Multiparametric Cardiac MRI for the Assessment and Stratification of Cardiac Allograft Vasculopathy in Heart Transplant Patients

Thursday, January 30, 2025

3:30 PM – 3:40 PM East Coast USA Time

Location: Empire Ballroom

Presenting Author(s)

Bruno B. Lima, MD, PhD

Assistant Professor of Medicine
Vanderbilt University Medical Center

Primary Author(s)

Bruno B. Lima, MD, PhD

Assistant Professor of Medicine
Vanderbilt University Medical Center

Co-Author(s)

SH

Sean G. Hughes, MD

Associate Professor of Clinical Medicine and Radiology Director, Cardiac MRI and Advanced Cardiac Imaging Fellowship
Vanderbilt University Medical Center
Tania Ruiz M., MD

Assistant Professor of Medicine
Vanderbilt University Medical Center
JD

Jeffrey M. Dendy, MD

Assistant Professor of Medicine
Vanderbilt University Medical Center
Jonathan Soslow, MD, MSCI

Professor
Vanderbilt University Medical Center
Vanderbilt
JW

John A. Wells, IV, MD

Assistant Professor of Clinical Medicine
Vanderbilt University Medical Center
LL

Luke Laws, MD

Clinical Fellow
Vanderbilt University Medical Center
JC

Jeff Carr, MD

Cornelius Vanderbilt Professor
Vanderbilt University Medical Center
CC

Chloe Carr, MD

Resident
Vanderbilt University Medical Center
KA

Kaushik Amancherla, MD

Assistant Professor of Medicine
Vanderbilt University Medical Center
QW

Quinn S. Wells, MD

Associate Professor
Vanderbilt Univ. Med Ctr
KS

Kelly Schlendorf, MD

Assistant Professor of Medicine and Director, Heart Failure and Transplant Program
Vanderbilt University Medical Center

Need statement: Cardiac Allograft Vasculopathy (CAV) is a leading cause of late mortality in heart transplant recipients. Current standard diagnostic approaches, such as Invasive Coronary Angiography (ICA), carry significant procedural risks, especially for patients with renal dysfunction. There is an unmet need for a non-invasive, comprehensive, and cost-effective diagnostic approach that can reliably detect and grade CAV severity, minimizing patient risk and healthcare burden.

Measurable Goal: To develop a multiparametric Cardiac MRI (CMR) algorithm incorporating parametric mapping (native T1, T2), perfusion imaging, and Late Gadolinium Enhancement (LGE) to accurately detect and stratify CAV severity with ≥80% accuracy, thereby providing an effective alternative to ICA and PET imaging.

Current vs. Goal Capabilities: Currently, ICA is the gold standard for diagnosing CAV, but it is invasive, carries procedural risks, and is not suitable for frequent monitoring. PET perfusion imaging is another option but is limited by high costs and the need for specialized infrastructure. The goal is to replace these invasive and expensive techniques with a multiparametric CMR approach that integrates tissue characterization (native T1, T2), perfusion metrics, and fibrosis detection (LGE), thereby providing a safer and equally effective screening option.

Must-Have Features:

A comprehensive CMR protocol incorporating native T1 and T2 mapping, perfusion imaging, and LGE.

High diagnostic accuracy for detecting moderate-to-severe CAV.

Reproducibility and standardization of results across different CMR platforms to ensure widespread adoption.

Nice-to-Have Features:

Integration of machine learning algorithms to automate the grading of CAV and improve diagnostic accuracy.

Shortened scanning time to improve patient comfort and enhance clinical workflow efficiency.

Compatibility with existing hospital CMR systems to facilitate implementation and reduce additional costs.