Due to the inter- and intra- variation of respiratory motion, it is highly desired to provide real-time volumetric images during the treatment delivery of lung stereotactic body radiation therapy (SBRT) for accurate and active motion management. In this proof-of-concept study, we propose a novel generative adversarial network integrated with perceptual supervision to derive instantaneous volumetric images from a single 2D projection. Our proposed network, named TransNet, consists of three modules, i.e., encoding, transformation and decoding modules. Rather than only using image distance loss between the generated 3D images and the ground truth 3D CT images to supervise the network, perceptual loss in feature space is integrated into loss function to force the TransNet to yield accurate lung boundary. Adversarial supervision is also used to improve the realism of generated 3D images. We conducted a simulation study on 20 patient cases, who had received lung SBRT treatments in our institution and undergone 4D-CT simulation, and evaluated the efficacy and consistency of our method for four different projection angles, i.e., 0, 30, 60 and 90 degree. For each 3D CT image set of a breathing phase, we simulated its 2D projections at these angles.Then for each projection angle, a patient's 3D CT images of 9 phases and the corresponding 2D projection data were used for training, with the remaining phase used for testing. The mean absolute error, normalized MAE, peak signal-to-noise ratio and structural similarity index metric achieved by our method are 99.3 HU, 0.032, 23.4 dB and 0.949, respectively. These results demonstrate the feasibility and efficacy of our 2D-to-3D method for lung cancer patients, which provides a potential solution for in-treatment real-time on-board volumetric imaging for accurate dose delivery to ensure the effectiveness of lung SBRT treatment.
Proton Pencil-Beam Scanning Stereotactic Body Radiation Therapy and Hypofractionated Radiation Therapy for Thoracic Malignancies: Patterns of Practice Survey and Recommendations for Future Development from NRG Oncology and PTCOG
Magnetic resonance delta radiomics to track radiation response in lung tumors receiving stereotactic MRI-guided radiotherapy
Mass General Brigham（麻省总医院布里格姆医疗系统）；Harvard Medical School（哈佛医学院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Maastricht University（马斯特里赫特大学）
Circulating Lymphocytes Preservation in Lung Cancer Stereotactic Body Radiation Therapy with Ultra-Fast Proton Delivery Using Modularized Pin Ridge Filters
Dosimetric and Deformation Effects of Image-Guided Interventions during Stereotactic Body Radiation Therapy of the Prostate using an Endorectal Balloon
Deep Learning-Based Automatic Delineation of Liver Domes in kV Triggered Images for Online Breath-hold Reproducibility Verification of Liver Stereotactic Body Radiation Therapy
Cleveland Clinic Foundation（克利夫兰医学中心）；Case Western Reserve University（凯斯西储大学）
Stereotactic Arrhythmia Radioablation for Refractory Ventricular Tachycardia: A Narrative Review and Exploratory Pooled Analysis of Clinical Outcomes and Toxicity
Emory University（埃默里大学）；University of Chicago（芝加哥大学）
A dosimetric and robustness analysis of Proton Arc Therapy (PAT) with Early Energy Layer and Spot Assignment (ELSA) for lung cancer versus conventional Intensity modulated therapy (IMPT)
Real-time markerless tumour tracking with patient-specific deep learning using a personalized data generation strategy: Proof of concept by phantom study
