Previous work showed that total variation superiorization (TVS) improves reconstructed image quality in proton computed tomography (pCT). The structure of the TVS algorithm has evolved since then and this work investigated if this new algorithmic structure provides additional benefits to pCT image quality. Structural and parametric changes introduced to the original TVS algorithm included: (1) inclusion or exclusion of TV reduction requirement, (2) a variable number, $N$, of TV perturbation steps per feasibility-seeking iteration, and (3) introduction of a perturbation kernel $0<α<1$. The structural change of excluding the TV reduction requirement check tended to have a beneficial effect for $3\le N\le 6$ and allows full parallelization of the TVS algorithm. Repeated perturbations per feasibility-seeking iterations reduced total variation (TV) and material dependent standard deviations for $3\le N\le 6$. The perturbation kernel $α$, equivalent to $α=0.5$ in the original TVS algorithm, reduced TV and standard deviations as $α$ was increased beyond $α=0.5$, but negatively impacted reconstructed relative stopping power (RSP) values for $α>0.75$. The reductions in TV and standard deviations allowed feasibility-seeking with a larger relaxation parameter $λ$ than previously used, without the corresponding increases in standard deviations experienced with the original TVS algorithm. This work demonstrates that the modifications related to the evolution of the original TVS algorithm provide benefits in terms of both pCT image quality and computational efficiency for appropriately chosen parameter values.
Total variation superiorization schemes in proton computed tomography image reconstruction
目的：迭代投影重建算法是目前质子计算机断层成像（pCT）的首选重建方法。然而，由于质子能量离散和多次库仑散射引起的测量数据的不一致性，重建图像中的噪声随着连续迭代而增加。在目前的工作中，我们研究了使用全变差优化（TVS）计划，可以作为一个算法的附加扰动弹性迭代投影算法的pCT图像重建。研究方法：块迭代对角松弛正交投影（DROP）算法用于重建Geant 4 Monte Carlo模拟pCT数据集。两个TVS计划添加到DROP进行了研究，第一次进行了superiorization的步骤，每个周期一次，第二次每个块一次。这些计划的简化，包括消除计算昂贵的可行性邻近检查步骤的TVS框架，也进行了研究。调制传递函数和对比度判别函数分别用于量化空间和密度分辨率。结果：与标准DROP算法相比，这两种TVS方案实现了更高的空间和密度分辨率。消除可行性邻近检查提高了图像质量，特别是图像噪声，在每块一次的优越化，同时也减半图像重建时间。总体而言，当每个组织块执行一次优越化并消除可行性邻近检查时，观察到最佳图像质量。结论：TVS使低对比度成像成为可能，有望将其纳入我们未来的pCT研究。
GPU-accelerated FREDopt package for simultaneous dose and LETd proton radiotherapy plan optimization via superiorization methods
Silesian University of Technology（西里西亚理工大学）；Institute of Nuclear Physics PAN（波兰科学院核物理研究所）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；National Oncology Institute, National Research Institute, Krakow Branch（国家肿瘤研究所克拉科夫分部）；Dipartimento di Scienze di Base e Applicate per l'Ingegneria, La Sapienza Università di Roma（罗马大学基础与应用工程科学系）；University of Haifa（海法大学）；Heidelberg University（海德堡大学）
Superiorization: An optimization heuristic for medical physics
The Graduate Center, City University of New York（纽约市立大学研究生中心）；Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México（墨西哥国立自治大学应用数学和系统研究所）；Stanford University（斯坦福大学）；University of Haifa（海法大学）
Performance of Hull-Detection Algorithms For Proton Computed Tomography Reconstruction
Baylor University（贝勒大学）；California State University, San Bernardino（加州州立大学圣贝纳迪诺分校）；University of Haifa（海法大学）；Loma Linda University Medical Center（洛马林达大学医学中心）
Proton Computed Tomography Image Reconstruction Based on the Richardson-Lucy Algorithm
