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.

该I-Corps项目的更广泛的影响/商业潜力是开发人工智能（AI）驱动的图像融合技术，旨在指导癌症活检。目前，指导活检员工融合磁共振成像（MRI）和超声波的黄金标准方法，可以将癌症活检的产量提高30％。但是，通过Fusion指导进行了不到20％的程序。现有的融合MRI和超声图像的方法需要外部跟踪硬件，该硬件像“医疗GPS”，以跟踪超声探针的位置和方向。该硬件很麻烦，可以为系统增加巨大的成本。可用的系统还要求临床用户手动对齐MRI和超声卷，这称为图像注册。图像注册是一项挑战任务，对系统的准确性产生了重大影响，通常会导致此类系统的性能不佳。拟议的技术使用AI模型来消除跟踪硬件的需求，并减少图像注册的专业知识需求。该技术也可以通过增强患者吞吐量，最大程度地减少系统设置时间来改善活检程序，并在设备齐全的诊所中提高融合引导活检的可访问性。这项I-Corps项目基于使用人工智能（AI）开发图像融合技术（AI），旨在消除用于消除癌症癌症生物生物生物生物生物生物生物生物生物生物生物生物生物的需求。提出的技术利用AI和机器学习来自动化融合过程，从而实现了更有效，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，以进行音量重建，交叉模态图像注册以及帧映射框架。结果将他的技术与传统方法进行比较，这些方法在很大程度上取决于融合磁共振成像（MRI）和超声图像的外部跟踪设备，表明它可以减少人类干预并改善图像融合性能。该技术工作已在经过同行评审的论文，专利申请和回顾性研究中进行了记录，以验证该技术。此外，拟议的技术解决了医生面临的挑战，提供了提高活检准确性，降低成本和缩短学习曲线的机会。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估来评估来获得支持的。