Total variation (TV) regularization is a popular reconstruction method for ill-posed imaging problems, and particularly useful for applications with piecewise constant targets. However, using TV for medical cone-beam computed X-ray tomography (CBCT) has been limited so far, mainly due to heavy computational loads at clinically relevant 3D resolutions and the difficulty in choosing the regularization parameter. Here an efficient minimization algorithm is presented, combined with a dynamic parameter adjustment based on control theory. The result is a fully automatic 3D reconstruction method running in clinically acceptable time. The input on top of projection data and system geometry is desired degree of sparsity of the reconstruction. This can be determined from an atlas of CT scans, or alternatively used as an easily adjustable parameter with straightforward interpretation.

翻译：全变分正则化是处理不适定成像问题的常用重建方法，特别适用于分段恒定目标的应用场景。然而，全变分方法在医学锥束计算机X射线断层扫描中的应用目前仍受限，主要源于临床相关三维分辨率下的巨大计算负荷以及正则化参数选择的困难。本文提出一种结合控制理论动态参数调整的高效最小化算法，实现了在临床可接受时间内运行的全自动三维重建方法。除投影数据与系统几何结构外，该方法以重建图像的期望稀疏度作为输入参数。该参数可通过CT扫描图谱确定，亦可作为具有直观解释的易调节参数使用。