Meeting Banner
Abstract #0232

Quantification of Extracellular Volume Fraction in Cardiac MRI without Blood Sampling Using Multi-Stage Training Deep Learning

Zhuaon Li1,2, Khalid Youssef2,3, Mehdi Amian1,2, Dilek M. Yalcinkaya2,4, Venkateshwar Polsani5, Michael Elliott6, Rohan Dharmakumar2, Robert Judd7, Dipan Shah8, Orlando Simonetti9, Matthew Tong9, and Behzad Sharif1,2
1Laboratory for Translational Imaging for Microcirculation, Weldon School of Biomedical Engineering, Purdue University, Indianapolis, IN, United States, 2Department of Radiology & Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States, 3Krannert Cardiovascular Research Center, Indiana University School of Medicine/IU Health Cardiovascular Institute, Indianapolis, IN, United States, 4Laboratory for Translational Imaging for Microcirculation,Electrical and Computer Engineering, Purdue University, Indianapolis, IN, United States, 5Piedmont Heart Institute, Atlanta, GA, United States, 6Atrium Health, Charlotte, NC, United States, 7Intelerad, Durham, NC, United States, 8Houston Methodist DeBakey Heart and Vascular Center, Houston, TX, United States, 9The Ohio State University, Columbus, OH, United States

Synopsis

Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence, Extracellular Volume Fraction, Hematocrit

Motivation: Blood sampling for hematocrit (HCT) measurement limits clinical use of extracellular volume fraction (ECV) in diagnosing myocardial diseases.

Goal(s): Develop and evaluate a deep learning (DL) model to predict HCT from cardiovascular magnetic resonance (CMR) data, exploring if additional features improve predictability across multiple centers.

Approach: Trained a multi-stage DL model using multi-center CMR T1 values and clinical features to predict HCT without blood sampling.

Results: The DL model identified native blood-pool T1 and gender as optimal features, achieving higher correlation with true HCT (R=0.65) than linear regression (R=0.59) and strong agreement between synthetic and true ECV (R=0.95).

Impact: This study shows that incorporating additional features in a DL model enhances HCT prediction from CMR data, eliminating the need for blood sampling. This advancement could streamline ECV measurement, making it more accessible for diagnosing myocardial diseases in clinical settings.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Abstract

Keywords