# Pediatric Cardiopulmonary MRI using RF Navigators and High Dimensional Deep Learning

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2024 · $754,285

## Abstract

ABSTRACT
The cardiac and pulmonary systems are inherently linked through the pulmonary vascular system which leads
to secondary pulmonary disease in cases of cardiac pathology. This is especially the case in congenital heart
disease where the pulmonary blood supply is often substantially altered by abnormal outflow tracts and
ventricular formation. Cross-sectional imaging has proven to be invaluable for assessing pediatric diseases,
including congenital heart disease; however, current cardiopulmonary evaluations typically require multiple
exams (SPECT, echo, MRI, and CT) to evaluate the cardiovascular and pulmonary systems. Each exam adds
risk to already fragile patients, introduces complex logistics of performing multiple exams, and can delay care of
patients who may require urgent management. Often, only a subset of exams are performed, and clinical
management is based on incomplete information and disregards the strong potential for cardiopulmonary
coupling. In this project, we aim to develop MRI methods that can simultaneously and efficiently evaluate both
anatomy and function in pediatric cardiopulmonary diseases. MRI is theoretically well suited for quantitatively
imaging both the cardiac and respiratory systems but is traditionally challenged by its slow imaging speed and
sensitivity to artifacts. Recently, our group has proposed methods for dramatically more robust lung imaging
using the combination of ultrashort echo time MRI with advanced motion corrected reconstruction strategies. In
this proposal, we extend these techniques and introduce novel methods to provide improved and comprehensive
diagnostics of the entire cardiopulmonary system. First, we introduce a free-running approach to
cardiopulmonary imaging to provide anatomical imaging and the quantifications of ventilation, perfusion, cardiac
function, and respiratory resolved cardiac flow dynamics. We specifically aim to image continuously with T1
weighted and velocity encoded sequences, and subsequently reconstruct this data with a high-dimensional deep
learning approach. The reconstructions use novel motion corrected methods to directly estimate images and
apply deep learning in a highly compressed space. Secondly, we aim to develop next-generation motion
management using an RF navigator technique, Beat Pilot Tone, that can be applied during any pulse sequence
to measure bulk, respiratory and cardiac motion. Beat Pilot Tone provides a basis for motion tracking that enables
improved imaging efficiency, a simplified setup without cardiac leads or respiratory belts, and much better
measures of bulk motion. These techniques will be evaluated in normal control participants and pediatric subjects
with congenital heart disease, each with comparisons to state-of-the-art imaging. The impact of this project is to
shift the paradigm for clinical management of cardiopulmonary diseases to a single-scan comprehensive imaging
study and supporting an integrated assessment of interaction between ...

## Key facts

- **NIH application ID:** 10855844
- **Project number:** 1R01HL173035-01
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Kevin Michael Johnson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $754,285
- **Award type:** 1
- **Project period:** 2024-07-01 → 2029-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10855844

## Citation

> US National Institutes of Health, RePORTER application 10855844, Pediatric Cardiopulmonary MRI using RF Navigators and High Dimensional Deep Learning (1R01HL173035-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10855844. Licensed CC0.

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