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.

项目总结/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 每年在美国。当症状出现时，肺癌的五年存活率是令人沮丧的17%。 低剂量CT的肺癌筛查已被证明可以降低高剂量CT患者的肺癌死亡率。 风险患者，因为癌症通常在IA期早期发现。连续性诊断需要组织取样， 尽管有气胸的风险，但通常通过经皮经胸肺活检进行采样 CT引导下由于取样可立即引起病灶周围出血和模糊视野， 病变，几乎没有犯错的余地。然而，目前的手术挑战，包括将病人转移到- 并且重复地从孔中出来，以用于频繁的徒手针调整和前进，引入误差， 花费大量的时间、成本，并且给患者带来电离辐射和并发症的风险。 本项目的目的是开发一种自主的针吸活检程序， 人工智能机器人引导，优化患者安全性和目标准确性。的方法 涉及（i）具有主动针放置器的高度灵巧的力敏感冗余机器人设计，该主动针放置器 在CT扫描仪内操作，可为任何胸部入路或患者铰接和操纵针 位置;（ii）人工智能计划器，可以找到新的，创伤较小的，更安全的活检方法 病变的患者特异性的方式，和（iii）闭环CT图像反馈控制，以精确地操纵针 可疑病变该项目的工作将通过以下具体目标进行：（1）开发 力敏机器人基于我们以前的机器人设计和验证真实的人的情况下，可达性 和安全性分析，（2）开发用于规划患者穿刺活检方法的指标和算法- 具体的方式，和（3）开发高保真呼吸幻影跨生物相关的变量， 在用户研究中比较自动机器人方法与徒手针放置。 拟议的办法提供了一种解决办法，可以大大拓宽办法的方向， 活检针的定位。半自治通过计算各种 计划方法时的因素，包括患者解剖结构、 敏感的结构和插入的深度-所有这些也会对病人的健康产生重大影响 在筛选期间，已经有肺功能受损或急性呼吸道疾病风险升高的患者， 气胸最后，机器学习、自动化和机器人技术的整合减少了变异 临床医生之间，利用人口数据，使数据支持的知情计划，并可以达到超级 同时减少手术时间和电离成像。我们的长期目标是评估 半自主方法对于传统方法是有利、更有效和/或可负担得起的 手动活检方法。该项目的成果将是一个经过验证的系统，可用于临床研究。