Deformable motion compensation for 3D image-guided interventional radiology

用于 3D 图像引导介入放射学的可变形运动补偿

基本信息

批准号：
10376182
负责人：
Alejandro Sisniega Crespo
金额：
$ 35.78万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10376182
关键词：
3-Dimensional Abdomen Acceleration Affect Algorithms Angiography Animal Experiments Animals Architecture Arterial Embolization Arteries Blood Vessels Breathing Cadaver Cardiac Characteristics Chemoembolization Clinical Clinical Research Complex Data Emergency Situation Evaluation Exhibits Financial compensation Fluoroscopy Frequencies Head Hemorrhage Image Intervention Interventional radiology Joints Learning Lesion Limb structure Liver Machine Learning Maps Methods Modeling Morphologic artifacts Morphology Motion Organ Outcome Patients Pelvis Performance Periodicity Peristalsis Physiological Play Primary carcinoma of the liver cells Procedures Property Prostate Ablation Radiation Dose Unit Recurrence Reporting Residual state Role Scanning Schedule Signal Transduction Spatial Distribution Structure Techniques Testing Therapeutic Embolization Three-Dimensional Image Time Training Validation Visualization Work X-Ray Computed Tomography arm base clinical application cone-beam computed tomography convolutional neural network deep learning design experimental study feeding heart motion image guided image guided intervention image guided radiation therapy image reconstruction imaging modality improved internal control minimally invasive novel porcine model preconditioning radiologist respiratory satisfaction simulation soft tissue standard care tumor

项目摘要

PROJECT SUMMARY / ABSTRACT C-arm cone-beam CT (CBCT) plays an increasing role in guidance of interventional radiology (IR) procedures in the abdo- men, with special emphasis in embolization procedures, such as transarterial chemoembolization (TACE) for treatment of hepatocellular carcinoma (HCC) or transarterial embolization (TAE) for control of internal hemorrhage. However, relatively long scan time of CBCT results in artifacts arising from organ motion (respiratory and cardiac motion and peristalsis). This poses a significant challenge to guidance in interventional radiology: for example, motion artifacts were found to render up to 25% of CBCT images un-interpretable in image-guided TACE, and 18% in CBCT-guided emergency TAE. The impact of motion is most significant in cases of single or isolated lesions treated with selective embolization that requires visual- ization of very small vascular structures. Existing motion correction methods often invoke assumption of periodicity, lim- iting their applicability outside of cardiac and respiratory motions, or rely on fiducial tracking or gated acquisition that disrupt IR workflow and/or increase radiation dose. Therefore, the application of CBCT in image-guided interventional procedures in the abdomen would significantly benefit from new methods that estimate complex deformable motion directly from image data. “Autofocus” techniques based on maximization of a regularized image sharpness criterion were shown to yield effective patient motion compensation in extremity, head and cardiac CBCT. However, current applications of such methods are limited to rigid motions. We hypothesize that deformable organ motion compensation in interven- tional soft-tissue CBCT can be achieved with advanced autofocus techniques using multiple locally rigid regions of in- terest, preconditioned with basic motion characteristics obtained through a machine learning decision framework. The following aims will be pursued: 1) Develop a joint multi-region autofocus optimization method to compensate deforma- ble organ motion. This includes incorporation into a comprehensive artifacts correction and image reconstruction pipe- line, design of multi-stage optimization schedules for convergence acceleration, and performance evaluation in deforma- ble phantoms, and cadaver and animal experiments. 2) Develop a decision framework for preconditioning of the motion compensation method through a combination of projection-based approaches for physiological signal estimations (res- piratory cycle) and a multi-input, multi-branch, deep learning architecture trained on extremely realistic simulated data that will estimate basic properties of motion (spatial distribution of amplitude, direction, and frequency) from an initial motion-contaminated image and its associated raw projection data. 3) Evaluate deformable motion compensation in animal experiments and in a clinical study in 50 cases of CBCT-guided TACE and assess image quality via expert observer evaluation of satisfaction and utility. The proposed work will yield a robust, practical method for compensation of deform- able soft-tissue motion in CBCT, removing a critical impediment to 3D guidance in IR. The deformable autofocus frame- work will be applicable to other interventions in which soft-tissue motion diminishes CBCT guidance, such as image-guided radiation therapy.

项目概要/摘要 C 形臂锥形束 CT (CBCT) 在指导腹部介入放射学 (IR) 手术中发挥着越来越重要的作用。男性，特别强调栓塞手术，例如用于治疗以下疾病的经动脉化疗栓塞术 (TACE) 然而，肝细胞癌（HCC）或经动脉栓塞（TAE）控制内出血的效果相对较差。 CBCT 扫描时间过长会导致器官运动（呼吸、心脏运动和蠕动）产生伪影。对介入放射学的指导提出了重大挑战：例如，运动伪影被发现会导致在图像引导的 TACE 中，高达 25% 的 CBCT 图像无法解读，在 CBCT 引导的紧急 TAE 中，高达 18% 的影响。在需要视觉的选择性栓塞的单个或孤立的治疗病变的情况下，运动的变化最为显着。现有的运动校正方法经常引用周期性、极限的假设。将其适用性置于心脏和呼吸运动之外，或依赖于基准跟踪或门控采集扰乱 IR 工作流程和/或增加辐射剂量因此，CBCT 在图像引导介入治疗中的应用。腹部手术将大大受益于估计复杂变形运动的新方法直接来自图像数据的基于正则化图像清晰度标准最大化的“自动对焦”技术。研究表明，在四肢、头部和心脏 CBCT 中可产生有效的患者运动补偿。此类方法仅限于刚性运动，我们在干预中捕获了可变形器官运动补偿。软组织 CBCT 可以通过先进的自动对焦技术使用多个局部刚性区域来实现。兴趣，以通过机器学习决策框架获得的基本运动特征为条件。我们将追求以下目标： 1）开发联合多区域自动对焦优化方法来补偿变形这包括纳入全面的伪影校正和图像重建管道。线，收敛加速的多阶段优化方案设计，以及变形性能评估 ble 模型、尸体和动物实验 2) 开发运动预处理的决策框架。通过结合基于投影的生理信号估计方法（res- 盗版循环）和在极其真实的模拟数据上训练的多输入、多分支深度学习架构将从初始值估计运动的基本属性（幅度、方向和频率的空间分布）运动污染图像及其相关的原始投影数据 3) 评估可变形运动补偿。在 50 例 CBCT 引导 TACE 的动物实验和临床研究中，并通过专家观察员评估图像质量满意度和实用性的评估所提出的工作将产生一种稳健、实用的变形补偿方法。 CBCT 中的软组织运动，消除了 IR 中 3D 引导的关键障碍。这项工作将适用于软组织运动削弱 CBCT 引导的其他干预措施，例如图像引导放射治疗。