提出一种节省显存的梯度近似方法，提升CT重建中隐式神经表示的优化效率。
Memory-efficient optimization of implicit neural representations for CT reconstruction
- 将梯度分解为雅可比-向量积，支持随机采样以降低显存占用
- 2D实验显示显存大幅减少，重建质量与标准方法相当
- 实现稀疏视角下3D锥束CT的高效重建，适合资源受限场景
隐式神经表示（INRs）为CT重建提供了一种参数高效且全可微的图像建模方式。然而，使用标准自动微分技术优化INRs进行CT重建时，尤其是在3D成像中，由于需要大量INR评估来模拟射线投影，可能导致显存消耗过高。为解决此问题，我们提出一种基于梯度分解为雅可比-向量积的内存高效的随机梯度近似方法，该方法适用于随机子采样，使用户可在显存使用与梯度近似精度之间进行权衡。在合成2D数据上的实验表明，该梯度近似方法相比标准INR训练显著减少显存占用，同时重建结果在收敛行为和均方误差方面表现相当。最后，我们在稀疏视角设置下成功实现了内存高效的3D锥束CT重建。
原文摘要 · Abstract (English)
Implicit neural representations (INRs) provide a parameter-efficient and fully differentiable image model for CT reconstruction. However, optimizing INRs for CT reconstruction using standard auto-differentiation techniques can be prohibitively GPU memory-intensive, especially in 3D imaging, due to the large number of INR evaluations needed to simulate ray projections. To address this issue, we propose a memory-efficient stochastic gradient approximation based on decomposing the gradient into a Jacobian-vector product that is amenable to stochastic subsampling. This approximation allows the user to trade-off between GPU memory usage and gradient approximation accuracy. Our experiments on synthetic 2D data demonstrate that gradient approximation uses far less GPU memory than standard INR training, while yielding reconstructions that are comparable in convergence behavior and mean squared error. Finally, we demonstrate that the proposed approach allows for memory-efficient 3D cone beam CT reconstruction in a sparse-view setting.
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