论文标题
双分支先验诊断：使用计划扫描和辅助分割损失的CNN用于介入的CBCT
Dual Branch Prior-SegNet: CNN for Interventional CBCT using Planning Scan and Auxiliary Segmentation Loss
论文作者
论文摘要
本文提议扩展到稀疏视图介入的CBCT重建，其中包含高质量的计划扫描。另一个头部学会了分割介入仪器，从而指导重建任务。在培训期间，先前的扫描被 +-5DEG内置不一致。实验表明，提出的模型，双分支先验陈述，明显优于> 2.8dB PSNR的任何其他评估模型。它也保持强大的WRT。旋转高达 +-5.5维。
This paper proposes an extension to the Dual Branch Prior-Net for sparse view interventional CBCT reconstruction incorporating a high quality planning scan. An additional head learns to segment interventional instruments and thus guides the reconstruction task. The prior scans are misaligned by up to +-5deg in-plane during training. Experiments show that the proposed model, Dual Branch Prior-SegNet, significantly outperforms any other evaluated model by >2.8dB PSNR. It also stays robust wrt. rotations of up to +-5.5deg.
