同时重建图像并校准扫描几何，提升各类断层成像精度。
Geometry Calibration in Tomography with a Differentiable Ray-Based Model
- 用可微分射线模型联合优化体积与几何参数。
- 在多种断层成像数据上验证，显著改善失准条件下的重建质量。
- 适合需要高精度几何校准的医学与工业成像研究者。
断层成像中，实际采集参数与标称参数间的几何错位会降低重建质量。本文提出一种框架，可对任意源-探测器配置下的体积与采集几何进行联合重建与校准。核心是基于射线追踪的可微分投影算子，其关于几何参数的梯度计算复杂度与前向算子相当。采用B样条基表示体积，实现连续可微，相比体素表示具有更优的优化收敛性。在CT、微米级CT、纳米级CT及正电子发射断层成像数据上，针对多种几何失准情形进行了验证，均取得显著性能提升。
原文摘要 · Abstract (English)
Geometric misalignments between the nominal and true acquisition parameters in tomography degrade reconstructions. We propose a framework that jointly reconstructs the volume and calibrates the acquisition geometry for arbitrary source--detector configurations. The core of our framework is an x-ray transform operator whose gradients with respect to the acquisition geometry can be efficiently computed with a ray-tracing method of structure and computational complexity similar to those of the forward operator. We represent the volume in a B-spline basis to provide a continuously differentiable model. This results in a better-behaved optimization landscape compared to voxel-based representations. We validate our framework with CT, micro-CT, nano-CT, and positron emission tomography data under a variety of geometric misalignments.
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