Task-Driven 3D Interventional Imaging

项目总结/摘要 虽然术中C形臂锥形束CT（CBCT）用于越来越多的微创成像， 引导程序，成像性能落后于传统的诊断CT -通常是由于挑战 特别是介入成像套件。然而，介入成像也有一些不同的广告， 利用先前研究和计划数据提供有关患者解剖结构、成像目标和 潜在的成像任务。再加上介入系统的灵活性增加， 将源和探测器放置在任意几何形状中，为进行诊断提供了独特的机会 CT做不到。具体而言，可以使用所有患者、任务和程序特定信息来驱动 这些介入系统在定制的轨道中，其中成像设备借助于数学模型， 成像性能预测的模型，可以前瞻性地驱动源-探测器轨迹以获取 最丰富的信息视图。这种“智能”成像系统可以自动最大化成像性能 具有更好的图像质量、更低的辐射剂量等，而不依赖于CT的经验或专业知识 技师我们提出了一个框架，整合了以前的解剖知识和治疗计划， 非圆形轨道的能力，以及成像任务的定义，以驱动自定义任务和患者， 用于3D介入成像的特定源-探测器轨迹。我们力求实现这一任务驱动的 介入成像通过以下具体目标：目标1：为任务驱动的介入成像奠定基础， 本发明涉及一种常规成像，包括用于基于任务的性能预测的定量框架、 最大限度地提高用户定义任务的区域性能。目标2：开发临床系统，用于任务驱动的 介入成像，包括控制两个最先进的临床C形臂的方法-一个落地式 机器人C形臂和移动的C形臂Cios Spin-优化的任务驱动轨迹。目标3：开展临床 初步评估患者任务驱动成像的试点研究。计划对两个国家进行试点研究， 临床目标应用：前列腺动脉栓塞和颈椎融合，这是传统的挑战， 被复杂的解剖结构和介入硬件所困扰，可能会严重降低特定 意见.将建立和初步确定拟议的任务驱动成像方法的安全性和可行性 将进行相对性能和观察者偏好测试的数据，以告知未来的临床研究。 这些目标的成功完成为任务驱动的介入成像开启了一个新的范例， 严格利用特定于患者的先验信息来获取和重建最适合特定患者的数据 介入成像任务。

PROJECT SUMMARY / ABSTRACT While intraoperative C-arm cone-beam CT (CBCT) is used in a growing spectrum of minimally invasive image- guided procedures, imaging performance lags behind conventional diagnostic CT – often due to challenges particular to the interventional imaging suite. However, interventional imaging also has a number of distinct ad- vantages with prior studies and planning data providing information about patient anatomy, imaging targets, and potential imaging tasks. Coupling this with the increased flexibility of interventional systems to automatically position the source and detector in arbitrary geometries opens a unique opportunity to do something diagnostic CT cannot do. Specifically, one can use all of the patient-, task-, and procedure-specific information to drive these intervention systems in customized orbits wherein the imaging device, with the help of a mathematical model of imaging performance prediction, can prospectively drive a source-detector trajectory to acquire the most information rich views. Such a “smart” imaging system could automatically maximize imaging performance with better image quality, lower radiation doses, etc. without relying on the experience or expertise of a CT technician. We propose a framework that integrates prior anatomical knowledge and treatment plans, the capability for non-circular orbits, and a definition of the imaging task to drive custom task- and patient- specific source-detector trajectories for 3D interventional imaging. We seek to achieve this task-driven interventional imaging through the following specific aims: Aim 1: Develop foundations for task-driven inter- ventional imaging including a quantitative framework for task-based performance prediction, an optimizer that maximizes regional performance for user-defined tasks. Aim 2: Develop clinical systems for task-driven in- terventional imaging including methods to command two state-of-the-art clinical C-arms – a floor-mounted robotic C-arm and a mobile C-arm, Cios Spin – in optimized task-driven trajectories. Aim 3: Conduct clinical pilot studies for an initial evaluation of task-driven imaging in patients. A pilot study is planned for two clinical target applications: prostatic artery embolization and cervical spine fusion which are traditionally chal- lenged by difficult anatomy and interventional hardware that can severely degrade measurements in specific views. Safety and feasibility of the proposed task-driven imaging approach will be established and preliminary data on relative performance and observer preference test will be conducted to inform future clinical studies. Successful completion of these aims begins opens a new paradigm for task-driven interventional imaging that rigorously leverages patient-specific prior information to acquire and reconstruct data that is optimal for specific interventional imaging tasks.