等价多尺度学习可逆重建用于锥束CT：从模拟到真实数据
Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CT: From Simulated to Real Data
AI总结：
本文提出LIRE++，一种旋转等价多尺度学习可逆重建方法，用于提升锥束CT的重建效率与图像质量。
摘要：
锥束CT（CBCT）是当今重要的成像模态，然而与更传统的计算机断层扫描（CT）相比，CBCT图像质量较低仍然是其应用的限制因素。深度学习重建方法是经典分析和迭代重建方法的有前途的替代方案，但将其应用于CBCT通常困难，因为缺乏真实数据、内存限制以及在临床相关分辨率下需要快速推理。在本工作中，我们提出了LIRE++，一种端到端的旋转等价多尺度学习可逆对偶方案，用于快速且内存高效的CBCT重建。内存优化和多尺度重建允许快速训练和推理，而旋转等价性提高了参数效率。LIRE++是在我们开发的快速准蒙特卡洛CBCT投影模拟器上训练的模拟投影数据。在合成数据上评估时，LIRE++在峰值信噪比方面比其他深度学习基线平均提高了1 dB。在真实临床数据上，LIRE++将重建与相应计划CT之间的平均绝对误差降低了10亨斯菲尔德单位，相较于当前专有状态最先进的混合深度学习/迭代方法。
英文摘要：
Cone Beam CT (CBCT) is an important imaging modality nowadays, however lower image quality of CBCT compared to more conventional Computed Tomography (CT) remains a limiting factor in CBCT applications. Deep learning reconstruction methods are a promising alternative to classical analytical and iterative reconstruction methods, but applying such methods to CBCT is often difficult due to the lack of ground truth data, memory limitations and the need for fast inference at clinically-relevant resolutions. In this work we propose LIRE++, an end-to-end rotationally-equivariant multiscale learned invertible primal-dual scheme for fast and memory-efficient CBCT reconstruction. Memory optimizations and multiscale reconstruction allow for fast training and inference, while rotational equivariance improves parameter efficiency. LIRE++ was trained on simulated projection data from a fast quasi-Monte Carlo CBCT projection simulator that we developed as well. Evaluated on synthetic data, LIRE++ gave an average improvement of 1 dB in Peak Signal-to-Noise Ratio over alternative deep learning baselines. On real clinical data, LIRE++ improved the average Mean Absolute Error between the reconstruction and the corresponding planning CT by 10 Hounsfield Units with respect to current proprietary state-of-the-art hybrid deep-learning/iterative method.
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。
