提出可旋转等变的多尺度可逆重建方法，提升锥束CT图像质量。
Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CT: From Simulated to Real Data
- 采用旋转等变与多尺度可逆结构，兼顾速度与内存效率。
- 在真实临床数据上使平均误差降低10 HU，优于现有商用混合方法。
- 适用于需要快速高质重建的医疗影像场景。
锥束CT（CBCT）是当前重要成像模态，但其图像质量低于传统CT，限制了应用。深度学习重建虽有潜力，但受限于缺乏真实标签数据、内存瓶颈及临床分辨率下的快速推理需求。本文提出LIRE++，一种端到端旋转等变的多尺度可逆原始-对偶学习重建框架，实现快速且内存高效的CBCT重建。通过内存优化与多尺度重构，支持高效训练与推理；旋转等变性提升参数效率。模型基于自研的快速准蒙特卡洛CBCT投影模拟器生成的仿真投影数据训练。在合成数据上，平均峰值信噪比提升1 dB；在真实临床数据上，重建结果与计划CT之间的平均绝对误差较当前商用先进混合方法降低10 Hounsfield Units。
原文摘要 · Abstract (English)
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.
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