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; 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.

项目摘要 /摘要 C-ARM锥束CT（CBCT）在ABDO中的介入放射学（IR）过程中起着越来越多的作用 男性，特别重点是栓塞程序，例如跨细胞化学栓塞（TACE）用于治疗 用于控制内部出血的肝细胞癌（HCC）或跨性栓塞（TAE）。但是，相对 CBCT的长时间扫描时间会导致器官运动（呼吸和心脏运动以及蠕动）引起的伪影。这 对介入放射学的指导构成了重大挑战：例如，发现运动伪像呈现 在图像引导的TACE中，最多可介入的CBCT图像中有25％，在CBCT引导的紧急TAE中最多可介入18％。影响 在用选择性栓塞处理的单个或孤立水平的情况下，运动的最重要 非常小的血管结构的融合。现有的运动校正方法通常会引用周期性的假设，lim- 在心脏和呼吸动作之外，或依靠基准跟踪或封闭式收购，将其适用 破坏IR工作流和/或增加辐射剂量。因此，CBCT在图像引导的介入中的应用 腹部中的程序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。基于正则图像清晰度标准的最大化的“自动对焦”技术是 显示可在肢体，头部和心脏CBCT中获得有效的患者运动补偿。但是，当前的应用程序 这种方法仅限于严格的运动。我们假设在间隔中进行可变形的器官运动补偿 - 使用高级自动对焦技术可以使用多个本地刚性区域来实现Tional软组织CBCT Terest，预先通过机器学习决策框架获得的基本运动特征进行预处理。这 将追求以下目标：1）开发一种联合多区域自动对焦优化方法来补偿变形 - BLE器官运动。这包括纳入全面的人工制品校正和图像重建管 - 线路，用于收敛加速度的多阶段优化时间表的设计，以及变形的性能评估 幻象，尸体和动物实验。 2）制定一个决策框架以预处理运动 通过基于投影的方法组合进行物理信号估计的组合（res- 火灾周期）和对极其逼真的模拟数据培训的多输入，多分支，深度学习的体系结构 这将估算运动的基本特性（放大器的空间分布，方向和频率） 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指导TACE和评估图像质量的临床研究中，动物实验和通过专家观察者进行了 评估满意度和实用性。拟议的工作将产生一种可靠的，实用的方法来补偿变形 - CBCT可能会软组织运动，从而消除了IR中3D指导的严重障碍。可变形的自动对焦帧 - 工作将适用于软组织运动减少CBCT指导的其他干预措施，例如图像引导 放射治疗。