Total variation superiorization schemes in proton computed tomography image reconstruction
Total variation superiorization schemes in proton computed tomography image reconstruction
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DOI：
10.1118/1.3504603
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发表时间：
2010-11-01
期刊：
影响因子：
3.8
通讯作者：
Rosenfeld, A. B.
中科院分区：
文献类型：
--
作者：
Penfold, S. N.;Schulte, R. W.;Rosenfeld, A. B.
目的：迭代投影重建算法是目前质子计算机层析成像(PCT)的首选重建方法。然而，由于质子能量散布和多次库仑散射引起的测量数据不一致，重建图像中的噪声随着连续迭代而增加。研究了全变差优化法(TVS)在抗扰动迭代投影算法中的应用。方法：采用块迭代对角松弛正交投影(DROP)算法重建GEANT4蒙特卡罗模拟PCT数据集。研究了两个添加到DROP上的TVS方案；第一个方案每周期执行一次优化步骤，第二个方案每块执行一次优化步骤。对这些方案的简化，包括消除TVS框架的计算代价高昂的可行性邻近检查步骤，也进行了研究。利用调制传递函数和对比度判别函数对空间分辨率和密度分辨率进行量化。结果：两种方案的空间分辨率和密度分辨率均优于标准DROP算法。消除了可行性邻近性检查，提高了图像质量，特别是图像噪声，同时图像重建时间也减半。结论：TVS实现的低对比度成像有望应用于未来的PCT研究。(C)2010年美国医学物理学家协会。[DOI：10.1118/1.3504603]
Purpose: Iterative projection reconstruction algorithms are currently the preferred reconstruction method in proton computed tomography (pCT). However, due to inconsistencies in the measured data arising from proton energy straggling and multiple Coulomb scattering, the noise in the reconstructed image increases with successive iterations. In the current work, the authors investigated the use of total variation superiorization (TVS) schemes that can be applied as an algorithmic add-on to perturbation-resilient iterative projection algorithms for pCT image reconstruction.Methods: The block-iterative diagonally relaxed orthogonal projections (DROP) algorithm was used for reconstructing GEANT4 Monte Carlo simulated pCT data sets. Two TVS schemes added on to DROP were investigated; the first carried out the superiorization steps once per cycle and the second once per block. Simplifications of these schemes, involving the elimination of the computationally expensive feasibility proximity checking step of the TVS framework, were also investigated. The modulation transfer function and contrast discrimination function were used to quantify spatial and density resolution, respectively.Results: With both TVS schemes, superior spatial and density resolution was achieved compared to the standard DROP algorithm. Eliminating the feasibility proximity check improved the image quality, in particular image noise, in the once-per-block superiorization, while also halving image reconstruction time. Overall, the greatest image quality was observed when carrying out the superiorization once per block and eliminating the feasibility proximity check.Conclusions: The low-contrast imaging made possible with TVS holds a promise for its incorporation into future pCT studies. (C) 2010 American Association of Physicists in Medicine. [DOI: 10.1118/1.3504603]