introduced a significance score to quantitatively evaluate the contribution of parameters in DL models as a guide for the subsequent individual-level adaptation. Finally, in the Bayesian adaptation stage, an alternative Bayesian reconstruction further optimizes the DL network parameters precisely according to the projection data of the target case. We validated DLBayesian with sparse-view (90 views) projections from a circular trajectory CT and a special data missing case from a multi-segment linear trajectory CT. The results underscore DLBayesian's superior generalization capabilities across variations in patients, anatomic structures, and data distribution, as well as excelling in contextual structure recovery compared to networks solely trained via supervised loss. Real experiments on a dead rat demonstrate its capability in practical CT scans. Show Chinese abstract

Abstract: 有限视角计算机断层扫描（CT）在减少辐射暴露和加快扫描过程方面具有显著潜力。 虽然深度学习（DL）方法在减轻由于投影视图数量减少引起的条纹伪影方面表现出有希望的结果，但其泛化能力仍然具有挑战性。 在本工作中，我们提出了一种由深度学习驱动的替代贝叶斯重建方法（DLBayesian），该方法能够高效地整合数据驱动先验和数据一致性约束。 DLBayesian包括三个阶段：组级嵌入、重要性评估和个体级一致性适应。 首先，DL网络参数被优化，以在大规模配对数据集上学习如何消除一般的有限视角伪影。 然后，我们引入了一个重要性分数，以定量评估DL模型中参数的贡献，作为后续个体级适应的指导。 最后，在贝叶斯适应阶段，一种替代贝叶斯重建进一步根据目标案例的投影数据精确优化DL网络参数。 我们通过来自圆形轨迹CT的稀疏视角（90视角）投影和来自多段线性轨迹CT的特殊数据缺失情况验证了DLBayesian。 结果表明，DLBayesian在患者、解剖结构和数据分布的变化方面具有优越的泛化能力，并且在上下文结构恢复方面优于仅通过监督损失训练的网络。 在死鼠上的实际实验展示了其在实际CT扫描中的能力。