Deformable motion compensation for 3D image-guided interventional radiology

项目摘要/摘要 C臂锥束CT(CBCT)在指导腹部介入放射学(IR)治疗中发挥着越来越重要的作用。 男性，特别强调栓塞术，如经动脉化疗栓塞术(TACE)治疗 肝细胞癌(HCC)或经动脉栓塞术(TAE)用于控制内出血。然而，相对来说， 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。这 对介入放射学的指导提出了重大挑战：例如，运动伪影被发现呈现 高达25%的CBCT图像在影像引导的TACE中无法解释，18%的图像在CBCT引导的急诊TAE中无法解释。其影响 在对单个或孤立的病变进行选择性栓塞术的情况下，运动的影响最为显著，这种情况需要视觉- 非常小的血管结构的程序化。现有的运动校正方法通常采用周期性假设，即线性运动校正。 基于它们在心脏和呼吸运动之外的适用性，或者依赖于基准跟踪或门控采集 扰乱红外工作流程和/或增加辐射剂量。因此，CBCT在影像引导介入治疗中的应用 腹部手术将大大受益于估计复杂变形运动的新方法。 直接从图像数据。基于正则化图像清晰度准则最大化的“自动聚焦”技术包括 在四肢、头部和心脏CBCT中显示出有效的患者运动补偿。然而，当前的应用程序 这类方法仅限于刚性运动。我们假设可变形器官的运动补偿在干涉- 常规软组织CBCT可以使用先进的自动聚焦技术来实现，该技术使用多个局部刚性区域 感兴趣的，通过机器学习决策框架获得基本运动特征的预条件。这个 1)提出了一种联合多区域自聚焦优化方法对变形进行补偿。 最好的器官运动。这包括纳入全面的伪影校正和图像重建管道- 设计了收敛加速的多阶段优化方案，并进行了变形性能评估。 幻影，身体和动物实验。2)为动议的前置条件制定决策框架 一种结合基于投影的生理信号估计方法的补偿方法 海盗循环)和基于极其逼真的模拟数据训练的多输入、多分支、深度学习架构 这将估计运动的基本属性(幅度、方向和频率的空间分布) 运动污染图像及其关联的原始投影数据。3)评估可变形运动补偿 50例CBCT引导下TACE的动物实验和临床研究并通过专家观察者评估图像质量 满意度和实用性的评估。建议的工作将产生一种稳健、实用的变形补偿方法-- 能够在CBCT中进行软组织运动，消除了IR中3D制导的一个关键障碍。可变形的自动对焦框- 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 放射疗法。

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.