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

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.