Cao Qian, Zbijewski Wojciech, Sisniega Alejandro, Yorkston John, Siewerdsen Jeffrey H, Stayman J Webster
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
Phys Med Biol. 2016 Oct 21;61(20):7263-7281. doi: 10.1088/0031-9155/61/20/7263. Epub 2016 Oct 3.
Application of model-based iterative reconstruction (MBIR) to high resolution cone-beam CT (CBCT) is computationally challenging because of the very fine discretization (voxel size <100 µm) of the reconstructed volume. Moreover, standard MBIR techniques require that the complete transaxial support for the acquired projections is reconstructed, thus precluding acceleration by restricting the reconstruction to a region-of-interest. To reduce the computational burden of high resolution MBIR, we propose a multiresolution penalized-weighted least squares (PWLS) algorithm, where the volume is parameterized as a union of fine and coarse voxel grids as well as selective binning of detector pixels. We introduce a penalty function designed to regularize across the boundaries between the two grids. The algorithm was evaluated in simulation studies emulating an extremity CBCT system and in a physical study on a test-bench. Artifacts arising from the mismatched discretization of the fine and coarse sub-volumes were investigated. The fine grid region was parameterized using 0.15 mm voxels and the voxel size in the coarse grid region was varied by changing a downsampling factor. No significant artifacts were found in either of the regions for downsampling factors of up to 4×. For a typical extremities CBCT volume size, this downsampling corresponds to an acceleration of the reconstruction that is more than five times faster than a brute force solution that applies fine voxel parameterization to the entire volume. For certain configurations of the coarse and fine grid regions, in particular when the boundary between the regions does not cross high attenuation gradients, downsampling factors as high as 10× can be used without introducing artifacts, yielding a ~50× speedup in PWLS. The proposed multiresolution algorithm significantly reduces the computational burden of high resolution iterative CBCT reconstruction and can be extended to other applications of MBIR where computationally expensive, high-fidelity forward models are applied only to a sub-region of the field-of-view.
将基于模型的迭代重建（MBIR）应用于高分辨率锥束CT（CBCT）在计算上具有挑战性，因为重建体积的离散化非常精细（体素大小<100 µm）。此外，标准的MBIR技术要求对采集的投影进行完整的轴向支持重建，因此通过将重建限制在感兴趣区域来排除加速。为了减轻高分辨率MBIR的计算负担，我们提出了一种多分辨率惩罚加权最小二乘（PWLS）算法，其中体积被参数化为精细和粗体素网格的并集以及探测器像素的选择性合并。我们引入了一个惩罚函数，旨在对两个网格之间的边界进行正则化。该算法在模拟四肢CBCT系统的仿真研究以及试验台上的物理研究中进行了评估。研究了由精细和粗子体积的不匹配离散化引起的伪影。精细网格区域使用0.15毫米体素进行参数化，粗网格区域中的体素大小通过改变下采样因子来变化。对于高达4倍的下采样因子，在任何一个区域中都未发现明显的伪影。对于典型的四肢CBCT体积大小，这种下采样对应于重建的加速，比将精细体素参数化应用于整个体积的暴力解决方案快五倍以上。对于粗网格和精细网格区域的某些配置，特别是当区域之间的边界不穿过高衰减梯度时，可以使用高达10倍的下采样因子而不引入伪影，从而在PWLS中实现约50倍的加速。所提出的多分辨率算法显著降低了高分辨率迭代CBCT重建的计算负担，并且可以扩展到MBIR的其他应用，其中计算成本高的高保真正向模型仅应用于视场的子区域。
