Langet Hélène, Riddell Cyril, Reshef Aymeric, Trousset Yves, Tenenhaus Arthur, Lahalle Elisabeth, Fleury Gilles, Paragios Nikos
Image Processing and Clinical Applications Laboratory, GE Healthcare, Buc F-78533, France; Laboratoire des Signaux et Systèmes, CentraleSupélec, Gif-sur-Yvette F-91192, France; Center for Visual Computing, CentraleSupélec, Châtenay-Malabry F-92295, France; and INRIA, Orsay F-91893, France.
Image Processing and Clinical Applications Laboratory, GE Healthcare, Buc F-78533, France.
Med Phys. 2015 Sep;42(9):5222-37. doi: 10.1118/1.4928144.
This paper addresses the reconstruction of x-ray cone-beam computed tomography (CBCT) for interventional C-arm systems. Subsampling of CBCT is a significant issue with C-arms due to their slow rotation and to the low frame rate of their flat panel x-ray detectors. The aim of this work is to propose a novel method able to handle the subsampling artifacts generally observed with analytical reconstruction, through a content-driven hierarchical reconstruction based on compressed sensing.
The central idea is to proceed with a hierarchical method where the most salient features (high intensities or gradients) are reconstructed first to reduce the artifacts these features induce. These artifacts are addressed first because their presence contaminates less salient features. Several hierarchical schemes aiming at streak artifacts reduction are introduced for C-arm CBCT: the empirical orthogonal matching pursuit approach with the ℓ0 pseudonorm for reconstructing sparse vessels; a convex variant using homotopy with the ℓ1-norm constraint of compressed sensing, for reconstructing sparse vessels over a nonsparse background; homotopy with total variation (TV); and a novel empirical extension to nonlinear diffusion (NLD). Such principles are implemented with penalized iterative filtered backprojection algorithms. For soft-tissue imaging, the authors compare the use of TV and NLD filters as sparsity constraints, both optimized with the alternating direction method of multipliers, using a threshold for TV and a nonlinear weighting for NLD.
The authors show on simulated data that their approach provides fast convergence to good approximations of the solution of the TV-constrained minimization problem introduced by the compressed sensing theory. Using C-arm CBCT clinical data, the authors show that both TV and NLD can deliver improved image quality by reducing streaks.
A flexible compressed-sensing-based algorithmic approach is proposed that is able to accommodate for a wide range of constraints. It is successfully applied to C-arm CBCT images that may not be so well approximated by piecewise constant functions.
本文探讨介入式C型臂系统的X射线锥束计算机断层扫描（CBCT）重建问题。由于C型臂旋转速度慢且平板X射线探测器帧率低，CBCT的欠采样是一个重要问题。这项工作的目的是提出一种新颖的方法，通过基于压缩感知的内容驱动分层重建，来处理分析重建中常见的欠采样伪影。
核心思想是采用分层方法，首先重建最显著的特征（高强度或梯度），以减少这些特征所产生的伪影。先处理这些伪影是因为它们的存在会干扰不太显著的特征。针对C型臂CBCT，引入了几种旨在减少条纹伪影的分层方案：用于重建稀疏血管的具有ℓ0伪范数的经验正交匹配追踪方法；使用压缩感知的ℓ1范数约束同伦的凸变体，用于在非稀疏背景上重建稀疏血管；全变差（TV）同伦；以及一种新的非线性扩散（NLD）经验扩展。这些原理通过惩罚迭代滤波反投影算法来实现。对于软组织成像，作者比较了使用TV和NLD滤波器作为稀疏性约束的情况，两者均使用交替方向乘子法进行优化，TV使用阈值，NLD使用非线性加权。
作者在模拟数据上表明，他们的方法能够快速收敛到压缩感知理论引入的TV约束最小化问题的良好近似解。使用C型臂CBCT临床数据，作者表明TV和NLD都可以通过减少条纹来提高图像质量。
提出了一种灵活的基于压缩感知的算法方法，该方法能够适应广泛的约束条件。它已成功应用于可能不太适合用分段常数函数近似的C型臂CBCT图像。
