Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( 🔒 ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Need Help

Quantitative MRI and Deep Learning Technologies for Classification of NAFLD

Award Number: U01EB031894
ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING
OPDIV: NIH
AWARD CLASS: COOPERATIVE AGREEMENT
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 08/01/2022
PERIOD OF PERFORMANCE END DATE: 04/30/2027

Group Awards By:

View Award Description

Quantitative MRI and Deep Learning Technologies for Classification of NAFLD - PROJECT SUMMARY Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in the U.S. and ranges from simple fatty liver (or non-alcoholic fatty liver, NAFL) to the progressive form, non-alcoholic steatohepatitis (NASH). About 20-30% of subjects with NAFL develop NASH, which is caused by hepatocyte injury, hepatic inflammation, and resultant hepatic fibrosis. NASH can lead to life-threatening conditions, but is difficult to diagnose at early stages. Liver biopsy is the current standard to diagnose NAFL/NASH, but biopsy is invasive, has associated morbidity, and is limited by sampling errors and inter-observer variability. Many patients present with later stage NASH, adversely impacting outcomes and healthcare costs, which are estimated at $32 billion annually in the U.S. Magnetic resonance imaging (MRI), including elastography (MRE), is a technology that can non-invasively quantify hepatic fat (MRI proton-density fat fraction), iron overload (MRI R2*), and fibrosis (MRE stiffness). However, current liver MRI is challenged by motion artifacts and incomplete signal models, which can compromise the accuracy and reproducibility of the quantitative parameters derived from them. In addition, early tissue changes associated with NASH are not adequately characterized using conventional MRI. The common requirements of breath-holding and long protocols also severely limit the adoption of liver MRI in the clinic. Furthermore, the present clinical interpretation of MRI has limited ability to distinguish NASH from NAFL. The research teams at the University of California Los Angeles, University of Arizona, and Siemens have been leading the development of motion-robust radial MRI to quantify hepatic PDFF and R2*, T2 and T1, perfusion, and stiffness. The Siemens team has also developed deep learning methods for medical image processing and disease detection and classification. In this bioengineering research partnership project, the multi-disciplinary research team will investigate four aims: (1) Develop a robust motion compensation framework for free-breathing multi-parametric quantitative radial liver MRI; (2) Accelerate quantitative liver MRI scans through combined acquisition and joint modeling of multiple parameters, data undersampling, and deep learning-based reconstruction and quantification; (3) Develop deep learning models to accurately classify NAFL versus NASH and measure the degree of fibrosis based on quantitative MRI; (4) Prospectively assess the new quantitative MRI and deep learning technologies for classifying NAFL versus NASH and measuring fibrosis in patients, with respect to liver biopsy. The new free- breathing quantitative MRI and deep learning technologies developed in this project will accurately classify NAFL versus NASH and measure fibrosis using data from the entire liver and thus help to avoid liver biopsy, allow monitoring of treatment responses, and accelerate the development and implementation of new therapies.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $608,848 )
2026	2026	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	001	5	4/15/2026	NON-COMPETING CONTINUATION	$608,848
2026	2025	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	4	2/9/2026	NON-COMPETING CONTINUATION	$0
														Subtotal = $608,848

Issue Date FY: 2025 ( Subtotal = $610,185 )
2025	2025	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	001	4	4/29/2025	NON-COMPETING CONTINUATION	$610,185
2025	2024	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	002	3	5/6/2025	NON-COMPETING CONTINUATION	$0
2025	2023	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	2	10/23/2024	NON-COMPETING CONTINUATION	$0
														Subtotal = $610,185

Issue Date FY: 2024 ( Subtotal = $570,640 )
2024	2024	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	3	5/8/2024	NON-COMPETING CONTINUATION	$570,640
2024	2022	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	001	1	7/18/2024	NEW	$0
														Subtotal = $570,640

Issue Date FY: 2023 ( Subtotal = $574,875 )
2023	2023	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	001	2	5/11/2023	NON-COMPETING CONTINUATION	$574,875
2023	2022	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	1	2/15/2023	NEW	$0
														Subtotal = $574,875

Issue Date FY: 2022 ( Subtotal = $608,300 )
2022	2022	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	1	7/19/2022	NEW	$608,300
														Subtotal = $608,300

Grand Total All Awards = $2,972,848

Top

All Categories

About

Search

Reports

Data Submission

Award Information

Quantitative MRI and Deep Learning Technologies for Classification of NAFLD

Award Number: U01EB031894

ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING

OPDIV: NIH

AWARD CLASS: COOPERATIVE AGREEMENT

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 08/01/2022

PERIOD OF PERFORMANCE END DATE: 04/30/2027

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer