用PCA压缩可微非均匀FBP模型，参数减少97.25%仍保精度
Compressibility Analysis for the differentiable shift-variant Filtered Backprojection Model
- 用PCA分析冗余权重，将参数分解为特征向量、压缩权重和均值向量
- 实现97.25%的可训练参数压缩，重建精度无损失
- 显著提升训练速度，更适合实际医学成像应用
可微非均匀滤波反投影（FBP）模型能重建任意非圆形轨迹的锥束计算机断层扫描（CBCT）数据。该方法利用深度学习估计重建所需的冗余权重，但每幅投影的权重计算仍非常耗时。本文提出基于主成分分析（PCA）的新压缩优化方法，对正弦轨迹投影数据学习的冗余权重进行分析，发现原始模型存在显著参数冗余。通过将PCA直接嵌入可微非均匀FBP重建流程，构建了由可训练特征向量矩阵、压缩权重和均值向量组成的结构。该技术在不牺牲重建精度的前提下，实现97.25%的可训练参数缩减，显著降低模型复杂度并大幅提升训练效率，极大增强了模型在真实场景中的实用性。
原文摘要 · Abstract (English)
The differentiable shift-variant filtered backprojection (FBP) model enables the reconstruction of cone-beam computed tomography (CBCT) data for any non-circular trajectories. This method employs deep learning technique to estimate the redundancy weights required for reconstruction, given knowledge of the specific trajectory at optimization time. However, computing the redundancy weight for each projection remains computationally intensive. This paper presents a novel approach to compress and optimize the differentiable shift-variant FBP model based on Principal Component Analysis (PCA). We apply PCA to the redundancy weights learned from sinusoidal trajectory projection data, revealing significant parameter redundancy in the original model. By integrating PCA directly into the differentiable shift-variant FBP reconstruction pipeline, we develop a method that decomposes the redundancy weight layer parameters into a trainable eigenvector matrix, compressed weights, and a mean vector. This innovative technique achieves a remarkable 97.25% reduction in trainable parameters without compromising reconstruction accuracy. As a result, our algorithm significantly decreases the complexity of the differentiable shift-variant FBP model and greatly improves training speed. These improvements make the model substantially more practical for real-world applications.
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