I-Corps: Artificial Intelligence (AI)-based Image Fusion Technology for Guiding Prostate Biopsies

I-Corps：基于人工智能 (AI) 的图像融合技术，用于指导前列腺活检

• 批准号: 2333204
• 负责人: Pingkun Yan
• 金额: $ 5万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333204&HistoricalAwards=false
• 关键词: Corps; Artificial; Intelligence; AI; based

The broader impact/commercial potential of this I-Corps project is the development of an artificial intelligence (AI)-driven image fusion technology designed to guide cancer biopsies. Currently, the gold standard approach for guiding biopsies employs fusing magnetic resonance imaging (MRI) and ultrasound, which can increase cancer biopsy yield by 30%. However, less than 20% of procedures are performed with fusion guidance. The existing methods for fusing MRI and ultrasound images require external tracking hardware that acts like a “medical GPS” to track the position and orientation of an ultrasound probe. The hardware is cumbersome to set up and adds significant cost to the system. The available systems also require clinical users to manually align MRI and ultrasound volumes, which is called image registration. Image registration is a challenging task and has a significant impact on the accuracy of the system, often leading to the poor performance of such systems. The proposed technology uses AI models to remove the need for tracking hardware and reduce the expertise requirement for image registration. This technology may improve biopsy procedures by enhancing patient throughput, minimizing system setup time, and increasing the accessibility of fusion-guided biopsies even in less-equipped clinics.This I-Corps project is based on the development of image fusion technology using Artificial Intelligence (AI) that aims to eliminate the need for external tracking hardware used in cancer biopsy procedures. The proposed technology utilizes AI and machine learning to automate the fusion process, resulting in a more efficient and accurate biopsy guidance system. It uses AI algorithms to automatically identify image features within the input images for volume reconstruction, cross modality image registration, and frame to volume mapping. Results comparing his technology with traditional methods that depend heavily on external tracking devices for fusing magnetic resonance imaging (MRI) and ultrasound images show that it reduces human intervention and improves image fusion performance. The technical work has been documented in peer-reviewed papers, patent filings, and retrospective studies to validate the technology. In addition, the proposed technology addresses the challenges faced by doctors, offering an opportunity for improved biopsy accuracy, reduced costs, and a shortened learning curve.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个I-Corps项目更广泛的影响/商业潜力是开发一种人工智能（AI）驱动的图像融合技术，旨在指导癌症活检。 目前，引导活检的金标准方法采用融合磁共振成像（MRI）和超声，这可以将癌症活检率提高30%。然而，只有不到20%的手术是在融合引导下进行的。用于融合MRI和超声图像的现有方法需要外部跟踪硬件，其作用类似于“医疗GPS”以跟踪超声探头的位置和取向。硬件设置繁琐，并且显著增加了系统的成本。可用的系统还要求临床用户手动对齐MRI和超声体积，这被称为图像配准。图像配准是一项具有挑战性的任务，对系统的精度有很大的影响，往往导致此类系统的性能不佳。拟议的技术使用人工智能模型来消除对跟踪硬件的需求并降低图像配准的专业知识要求。 该技术可以通过提高患者吞吐量，最大限度地减少系统设置时间，并增加融合引导活检的可访问性，甚至在设备较少的诊所，来改善活检程序。该I-Corps项目基于使用人工智能（AI）的图像融合技术的开发，旨在消除癌症活检程序中使用的外部跟踪硬件的需要。 所提出的技术利用人工智能和机器学习来自动化融合过程，从而实现更高效、更准确的活检引导系统。它使用AI算法自动识别输入图像中的图像特征，用于体积重建、跨模态图像配准和帧到体积映射。 将他的技术与严重依赖外部跟踪设备来融合磁共振成像（MRI）和超声图像的传统方法进行比较的结果表明，它减少了人为干预并提高了图像融合性能。 这项技术工作已被记录在同行评审论文、专利申请和回顾性研究中，以验证该技术。 此外，该技术解决了医生面临的挑战，为提高活检准确性、降低成本和缩短学习曲线提供了机会。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。