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 到目前为止，肺癌是全球癌症死亡的主要原因，约有15万人死亡 在美国每年一次。当症状出现时，肺癌五年的存活率是令人沮丧的17%。 低剂量CT肺癌筛查已被证明可以降低高危人群中的肺癌死亡率。 风险患者，因为癌症通常在IA期被发现得很早。明确的诊断需要组织采样， 尽管有气胸的风险，抽样通常是通过经皮经胸肺活检进行的。 在CT引导下。由于采样可能会导致立即的周围出血和模糊的视野 损伤，几乎没有出错的余地。然而，目前的程序挑战，涉及翻译患者- 以及-重复出孔以进行频繁的徒手调针和进针，引入误差， 这会耗费大量的时间和成本，而且会给患者带来电离辐射和并发症的风险。 该项目的目的是开发一种在以下条件下进行的自主针刺活检程序 人工智能机器人引导，优化患者安全和靶向准确性。该方法 涉及(I)高度灵巧、力敏感的冗余式机器人设计，带有主动置针装置， 在CT扫描仪内操作，可以为任何胸腔入路或患者连接和操控针头 位置；(Ii)人工智能规划器，找到新的、创伤较小和更安全的活体检查方法 以患者特定的方式进行病变，以及(Iii)闭环式CT图像反馈控制，以精确地操纵针头 可疑的损伤。该项目的工作将通过以下具体目标进行：(1)制定 力敏感机器人基于我们以前的机器人设计和在真实人类案例上的可达性验证 和安全性分析，(2)开发用于规划患者的针吸活检方法的指标和算法- 具体方式，以及(3)通过生物相关变量和 将自动机器人徒手放针的方法与用户研究进行比较。 建议的方法提供了一种解决方案，可以显著拓宽方法方向和 活组织检查用针的位置。半自主性通过计算各种不同的 规划优化准确性和安全性的方法的因素，包括患者解剖、从 敏感的结构和插入的深度-所有这些也会对患者的健康产生重大影响 在筛查期间，已经有肺部状况受损或急性肺炎风险增加的人 气胸。最后，机器学习、自动化和机器人技术的集成减少了差异 在临床医生之间，利用人口数据来制定数据支持的知情计划，并可以达到超级 在减少程序时间和电离成像的同时，提高了人类的精确度。我们的长期目标是评估如何 半自主方法可能比传统方法更有利、更有效和/或负担得起 人工活组织检查方法。该项目的结果将是一个经过验证的系统，为临床研究做好准备。