# Machine learning for fast motion compensated quantitative abdominal DCE-MRI

> **NIH NIH R21** · BOSTON CHILDREN'S HOSPITAL · 2020 · $708,000

## Abstract

Project Summary:
Functional imaging with dynamic contrast-enhanced MRI (DCE-MRI) provides important physiological markers
of permeability, perfusion and glomerular filtration rate (GFR), a measure of kidney function, without exposing
patients to ionizing radiation. DCE-MR images are at the same time used for evaluation of anatomy. Functional
markers from DCE-MRI, if computed accurately, would play a critical role in diagnosing and assessing the
progression of a number of pediatric diseases including those compromising kidney function, liver diseases,
tumors, and Crohn's disease. One of the most important applications of DCE-MRI is assessing kidney function
(GFR) in hydronephrosis patients with obstruction. In the absence of GFR information, children who stand to
benefit from immediate surgical reconstruction might be overlooked or delayed in receiving treatment, and
those who might benefit from a more conservative approach (i.e., “watchful waiting”) might receive an
unnecessary surgical intervention. While the current reference standard, nuclear renography (MAG3), yields
some useful diagnostic information, it is slow, provides low resolution, does not offer anatomic detail, and
delivers potentially harmful ionizing radiation. There is a clinical need for accurate computation of quantitative
functional markers. Unfortunately, current methods of DCE-MRI in neonates and children are less than optimal,
and therefore, DCE-MRI is underutilized in clinical practice. The technical challenges include insufficient
temporal resolution to capture fast arterial input function (AIF) dynamics (which are required for accurate
computation of quantitative markers), unavoidable respiratory motion and bulk motion (which reduce image
quality and significantly lower the accuracy of parameter estimates), and a lack of robust, fast, automated post
processing techniques for accurate computation of markers. Thus, there is an urgent, unmet need to develop a
motion-compensated, high spatiotemporal resolution DCE-MRI method addressing these challenges. The
primary objective of this exploratory, three-year study, is three-fold: first, to develop and evaluate a new bulk
and respiratory motion-compensated, high spatiotemporal resolution DCE-MRI technique for accurate
estimation of functional markers; second, to further improve the robustness and speed of DCE-MRI using a
fast, deep learning (DL) technique with integrated temporal prior for the reconstruction of motion-compensated,
higher quality, high temporal resolution images; and third, to develop an automatic quantitative analysis
pipeline including segmentation and tracer kinetic model-fitting using DL techniques for fast, robust and
accurate quantification of functional markers. The successful completion of these aims will provide new,
clinically important abdominal imaging capabilities, with real-time, motion-compensated image reconstruction
and reliable real-time parameter estimation from high temporal and spatial resolu...

## Key facts

- **NIH application ID:** 9957672
- **Project number:** 1R21EB029627-01
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Sila Kurugol
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $708,000
- **Award type:** 1
- **Project period:** 2020-09-15 → 2024-09-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9957672

## Citation

> US National Institutes of Health, RePORTER application 9957672, Machine learning for fast motion compensated quantitative abdominal DCE-MRI (1R21EB029627-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9957672. Licensed CC0.

---

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