# Improved MRI guidance of pediatric catheterization via autonomous multi-beat data synthesis

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2022 · $627,572

## Abstract

Project Summary
 Cardiovascular catheterization is a minimally-invasive approach to measure hemodynamics and treat
abnormalities, such as vascular stenosis. Conventionally, X-ray fluoroscopy guides catheterizations, but it uses
ionizing radiation and suffers from poor soft tissue contrast even with the use of exogenous contrast agents.
This is problematic in children as they are particularly susceptible to radiation, often require repeat
assessments, and can have complex anatomy that is difficult to navigate. MRI guidance provides a non-
ionizing alternative that can improve soft tissue contrast and enhance evaluations. In addition to guiding
diagnostic catheterization, first-in-human studies 15 years ago demonstrated that MRI could guide cardiac
interventions such as coarctation angioplasty. However, clinical adoption since then has been limited by 1) few
MRI-safe interventional devices and 2) the relatively poor real-time MRI image quality available during
procedures. The number of MRI-safe devices is increasing, and low-field MRI has emerged as a new way to
significantly reduce device heating. Yet, poor real-time image quality remains a significant barrier. Despite
parallel imaging and accelerated image reconstructions, image quality is constrained by the limited set of MRI
samples available for image reconstruction in a single-shot, real-time acquisition.
 The solution: This proposal aims to leverage multi-beat information to improve real-time MRI image
quality during pediatric interventions. The goal is to improve image quality by increasing data available for
image reconstruction and improve device visualization by highlighting motion between beats. The central
hypothesis is that, in the interventional setting, multi-beat information will improve real-time MRI image quality
and device visualization, leading to improved operator confidence and performance. To test this hypothesis,
multi-beat’s impact on interventional image quality (Aim 1), impact on device visualization (Aim 2), and clinical
utility in pediatric patients (Aim 3) are evaluated. The proposal is supported by a team that includes pediatric
cardiologists at the forefront of MRI-guided interventions and prior research experience developing a closed-
loop MRI data collection technique which robustly identifies similar heartbeats. The platform was originally
designed for a different application – improving non-interventional MRI imaging of adults with arrythmia. Here,
it is adapted to explore a new imaging paradigm (interventional imaging) in a new patient population (pediatric
patients) for the PI. The work is expected to establish a new approach for real-time MRI-guided cardiovascular
interventions by addressing the areas that currently limit image guided confidence: poor anatomic and device
visualization. Success would not only improve MRI guidance of cardiovascular interventions in children but
also create new opportunities for MRI guidance of cardiovascular and non-cardiovascu...

## Key facts

- **NIH application ID:** 10412491
- **Project number:** 1R01HL162671-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Francisco J Contijoch
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $627,572
- **Award type:** 1
- **Project period:** 2022-06-15 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10412491

## Citation

> US National Institutes of Health, RePORTER application 10412491, Improved MRI guidance of pediatric catheterization via autonomous multi-beat data synthesis (1R01HL162671-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10412491. Licensed CC0.

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