# Novel Computational Framework for Free-Breathing & Ungated Dynamic MRI

> **NIH NIH R01** · UNIVERSITY OF VIRGINIA · 2024 · $548,956

## Abstract

Pulmonary hypertension (PH) is a chronic disease with high mortality. Several pulmonary
vasodilators that reduce morbidity and mortality, especially with earlier initiation, have been
FDA approved. However, the identification of individuals that would benefit from such therapies
currently requires extensive testing of both the heart and the lungs using multiple modalities,
resulting in high healthcare costs and delay in diagnosis. This proposal seeks to introduce an MR
imaging imaging protocol to diagnose PH within a single imaging session by providing
assessments of both cardiac and pulmonary systems. Current MRI methods have several
limitations in the above setting. This renewal application aims to overcome these drawbacks
using a novel generative SToRM (g-SToRM) framework, which capitalizes on the recent
advances in deep generative models and unsupervised learning. This framework significantly
improves the analysis manifold regularization framework (SToRM) for cardiac MRI, developed
in the previous project. The proposed imaging methods will be validated by comparisons against
current breath-held MRI and CT imaging protocols. The preliminary utility of the quantitative
metrics to predict PH will also be determined.

## Key facts

- **NIH application ID:** 11160215
- **Project number:** 7R01EB019961-06
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** Mathews Jacob
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $548,956
- **Award type:** 7
- **Project period:** 2016-04-01 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11160215

## Citation

> US National Institutes of Health, RePORTER application 11160215, Novel Computational Framework for Free-Breathing & Ungated Dynamic MRI (7R01EB019961-06). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/11160215. Licensed CC0.

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