目的：对于立体定向肺放疗中的实时无标记肿瘤跟踪，我们提出了一种不同的方法，该方法使用个性化数据生成策略，使用患者特定的深度学习（DL），避免了收集大量患者数据集的需要。我们用数字体模模拟和环氧树脂体模研究验证了我们的策略。研究方法：我们使用卷积神经网络开发了用于放射治疗的肺肿瘤跟踪，该卷积神经网络通过使用从每个体模的治疗计划4D-CT生成的多个数字重建射线照片（DRR）为每个体模的病变进行训练。我们使用各种投影几何形状生成的大量训练DRR来训练肿瘤-骨分化，以模拟肿瘤运动。我们解决了使用DRR进行训练和X射线图像进行跟踪的问题，通过使用训练DRR与随机对比度变换和随机噪声添加。结果：我们将足够的跟踪精度定义为满足等中心跟踪误差<1 mm的帧百分比。在模拟研究中，我们在3 cm球形和1.5 x 2.25 x 3 cm卵形质量中实现了100%的跟踪精度。在体模研究中，我们分别在3 cm和2 cm球形肿块中实现了100%和94.7%的跟踪准确度。这需要32.5 ms/帧（30.8 fps）的实时处理。结论：我们证明了基于患者特定DL的立体定向肺放疗的实时无标记肿瘤跟踪框架的潜在可行性，该框架具有数字体模和环氧树脂体模研究的个性化数据生成。知识进展：使用DL和个性化数据生成是实时肺部肿瘤跟踪的有效策略。
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；Kharkevich Institute for Information Transmission Problems（哈尔克维奇信息传输问题研究所）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Moscow Gamma-Knife Center（莫斯科伽马刀中心）；Burdenko Neurosurgery Institute（布尔坚科神经外科研究所）
Statistical quality control for volumetric modulated arc therapy (VMAT) delivery using machine log data
本研究的目的是使用机器日志数据建立用于监测体积调制弧治疗（VMAT）输送误差的统计质量控制。Eclipse和Clinac iX直线加速器与RapidArc系统（Varian Medical Systems，Palo Alto，USA）用于交付VMAT计划。在输送RapidArc射野期间，机器确定输送的电机单位（MU）和机架角度位置精度，并在完成RapidArc输送后在控制台监视器上显示MU（sigma_MU;剂量测定误差）和机架角度（sigma_GA;几何误差）的标准偏差。在本研究中，首先分析日志数据以确认其有效性和可用性;然后，应用统计过程控制（SPC）及时监测所有RapidArc射野的sigma_MU和sigma_GA：99名患者共195个弧射野。所有射野的sigma_MU和sigma_GA测定两次，即首先在患者特定计划QA期间测定，然后在首次治疗期间再次测定。无论治疗部位如何，sigma_MU和sigma_GA时间序列都非常稳定;然而，sigma_GA强烈依赖于机架旋转速度。立体定向体部放射治疗（SBRT）的RapidArc输送的sigma_GA小于典型VMAT。因此，SPC分别适用于SBRT病例和一般病例。此外，机架旋转的电位计的精度是重要的，因为sigma_GA可以由于其条件而显著改变。通过将SPC应用于sigma_MU和sigma_GA，可以有效地监控交货错误。然而，S…
A volumetric Modulated Arc Therapy (VMAT) combined with deep inspiration breath hold (DIBH) technique for adjuvant irradiation for left sided breast cancer
Mixed Ion Beams Enable Simultaneous Treatment and Real-Time Imaging in Carbon Ion Therapy
GSI Helmholtzzentrum für Schwerionenforschung（GSI亥姆霍兹重离子研究中心）；University College London（伦敦大学学院）；Centro Nazionale di Adroterapia Oncologica (CNAO)（国家肿瘤质子治疗中心）；Technische Universität Darmstadt（达姆施塔特工业大学）；University Federico II（那不勒斯费德里科二世大学）
Early Response Assessment in Lung Cancer Patients using Spatio-temporal CBCT Images
Indian Institute of Technology Kharagpur（印度理工学院卡拉克普尔分校）；Tata Medical Center, Kolkata（塔塔医疗中心（加尔各答））；Arden Cancer Centre（阿登癌症中心）；Department of Computer Science and Engineering（计算机科学与工程系）；Department of Radiation Oncology（放射肿瘤学系）；Department of Nuclear Medicine（核医学系）；Department of Medical Physics（医学物理系）
Renormalization of radiobiological response functions by energy loss fluctuations and complexities in chromosome aberration induction: deactivation theory for proton therapy from cells to tumor control
Terbium-149 PET/CT: First Quantitative Imaging with a Clinical Long-Axial Field-of-View Scanner
Department of Nuclear Medicine, Inselspital, Bern University Hostpital, University of Bern（伯尔尼大学医院因塞尔斯皮塔尔核医学科）；ARTORG Center for Biomedical Engineering Research, University of Bern（伯尔尼大学ARTORG生物医学工程研究中心）；Albert Einstein Center for Fundamental Physics (AEC), Laboratory for High Energy Physics (LHEP), University of Bern（伯尔尼大学阿尔伯特·爱因斯坦基础物理中心（AEC）高能物理实验室（LHEP））；Center for Radiopharmaceutical Sciences, PSI Center for Life Sciences, Villigen-PSI（维利根-PSI PSI生命科学中心放射药学科学中心）；Institut Laue-Langevin（朗之万研究所）；Laboratory of Radiochemistry, PSI Center for Nuclear Engineering and Sciences（PSI核工程与科学中心放射化学实验室）
Predicting respiratory motion for real-time tumour tracking in radiotherapy
Baltic Institute of Advanced Technology（波罗的海高级技术研究所）；Vytautas Magnus University（维陶塔斯·马格努斯大学）；Aalto University（阿尔托大学）；Rubedo Systems, UAB（Rubedo系统有限责任公司）；Hospital of Lithuanian University of Health Sciences Kauno Klinikos（立陶宛健康科学大学考纳斯诊所医院）
