# Non-contrast 3D T1p Mapping for Myocardial Fibrosis Quantification of Pediatric Cardiomyopathy Patients

> **NIH NIH K99** · NORTHWESTERN UNIVERSITY · 2022 · $98,695

## Abstract

PROJECT SUMMARY
 The development of myocardial fibrosis is associated with nearly all forms of pediatric
heart disease including hypertrophic cardiomyopathy, congenital heart disease, diastolic
dysfunction, arrhythmia, myocarditis, and sudden cardiac death. Despite the pervasive nature of
myocardial fibrosis, the current technology available to detect fibrosis is suboptimal for studying
pediatric cardiomyopathy. Cardiac MRI (CMR) is the gold standard noninvasive screening tool to
detect both diffuse and focal fibrosis, through extracellular volume (ECV) and late gadolinium
enhancement (LGE) imaging, respectively. Unfortunately, both ECV and LGE CMR require the
administration of a gadolinium-based contrast agent (GBCA), which accumulates in the brain
even when renal function is normal, including in children. In addition, traditional CMR requires
subjects to hold their breath for accurate imaging. However, many pediatric patients cannot
adequately hold their breath and so are put under general anesthesia (GA), which is not ideal as
GA poses an additional health risk and significant financial cost. Furthermore, the current 2D
techniques for fibrosis imaging have insufficient spatial resolution, and thus are only able to
acquire data in sections of the left ventricle (6-10 mm thick) of the heart, completely missing
fibrosis information in the right ventricle (3-5 mm thick), which is known to be the substrate for
some tachycardia arrhythmias. Therefore,
breathing, T1ρ mapping is a
promising non-contrast CMR technique that can be used to detect both focal and diffuse
myocardial fibrosis. Despite its enormous potential for assessment of myocardial fibrosis in
pediatric patients, cardiac T1ρ mapping suffers from several technical limitations: (a) poor spatial
resolution, (b) long scan time (up to 18 min), and (c) undeveloped pipeline for clinical integration.
Additionally, the volumetric cardiac T1ρ mapping sequences that have been developed have only
been tested on adult patients, and in very few subjects (n < 15). Therefore, in this study, I seek to
address these limitations of 3D cardiac T1ρ mapping by (1) using innovative k-space sampling
with deep learning for achieving unprecedented image quality with acceptable scan and
reconstruction time, (2) implementing deep learning to automate image analysis and fibrosis
quantification to make the information readily accessible for patient care, and (3) scanning a large
population of pediatric patients to make this the most comprehensive T1ρ mapping study to date.
there is a strong need to develop a non-contrast, free-
 volumetric imaging test for detecting fibrosis in pediatric patients.

## Key facts

- **NIH application ID:** 10351919
- **Project number:** 1K99HL161469-01
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** Suvai Gunasekaran
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $98,695
- **Award type:** 1
- **Project period:** 2022-03-01 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10351919

## Citation

> US National Institutes of Health, RePORTER application 10351919, Non-contrast 3D T1p Mapping for Myocardial Fibrosis Quantification of Pediatric Cardiomyopathy Patients (1K99HL161469-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10351919. Licensed CC0.

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