Motivation: Gadolinium-based contrast agents (GBCAs) are used in approximately a third of all MRI scans. The unique relaxation parameters of GBCAs create indispensable image contrast for a wide range of clinical applications, such as angiography and tumor detection. However, the usage of GBCAs has been linked to the development of nephrogenic systemic fibrosis (NSF). NSF can be painful, cause severe disability, and even death. The risk of developing NSF prevents millions of patients with advanced chronic kidney disease (CKD) from receiving contrast-enhanced MRI exams. The recent identification of gadolinium deposition within the brain and body has raised additional safety concerns about the usage of GBCAs. Studies have demonstrated increased signal intensity on the unenhanced T1-weighted MR images that is correlated with previous GBCA exposure, and this gadolinium retention is independent of renal function. While initial reports focused on linear GBCAs, more recent reports show that gadolinium deposition occurs with macrocyclic GBCAs as well, albeit at lower levels. FDA has recently issued warnings about gadolinium retention following contrast-enhanced MRI, and required GBCA manufacturers to conduct human and animal studies to further assess the safety of these contrast agents. This project addresses these concerns by developing low-dose and zero-dose contrast-enhanced MRI using artificial intelligence (AI) and deep learning (DL). Approach: This fast-track project has two phases and three aims.
Aim 1 (Phase I) is to develop a DL method that can synthesize full-dose contrast-enhanced MR images using pre-contrast images and contrast-enhanced images acquired with only 10% of standard GBCA dose. A software infrastructure will be constructed to seamlessly integrate the DL software between MR scanners and PACS.
Aim 2 (Phase II) is to develop a DL method that can synthesize full-dose contrast-enhanced MR images using GBCA-free acquisitions with different image contrast.
In Aim 3 (Phase II), we will clinically validate and evaluate both low-dose and zero-dose DL methods, including on patients with mild- to-moderate CKD. Non-inferiority tests and diagnostic performance of the synthesized full-dose images compared to the true full-dose images will be performed. Significance: This work will lead to safer contrast-enhanced MRI. The low-dose and zero-dose contrast-enhanced MRI method will benefit not only millions of patients with advanced CKD, who cannot currently undergo contrast-enhanced MRI, but many more patients with normal kidney function, who are at the risk of gadolinium retention after contrast-enhanced MRI.
Gadolinium-based contrast agents (GBCAs) are widely used in MRI exams to create indispensable image contrast for monitoring treatment and investigating pathology and function. However, the usage of GBCAs has been linked to the development of nephrogenic systemic fibrosis, preventing patients with advanced chronic kidney disease from receiving contrast-enhanced MRI exams, as well as potential gadolinium deposition in the body and brain for patients with normal kidney function. This project aims to address these problems by developing and validating low-dose and zero-dose contrast-enhanced MRI using deep learning. !
