# Low- and Zero-dose Contrast-enhanced MRI Using Deep Learning

> **NIH NIH R44** · SUBTLE MEDICAL, INC. · 2020 · $742,405

## Abstract

Project Summary
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.

## Key facts

- **NIH application ID:** 10140491
- **Project number:** 4R44EB027560-02
- **Recipient organization:** SUBTLE MEDICAL, INC.
- **Principal Investigator:** Enhao Gong
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $742,405
- **Award type:** 4N
- **Project period:** 2020-08-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10140491

## Citation

> US National Institutes of Health, RePORTER application 10140491, Low- and Zero-dose Contrast-enhanced MRI Using Deep Learning (4R44EB027560-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10140491. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*