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

• 批准号: 10531910
• 负责人: Alejandro Sisniega Crespo
• 金额: $ 36.84万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 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/10531910
• 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; Compensation; Complex; Data; Emergency Situation; Evaluation; Exhibits; 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; Periodicals; Periodicity; Peristalsis; Physiological; Play; Primary carcinoma of the liver cells; Procedures; Property; Prostate Ablation; Radiation Dose Unit; Recurrent tumor; Reporting; Residual state; Role; Running; Scanning; Schedule; Signal Transduction; Spatial Distribution; Structure; Techniques; Testing; Therapeutic Embolization; Three-Dimensional Image; Time; Training; Validation; Visualization; Work; X-Ray Computed Tomography; arm; clinical application; cone-beam computed tomography; convolutional neural network; deep learning; design; experimental study; feeding; heart imaging; heart motion; image guided; image guided intervention; image guided radiation therapy; image reconstruction; imaging modality; improved; interest; internal control; minimally invasive; novel; porcine model; preconditioning; radiologist; respiratory; satisfaction; simulation; soft tissue; standard care

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.

项目总结/摘要 C形臂锥形束CT（CBCT）在腹部介入放射学（IR）程序的引导中发挥着越来越重要的作用。 男性，特别强调栓塞手术，如经动脉化疗栓塞（TACE）治疗 肝细胞癌（HCC）或经动脉栓塞（TAE）以控制内出血。但相对 CBCT的长扫描时间导致由器官运动（呼吸和心脏运动以及痉挛）引起的伪影。这 对介入放射学中的引导提出了重大挑战：例如， 在图像引导的TACE中，高达25%的CBCT图像无法判读，在CBCT引导的急诊TAE中，高达18%。的影响 在选择性栓塞治疗的单个或孤立病变的情况下， 非常小的血管结构的分化。现有的运动校正方法往往援引的周期性假设，lim， 在心脏和呼吸运动之外的应用，或依赖于基准跟踪或门控采集， 干扰IR工作流程和/或增加辐射剂量。因此，CBCT在影像引导介入治疗中的应用 腹部手术将显著受益于估计复杂可变形运动的新方法 直接从图像数据。基于正则化图像清晰度标准的最大化的“自动聚焦”技术， 显示在肢体、头部和心脏CBCT中产生有效的患者运动补偿。然而，目前的应用 这种方法的局限性是刚性运动。我们假设，在干预中可变形器官运动补偿- 传统的软组织CBCT可以通过先进的自动聚焦技术来实现， 兴趣，用通过机器学习决策框架获得的基本运动特征进行预处理。的 本文的主要目标是：1）提出一种多区域联合自聚焦优化方法， 器官运动。这包括纳入全面的伪影校正和图像重建管道- 线，设计多阶段优化调度收敛加速，并在变形性能评估， 可移植模型，尸体和动物实验。2)为动议的预处理制定决策框架 通过用于生理信号估计的基于投影的方法的组合的补偿方法（分辨率， 呼吸周期）和一个多输入、多分支、深度学习架构，该架构是在极其逼真的模拟数据上训练的 它将从初始运动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，通过专家观察员评估图像质量 满意度和效用评价。所提出的工作将产生一个强大的，实用的方法补偿变形- 在CBCT中能够进行软组织运动，消除了IR中3D引导的关键障碍。可变形自动对焦框架- 工作将适用于软组织运动减少CBCT引导的其他干预，如图像引导 放射治疗