Automated Planning and Robotic Delivery of Needle Biopsies under CT Image Guidance

CT 图像引导下穿刺活检的自动规划和机器人传送

• 批准号: 10619755
• 负责人: Michael Yip
• 金额: $ 60.02万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-16 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10619755
• 关键词: 3-Dimensional; Acute; Address; Air Embolism; Algorithms; Anatomy; Articulation; Artificial Intelligence; Automation; Biopsy; Blood Vessels; Breathing; Cancer Etiology; Cessation of life; Chest; Clinical Research; Comparative Study; Complication; Computed Tomography Scanners; Conscious; Core Biopsy; Data; Decision Making; Diagnosis; Fatigue; Feedback; Freedom; Goals; Health; Hemorrhage; Hemothorax; Hour; Human; Image; Individual; Intervention; Ionizing radiation; Length; Lesion; Link; Lung; Machine Learning; Malignant Neoplasms; Malignant neoplasm of lung; Manuals; Methods; Motion; Movement; Needle biopsy procedure; Needles; Newly Diagnosed; Obstruction; Outcome; Patients; Performance; Periodicals; Peripheral; Physicians; Pleura; Pleural; Pneumothorax; Population; Population Heterogeneity; Positioning Attribute; Procedures; Prospective Studies; Puncture procedure; Radiation; Risk; Risk Factors; Robot; Robotics; Safety; Sampling; Scanning; Somatotype; Specific qualifier value; Structure; Survival Rate; Symptoms; System; Time; Tissue Sample; Tissues; Translating; United States; Variant; Visualization; Work; X-Ray Computed Tomography; clinically relevant; cost; design; dexterity; exhaust; high risk; image guided; imager; interest; ionization; low dose computed tomography; lung cancer screening; mortality; neurovascular; patient population; patient safety; radiologist; real-time images; robot control; robotic system; screening; vector; ventilation

PROJECT SUMMARY / ABSTRACT Primary lung cancer is by far the leading cause of cancer death worldwide, with approximately 150,000 deaths yearly in the United States. When symptoms arise, the lung cancer survival rate at five years is a dismal 17%. Lung cancer screening with low-dose CT has been shown to reduce mortality from lung cancer among high- risk patients as the cancer is typically caught early, in stage IA. Definitive diagnosis requires tissue sampling, and despite risks of pneumothorax, sampling is often performed by percutaneous transthoracic lung biopsies under CT guidance. Since sampling can cause immediate perilesional hemorrhage and obscure views of the lesion, there is little room for error. However, current procedural challenges, involving translating the patient in- and-out of the bore repetitively for frequent freehand needle adjustments and advancements, introduce errors, take significant time, cost, and confers ionizing radiation and risk of complications to the patient. The purpose of this project is to develop an autonomous needle biopsy procedure performed under artificially intelligent robot guidance, optimizing for patient safety and targeting accuracy. The approach involves (i) a highly dexterous, force-sensitive redundant robot design with an active needle placer that operates inside the CT scanner, and can articulate and steer needles for any thoracic approach or patient position; (ii) an artificially intelligent planner that finds new, less traumatic, and safer approaches to biopsy lesions in a patient-specific manner, and (iii) closed-loop CT-image feedback control to precisely steer needles to suspect lesions. The work of this project is to be carried out via the following Specific Aims: (1) develop the force sensitive robot based on our previous robotic designs and validate on real human cases for reachability and safety analysis, (2) develop metrics and algorithms for planning needle biopsy approaches in a patient- specific way, and (3) develop high-fidelity breathing phantoms across biologically relevant variables and compare the automated robotic approach to freehanded needle placement a user study. The proposed approach offers a solution that could significantly broaden the approach direction and positioning of needles for biopsies. The semi-autonomy provides significant value by computing a variety of factors in planning the approach that optimizes accuracy and safety, including patient anatomy, safety from sensitive structures, and depth of insertions --- all of which can also have a significant effect on patient health during screening, who are already have compromised pulmonary status or have elevated risk of acute pneumothorax. Finally, the integration of machine learning, automation, and robotics reduces the variation between clinicians, leverages population data to make data-backed informed plans, and can reach super- human precision while reducing procedure time and ionizing imaging. Our long-term goal is to evaluate how the semi-autonomous approach can be advantageous to, more effective than, and/or affordable to traditional manual biopsy approaches. The outcome of this project will be a validated system ready for a clinical study.

项目摘要 /摘要 原发性肺癌是迄今为止全球癌症死亡的主要原因，约有15万人死亡 一年一度在美国。当出现症状时，五年时的肺癌存活率为17％。 已经证明，用低剂量CT进行肺癌筛查可以降低高肺癌死亡率 风险患者通常会在IA期提早捕获癌症。确定的诊断需要组织采样， 尽管气胸有风险，但经皮经胸肺活检通常会进行采样 在CT指导下。由于采样会引起直接的周围出血，并蒙蔽 病变，几乎没有错误的空间。但是，当前的程序挑战涉及将患者转化为 并重复出现孔，以频繁进行徒手调整和进步，引入错误， 花费大量时间，成本和赋予电离辐射以及对患者并发症的风险。 该项目的目的是制定在下面执行的自主针头活检程序 人工智能的机器人指导，以优化患者的安全性和靶向准确性。方法 涉及（i）高度灵巧的，力敏感的冗余机器人设计，并带有一个活跃的针脚垫圈， 在CT扫描仪内部操作，并可以针对任何胸腔方法或患者表达并转向针头 位置; （ii）一个人为聪明的计划者，发现了新的，更少的创伤性和更安全的活检方法 以特定于患者的方式病变，（iii）闭环CT图像反馈控制到精确转向针头 怀疑病变。该项目的工作应通过以下特定目的进行：（1）开发 基于我们以前的机器人设计的强制敏感机器人，并验证真实的人类案例可及 和安全分析，（2）开发用于计划针头活检方法的指标和算法 - 特定方式，（3）在生物学相关的变量和 比较自动化的机器人方法，以徒手安置用户研究。 拟议的方法提供了一种解决方案，可以显着扩大进近方向和 活检针的定位。半自治通过计算各种 规划优化准确性和安全性的方法的因素，包括患者解剖学，安全性 敏感结构和插入深度 - 所有这些都会对患者健康产生重大影响 在筛查期间，已经受损的肺状况或急性风险较高 气胸。最后，机器学习，自动化和机器人技术的集成减少了变化 在临床医生之间，利用人口数据制定了数据支持的知情计划，并可以达到超级 人体精确度，同时减少程序时间和电离成像。我们的长期目标是评估 半自主的方法可以比传统更有效，更有效和/或负担得起 手动活检接近。该项目的结果将是一个验证的系统，准备进行临床研究。