Lee Heui Chang, Song Bongyong, Kim Jin Sung, Jung James J, Li H. Harold, Mutic Sasa, Park Justin C
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA.
J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA.
Oncotarget. 2016 Dec 27;7(52):87342-87350. doi: 10.18632/oncotarget.13567.
The purpose of this study is to develop a fast and convergence proofed CBCT reconstruction framework based on the compressed sensing theory which not only lowers the imaging dose but also is computationally practicable in the busy clinic. We simplified the original mathematical formulation of gradient projection for sparse reconstruction (GPSR) to minimize the number of forward and backward projections for line search processes at each iteration. GPSR based algorithms generally showed improved image quality over the FDK algorithm especially when only a small number of projection data were available. When there were only 40 projections from 360 degree fan beam geometry, the quality of GPSR based algorithms surpassed FDK algorithm within 10 iterations in terms of the mean squared relative error. Our proposed GPSR algorithm converged as fast as the conventional GPSR with a reasonably low computational complexity. The outcomes demonstrate that the proposed GPSR algorithm is attractive for use in real time applications such as on-line IGRT.
本研究的目的是基于压缩感知理论开发一种快速且有收敛性证明的CBCT重建框架，该框架不仅能降低成像剂量，而且在繁忙的临床环境中计算上切实可行。我们简化了用于稀疏重建的梯度投影（GPSR）的原始数学公式，以减少每次迭代中进行线搜索过程时前向和后向投影的次数。基于GPSR的算法通常比FDK算法显示出更好的图像质量，特别是当只有少量投影数据可用时。当从360度扇形束几何结构中仅有40个投影时，基于GPSR的算法在10次迭代内就均方相对误差而言超过了FDK算法。我们提出的GPSR算法收敛速度与传统GPSR一样快，且计算复杂度合理较低。结果表明，所提出的GPSR算法对于在线IGRT等实时应用具有吸引力。
