用可训练高斯模型压缩锥束CT重建参数，提速降参且精度损失小。
GB-SVFBP: Gaussian-Based Shift-Variant FBP neural network
- 用可训练2D高斯模型替代传统滤波环节中轨迹相关部分
- 参数量减少99%，单轨迹训练时间降至1/4，重建质量轻微下降
- 适合需要快速部署的非圆轨迹锥束CT实际应用
本文提出一种基于高斯的变位移滤波反投影（GB-SVFBP）神经网络，用于高效重建非圆轨迹锥束计算机断层成像。传统可微变位移滤波反投影模型包含加权、微分、二维Radon变换和二维反投影等操作。所提方法在该框架基础上，引入可训练的二维高斯模型表示滤波过程中的轨迹相关部分，显著减少可训练参数数量。实验表明，该模型参数量减少99%，仅牺牲少量重建质量；每个轨迹的训练时间缩短至原来的四分之一，大幅加速收敛。这些改进显著提升了模型的实际可用性与效率，适用于真实场景下的非圆轨迹锥束CT重建。
原文摘要 · Abstract (English)
This paper proposes a Gaussian-Based Shift-Variant filtered backprojection (FBP) neural network, which is designed for the efficient reconstruction of non-circular trajectory cone beam computed tomography. The traditional differentiable shift-variant FBP model consists of a filtering component and a backprojection process. The filtering component includes operations such as weightings, differentiations, a 2D Radon transform, and a 2D backprojection. The proposed methods build on this framework by introducing a trainable 2D Gaussian model to represent the trajectory-related part in the filtering process, achieving a substantial reduction in the number of trainable parameters. Experimental results demonstrate that the proposed model reduces the parameter count by 99%, while only sacrificing a slight amount of reconstruction quality. Furthermore, the training time for each trajectory is reduced to one-fourth of the original, significantly accelerating convergence. These enhancements demonstrate a considerable augmentation in the model's practicality and effectiveness, making it a valuable asset for real-world applications.
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