Online adaptive radiation therapy (ART) promises the ability to deliver an optimal treatment in response to daily patient anatomic variation. A major technical barrier for the clinical implementation of online ART is the requirement of rapid image segmentation. Deformable image registration (DIR) has been used as an automated segmentation method to transfer tumor/organ contours from the planning image to daily images. However, the current computational time of DIR is insufficient for online ART. In this work, this issue is addressed by using computer graphics processing units (GPUs). A grey-scale based DIR algorithm called demons and five of its variants were implemented on GPUs using the Compute Unified Device Architecture (CUDA) programming environment. The spatial accuracy of these algorithms was evaluated over five sets of pulmonary 4DCT images with an average size of 256x256x100 and more than 1,100 expert-determined landmark point pairs each. For all the testing scenarios presented in this paper, the GPU-based DIR computation required around 7 to 11 seconds to yield an average 3D error ranging from 1.5 to 1.8 mm. It is interesting to find out that the original passive force demons algorithms outperform subsequently proposed variants based on the combination of accuracy, efficiency, and ease of implementation.
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）
