Benson T M, Gregor J
Department of Computer Science, University of Tennessee, Knoxville, Tennessee 37996-3450, USA.
Phys Med Biol. 2006 Sep 21;51(18):4533-46. doi: 10.1088/0031-9155/51/18/006. Epub 2006 Aug 30.
Three-dimensional iterative reconstruction of high-resolution, circular orbit cone-beam x-ray CT data is often considered impractical due to the demand for vast amounts of computer cycles and associated memory. In this paper, we show that the computational burden can be reduced by limiting the reconstruction to a small, well-defined portion of the image volume. We first discuss using the support region defined by the set of voxels covered by all of the projection views. We then present a data-driven preprocessing technique called focus of attention that heuristically separates both image and projection data into object and background before reconstruction, thereby further reducing the reconstruction region of interest. We present experimental results for both methods based on mouse data and a parallelized implementation of the SIRT algorithm. The computational savings associated with the support region are substantial. However, the results for focus of attention are even more impressive in that only about one quarter of the computer cycles and memory are needed compared with reconstruction of the entire image volume. The image quality is not compromised by either method.
由于需要大量的计算机周期和相关内存,高分辨率圆形轨道锥束X射线CT数据的三维迭代重建通常被认为是不切实际的。在本文中,我们表明通过将重建限制在图像体积的一个小的、定义明确的部分,可以减轻计算负担。我们首先讨论使用由所有投影视图覆盖的体素集定义的支持区域。然后,我们提出一种称为注意力焦点的数据驱动预处理技术,该技术在重建前启发式地将图像和投影数据分离为对象和背景,从而进一步缩小感兴趣的重建区域。我们基于小鼠数据和SIRT算法的并行实现给出了这两种方法的实验结果。与支持区域相关的计算节省是巨大的。然而,注意力焦点的结果更令人印象深刻,因为与重建整个图像体积相比,只需要大约四分之一的计算机周期和内存。两种方法都不会损害图像质量。