变形图像配准（REDD）的准确性在放射治疗计划中具有显著的剂量学影响。我们使用变形点和体积的变化来评估各种不同算法的准确性。首先使用虚拟变形QA软件（ImSimQA，Oncology System Limited，UK）生成参考图像（Iref）和体积（Iref）。我们根据变形的类型，即变形1是为了增加松弛（松弛），变形2是为了减少，用变形点和松弛的轴向移动来变形Iref。通过ImSimQA软件获得的变形图像（Idef）和体积（Vdef）使用反变换算法逆变形为Iref和Iref。结果，我们从Idef获取变形图像（Iid），从Vdef获取体积（Vid）。算法包括Horn Schunk光流（HS）、迭代光流（IOF）、Modified Demons（MD）和Fast Demons（FD），采用MATLAB的可变形图像配准和自适应放射治疗工具包（DIRART）。使用归一化互信息（NMI）和归一化互相关（NCC）度量来计算Iref和Iid之间的图像相似性。当变形点移动距离为4mm时，所有算法的NMI均在1.81以上，NCC均在0.99以上，当变形点移动距离增加或减少约12%时，无论何种变形，NMI与Vid的差值均在5%以内，除MD算法外，变形1的Dice相似系数（DSC）均在0.95以上。在变形2的情况下，DSC的在所有的迭代算法中均大于0.95。Idef和Vdef尚未完全恢复到Iref和Iref，并且Iref算法的精度根据变形程度而不同。因此，应该针对所需的应用程序来验证并行计算算法的性能。
英文摘要：
The accuracy of deformable image registration (DIR) has a significant dosimetric impact in radiation treatment planning. We evaluated accuracy of various DIR algorithms using variations of the deformation point and volume. The reference image (Iref) and volume (Vref) was first generated with virtual deformation QA software (ImSimQA, Oncology System Limited, UK). We deformed Iref with axial movement of deformation point and Vref depending on the types of deformation that are the deformation1 is to increase the Vref (relaxation) and the deformation 2 is to decrease . The deformed image (Idef) and volume (Vdef) acquired by ImSimQA software were inversely deformed to Iref and Vref using DIR algorithms. As a result, we acquired deformed image (Iid) from Idef and volume (Vid) from Vdef. The DIR algorithms were the Horn Schunk optical flow (HS), Iterative Optical Flow (IOF), Modified Demons (MD) and Fast Demons (FD) with the Deformable Image Registration and Adaptive Radiotherapy Toolkit (DIRART) of MATLAB. The image similarity between Iref and Iid was calculated using the metrics that were Normalized Mutual Information (NMI) and Normalized Cross Correlation (NCC). When moving distance of deformation point was 4 mm, the value of NMI was above 1.81 and NCC was above 0.99 in all DIR algorithms.When the Vref increased or decreased about 12%, the difference between Vref and Vid was within 5% regardless of the type of deformation.The value of Dice Similarity Coefficient (DSC) was above 0.95 in deformation1 except for the MD algorithm. In case of deformation 2, that of DSC was above 0.95 in all DIR algorithms. The Idef and Vdef have not been completely restored to Iref and Vref and the accuracy of DIR algorithms was different depending on the degree of deformation. Hence, the performance of DIR algorithms should be verified for the desired applications.
A Contour-Guided Deformable Image Registration Algorithm for Adaptive Radiotherapy
在自适应放射治疗中，通常在计划CT和治疗CT（或锥形束CT）之间进行可变形图像配准，以生成用于剂量累积和轮廓传播的变形向量场（DVF）。治疗CT上的自动传播轮廓可能包含相对较大的误差，特别是在低对比度区域。经常需要临床医生检查和编辑传播的轮廓。编辑后的轮廓能够满足适应性治疗的临床要求;但DVF仍然不准确且与编辑后的轮廓不一致。本工作的目的是开发一种轮廓引导的可变形图像配准（CG-100）算法，以提高自适应放射治疗的DVF的准确性和一致性。将编辑的轮廓并入配准算法是通过用描绘的结构集对之间的强度匹配项正则化原始demons算法的目标函数来实现的。通过遵循原始的基于GPU的demons算法计算框架[Gu等人，Phys Med Biol.55（1）：207-219，2010]，在计算机图形处理单元（GPU）上实现CG-DES算法。CG-ESTA的性能进行了评估的五个临床头颈部和盆腔癌患者的数据。结果表明，与原Demons算法相比，CG-DVF算法在保持原Demons算法较高计算效率的同时，提高了DVF算法的精度和一致性.
A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；School of Mathematics, Sun Yat-sen University（中山大学数学学院）；Stanford University（斯坦福大学）；Guangdong Medical University（广东医科大学）；Xiaohe Healthcare ByteDance（字节跳动小河医疗）
Neural Graphics Primitives-based Deformable Image Registration for On-the-fly Motion Extraction
Paul Scherrer Institut（保罗·谢勒研究所）；ETH Zurich（苏黎世联邦理工学院）；University Hospital of Zurich（苏黎世大学医院）；University of Bern（伯尔尼大学）；Inselspital, Bern University Hospital（伯尔尼大学医院（ Inselspital））
In Vivo Quantification of Glioma-Induced Solid Stress Using MR Elastography and Deformable Image Registration
Charité – Universitätsmedizin Berlin（柏林夏里特医科大学）；Berlin Institute of Health at Charité – Universitätsmedizin Berlin（柏林健康研究所（隶属于柏林夏里特医科大学））；Humboldt University, Berlin（柏林洪堡大学）；Physikalisch-Technische Bundesanstalt, Berlin（德国联邦物理技术研究院）；Technical University Berlin, Einstein Center Digital Future（柏林工业大学爱因斯坦数字中心）
Continuous sPatial-Temporal Deformable Image Registration (CPT-DIR) for motion modelling in radiotherapy: beyond classic voxel-based methods
Center for Proton Therapy, Paul Scherrer Institut（保罗·施特格研究所质子治疗中心）；Department of Computer Science, ETH Zürich（苏黎世联邦理工学院计算机科学系）；Department of Physics, ETH Zürich（苏黎世联邦理工学院物理系）；Department of Automation, Tsinghua Univeristy（清华大学自动化系）；S-Lab, Nanyang Technological University（南洋理工大学S实验室）
Deformable Image Registration of Dark-Field Chest Radiographs for Local Lung Signal Change Assessment
Technical University of Munich（慕尼黑工业大学）；Munich Institute for Biomedical Engineering, Technical University of Munich（慕尼黑工业大学慕尼黑生物医学工程研究所）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）；TUM University Hospital（慕尼黑工业大学大学医院）；Philips Innovative Technologies（飞利浦创新技术公司）；Institute for Advanced Study, Technical University of Munich（慕尼黑工业大学高等研究院）
MICDIR: Multi-scale Inverse-consistent Deformable Image Registration using UNetMSS with Self-Constructing Graph Latent
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；Human Technopole（人类技术园）；German Centre for Neurodegenerative Disease（德国神经退行性疾病中心）；Centre for Behavioural Brain Sciences（行为脑科学中心）
