Chemical “contrast agents” play a crucial role in modern cancer diagnosis by enhancing the accuracy of medical imaging techniques such as X-rays, computed tomography (CT), and magnetic resonance imaging (MRI).
However, the use of these agents can be expensive, time-consuming, and potentially risky to one’s health.
Researchers at Western Reserve University Event aim to develop an artificial intelligence (AI) alternative that can generate virtual contrast-enhanced images without the need for chemical agents. This initiative is made possible by a new four-year, $1.125 million grant from the National Science Foundation (NSF).
Their goal is to conduct a safer, faster, and more cost-effective imaging process using only non-contrast images.
The interdisciplinary team is committed to improving the medical scanning process for both healthcare providers and patients through the utilization of this grant. By implementing electronic contrast-enhanced scanning, we aim to streamline procedures while ensuring optimal patient care.
Shuo Li, associate professor and project leader at the Case School of Engineering (CSE)
This project is jointly led by Vipin Chaudhary, the Kevin J. Kranzusch Professor and head of computer and data sciences at CSE, Leonardo Kayat Bittencourt, an associate professor at the School of Medicine and head of innovation in imaging at UH Cleveland Medical Center, and Sree Harsha Tirumani, a senior professor in the School of Medicine and vice chairman for scientific research in radiology at the University of Cleveland. The Case Center for Imaging Research team comprises Li, Kayat, and Tirumani.
The Challenge
While chemical contrast agents are currently the standard for diagnosing various diseases, the increasing patient volume, associated costs, and potential side effects pose significant challenges. Risks to patients include rare allergic reactions and other short-term adverse effects.
Introducing a non-chemical scanning approach could address these challenges, potentially saving time and costs. Such a method could also help alleviate concerns related to possible supply chain issues like the recent global shortage of iodinated contrast media in 2022.
The Project
This endeavor capitalizes on the engineering and medical expertise of the research team to develop a novel, virtual method of delivering essential diagnostic information to healthcare providers and patients, all without the need for chemical contrast agents.
To enhance and validate new models for MRI imaging, the researchers will explore novel image features. By leveraging the unique capabilities of AI, the team is developing an AI-based contrast agent technique.
This initiative is supported by the NSF Smart Health and Biomedical Research in the Age of Artificial Intelligence and Advanced Data Science program, which promotes advancements in data science, engineering, mathematics, statistics, behavioral, and mental research for the greater good.
