Cone-beam computed tomography (CBCT) systems, with their portability, present a promising avenue for direct point-of-care medical imaging, particularly in critical scenarios such as acute stroke assessment. However, the integration of CBCT into clinical workflows faces challenges, primarily linked to long scan duration resulting in patient motion during scanning and leading to image quality degradation in the reconstructed volumes. This paper introduces a novel approach to CBCT motion estimation using a gradient-based optimization algorithm, which leverages generalized derivatives of the backprojection operator for cone-beam CT geometries. Building on that, a fully differentiable target function is formulated which grades the quality of the current motion estimate in reconstruction space. We drastically accelerate motion estimation yielding a 19-fold speed-up compared to existing methods. Additionally, we investigate the architecture of networks used for quality metric regression and propose predicting voxel-wise quality maps, favoring autoencoder-like architectures over contracting ones. This modification improves gradient flow, leading to more accurate motion estimation. The presented method is evaluated through realistic experiments on head anatomy. It achieves a reduction in reprojection error from an initial average of 3mm to 0.61mm after motion compensation and consistently demonstrates superior performance compared to existing approaches. The analytic Jacobian for the backprojection operation, which is at the core of the proposed method, is made publicly available. In summary, this paper contributes to the advancement of CBCT integration into clinical workflows by proposing a robust motion estimation approach that enhances efficiency and accuracy, addressing critical challenges in time-sensitive scenarios.

翻译：锥束计算机断层扫描(CBCT)系统因其便携性，为直接床旁医学成像提供了有前景的应用途径，特别是在急性卒中评估等关键场景中。然而，CBCT整合到临床工作流程面临挑战，主要与扫描时间长导致患者在扫描期间移动，从而造成重建体图像质量下降有关。本文提出一种新颖的CBCT运动估计方法，利用基于梯度的优化算法，该算法借助锥束CT几何中反投影算子的广义导数。在此基础上，构建了一个完全可微的目标函数，用于评估当前运动估计在重建空间中的质量。我们大幅加速了运动估计，与现有方法相比实现了19倍的提速。此外，我们研究了用于质量度量回归的网络架构，提出预测体素级质量图，倾向于采用类自编码器架构而非收缩架构。这一修改改善了梯度流，从而实现更精确的运动估计。通过头部解剖的真实实验对所提方法进行了评估。该方法在运动补偿后，将重投影误差从初始平均3毫米降至0.61毫米，并持续展现出优于现有方法的性能。作为所提方法核心的反投影操作的解析雅可比矩阵已公开提供。总之，本文通过提出一种稳健的运动估计方法，提升了效率与精确度，应对了时间敏感场景中的关键挑战，为推动CBCT整合到临床工作流程做出了贡献。