HUN-REN Wigner Research Centre for Physics（匈牙利科学院维格纳物理研究中心）；ELTE Eötvös Loránd University, Institute of Physics（罗兰大学物理研究所）；National Institute of Oncology（国家肿瘤研究所）；Budapest University of Technology and Economics, Institute of Nuclear Techniques（布达佩斯技术与经济大学核技术研究所）
我们正在探索低剂量质子射线照相术和计算机断层成像（pCT）技术，以提高质子治疗计划的准确性，并在治疗时提供无伪影图像进行验证和适应性治疗。在这里，我们报告了我们的原型pCT头部扫描仪的全面光束测试结果。探测器系统和数据采集可实现每秒超过一百万个单独测量的质子的持续速率，允许在六分钟或更短的射束时间内完成完整的CT扫描。为了评估质子射线照相以及计算机断层成像扫描仪的性能，我们在Northwestern Medicine Chicago质子中心进行了多次体模扫描，包括设计用于评估空间分辨率的定制体模、评估相对阻止本领测量的体模和剂量测定体模。一些图像，性能，和剂量测定结果，从这些幻影扫描的仪器，数据采集系统和校准方法的描述一起。
Analysis methods for in-beam PET images in proton therapy treatment verification: a comparison based on Monte Carlo simulations
背景和目的：束内正电子发射断层成像（PET）是质子治疗中可用于体内非侵入性治疗监测的模式之一。可以比较在各种治疗会话期间获得的PET分布，以便识别具有解剖学变化的区域。本工作的目的是在质子治疗验证的分次间PET图像比较的背景下测试和比较不同的分析方法。研究方法：在我们的研究中，我们使用FLUKA Monte Carlo代码和人工生成的CT扫描来模拟质子治疗过程中不同阶段的束内PET分布。我们比较了Beam-Eye-View方法、Most-Likely-Shift方法、基于体素的形态学方法和伽马评估方法，以比较治疗开始时和治疗几周后的PET图像。将结果与CT扫描进行比较。结果和结论：如果有很多统计数据可用，则三维方法（如VBM和伽马）优于二维方法（如MLS和BEV），因为这些方法允许识别具有异常活动的区域。VBM方法的缺点是需要大量的MC模拟。伽马分析的缺点是，在公差标准上不存在临床适应症。在计算时间方面，BEV和MLS方法是优选的。我们建议同时使用这四种方法，以便最好地确定活动变化的位置和原因。
Reconstruction of proton relative stopping power with a granular calorimeter detector model
University of Kaiserslautern-Landau（凯泽斯劳滕-兰道大学）；University of Bergen（卑尔根大学）；HUN-REN Wigner Research Centre for Physics（匈牙利研究与教育网络维格纳物理研究中心）；Research and Production Enterprise ”LTU” (RPELTU)（LTU研究与生产企业）；Institute for Subatomic Physics, Utrecht University/Nikhef（乌得勒支大学/尼赫夫亚原子物理研究所）；St. Petersburg University（圣彼得堡大学）；Western Norway University of Applied Sciences（西挪威应用科学大学）
Generating synthetic computed tomography for radiotherapy: SynthRAD2025 challenge report
Radiation Physics, Department of Hematology, Oncology；Radiation Physics, Skåne University Hospital, Lund, Sweden；Medical Radiation Physics, Department of Clinical Sciences Lund, Lund University, Lund, Sweden；Radiotherapy Department, University Medical Center Utrecht, Utrecht, The Netherlands；Computational Imaging Group for MR Diagnostics \& Therapy, University Medical Center Utrecht, Utrecht, The Netherlands；Division of Medical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany；Heidelberg Institute for Radiation Oncology (HIRO)；National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany；Department of Radiation Oncology；Cyberknife Center, University Hospital of Cologne, Cologne, Germany；Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands；Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands；Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany；Bavarian Cancer Research Center (BZKF), Munich, Germany；Australian eHealth Research Center, CSIRO, Brisbane, Australia；School of Computer Science, University of Sydney, Sydney, Australia；Department of Orthodontics；Pattern Recognition Lab, FAU Erlangen-Nuremberg, Germany；RIKEN Center for Integrative Medical Sciences, Tokyo, Japan；Erasmus Mundus Joint Master's Degree IPCVai, University of Bordeaux, France；Computer Science, Huazhong University of Science；Huazhong University of Science；Canon Medical Systems (China) CO., LTD., Beijing, China；Department of Radiation Oncology, Inselspital, Bern University Hospital；University of Bern, Bern, Switzerland
