加速隐式神经表示在低剂量CT重建中的优化，提升速度并保持精度。
Accelerated Optimization of Implicit Neural Representations for CT Reconstruction
- 改进损失函数以改善优化条件，加快收敛。
- 采用交替方向乘子法，实现更快的稀疏视角重建。
- 适用于需要快速重建的医学影像场景，如低剂量CT。
受计算机视觉中求解复杂逆问题的成功启发，隐式神经表示（INRs）被用于低剂量/稀疏视角X射线计算机断层扫描（CT）重建。INR将CT图像表示为小型神经网络，以空间坐标为输入，输出衰减值。将INR拟合到投影数据的过程类似于经典的模型基迭代重建方法。然而，使用损失函数和基于梯度的算法训练INR可能极其缓慢，通常需要数干次迭代才能收敛。本文研究了加速INR在CT重建中优化的方法，提出两种策略：(1) 使用改进条件的修改损失函数；(2) 基于交替方向乘子法（ADMM）的算法。实验表明，这两种方法在稀疏视角设置下显著加速了合成乳腺CT幻影的INR重建。
原文摘要 · Abstract (English)
Inspired by their success in solving challenging inverse problems in computer vision, implicit neural representations (INRs) have been recently proposed for reconstruction in low-dose/sparse-view X-ray computed tomography (CT). An INR represents a CT image as a small-scale neural network that takes spatial coordinates as inputs and outputs attenuation values. Fitting an INR to sinogram data is similar to classical model-based iterative reconstruction methods. However, training INRs with losses and gradient-based algorithms can be prohibitively slow, taking many thousands of iterations to converge. This paper investigates strategies to accelerate the optimization of INRs for CT reconstruction. In particular, we propose two approaches: (1) using a modified loss function with improved conditioning, and (2) an algorithm based on the alternating direction method of multipliers. We illustrate that both of these approaches significantly accelerate INR-based reconstruction of a synthetic breast CT phantom in a sparse-view setting.
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