Particle-Tracking Proton Computed Tomography-Data Acquisition, Preprocessing, and Preconditioning.
Particle-Tracking Proton Computed Tomography-Data Acquisition, Preprocessing, and Preconditioning.
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DOI：
10.1109/access.2021.3057760
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Schulte RW
中科院分区：
文献类型：
--
作者：
Schultze B;Karbasi P;Sarosiek C;Coutrakon G;Ordoñez CE;Karonis NT;Duffin KL;Bashkirov VA;Johnson RP;Schubert KE;Schulte RW
质子 CT (pCT) 是一种很有前途的新型成像技术，可以比 X 射线 CT 更准确地重建患者每立方毫米体素的相对阻止本领 (RSP)。反过来，这将带来更好的质子范围精度，从而缩小计划肿瘤体积 (PTV)。硬件描述和一些重建图像之前已经报道过。在一系列的两个贡献中，我们重点介绍将 pCT 探测器数据转换为最终重建的 pCT 图像以应用于质子治疗计划的软件算法。如何实现这一点有多种选择，我们将在数据处理链的每个阶段描述我们的解决方案。在本系列的第一篇论文中，我们介绍了 pCT 跟踪和能量范围探测器的数据采集以及数据的预处理方式，包括从跟踪数据转换为格式良好的轨迹信息，以及从校准的多级能量范围探测器的数据转换为水当量路径长度。然后将这些预处理后的数据用于通过 FDK 锥束 CT 算法进行初始图像形成。数据采集、预处理和 FDK 重建的输出以及两个模型（包括儿童头部模型）的说明性成像结果一起呈现。本系列的第二篇论文将演示如何结合使用迭代求解器和优化方法来进一步改进前期 FDK 图像重建所产生的图像以及在混合 CPU/GPU 计算机集群上实施这些算法。
Proton CT (pCT) is a promising new imaging technique that can reconstruct relative stopping power (RSP) more accurately than x-ray CT in each cubic millimeter voxel of the patient. This, in turn, will result in better proton range accuracy and, therefore, smaller planned tumor volumes (PTV). The hardware description and some reconstructed images have previously been reported. In a series of two contributions, we focus on presenting the software algorithms that convert pCT detector data to the final reconstructed pCT images for application in proton treatment planning. There were several options on how to accomplish this, and we will describe our solutions at each stage of the data processing chain. In the first paper of this series, we present the data acquisition with the pCT tracking and energy-range detectors and how the data are preprocessed, including the conversion to the well-formatted track information from tracking data and water-equivalent path length from the data of a calibrated multi-stage energy-range detector. These preprocessed data are then used for the initial image formation with an FDK cone-beam CT algorithm. The output of data acquisition, preprocessing, and FDK reconstruction is presented along with illustrative imaging results for two phantoms, including a pediatric head phantom. The second paper in this series will demonstrate the use of iterative solvers in conjunction with the superiorization methodology to further improve the images resulting from the upfront FDK image reconstruction and the implementation of these algorithms on a hybrid CPU/GPU computer cluster.
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影响因子：
1.8
作者：
Johnson RP;Bashkirov V;DeWitt L;Giacometti V;Hurley RF;Piersimoni P;Plautz TE;Sadrozinski HF;Schubert K;Schulte R;Schultze B;Zatserklyaniy A
通讯作者：
Zatserklyaniy A
DOI：
10.1016/j.nima.2012.04.029
发表时间：
2013-01-21
期刊：
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment
影响因子：
--
作者：
Sadrozinski HF;Johnson RP;Macafee S;Plumb A;Steinberg D;Zatserklyaniy A;Hurley VB;Schulte R
通讯作者：
Schulte R
影响因子：
19.7
作者：
WILSON, RR
通讯作者：
WILSON, RR
DOI：
10.1016/j.nima.2015.07.066
发表时间：
2016-02-11
期刊：
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment
影响因子：
--
作者：
Bashkirov VA;Johnson RP;Sadrozinski HF;Schulte RW
通讯作者：
Schulte RW
影响因子：
3.2
作者：
CORMACK, AM
通讯作者：
CORMACK, AM