GB-SVFBP：基于高斯的变移位FBP神经网络
GB-SVFBP: Gaussian-Based Shift-Variant FBP neural network
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中文总结 AI 辅助
针对非圆形轨迹锥束CT重建问题，提出基于高斯的变移位FBP神经网络，引入可训练二维高斯模型减少参数数量，实验表明其参数减少99%，训练时间降为四分之一，显著提升模型实用性与有效性。
中文摘要 AI 辅助
本文提出了一种基于高斯的变移位滤波反投影（FBP）神经网络，用于高效重建非圆形轨迹锥束计算机断层扫描。传统的可微变移位FBP模型由滤波组件和反投影过程组成，滤波组件包括加权、微分、二维拉东变换和二维反投影等操作。本文方法在此框架基础上，引入可训练的二维高斯模型来表示滤波过程中与轨迹相关的部分，大幅减少了可训练参数数量。实验结果表明，该模型参数数量减少了99%，仅略微牺牲重建质量，且每条轨迹的训练时间降至原来的四分之一，显著加速了收敛。这些改进证明了模型实用性和有效性的显著提高，对实际应用具有重要价值。
英文摘要
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.
