# Reading workstation for clinical contrast echocardiography

> **NIH NIH R43** · NARNAR, LLC · 2021 · $252,399

## Abstract

Proposal Summary
There is increasing appreciation of a syndrome in which patients female patients, present with chest
pain due to myocardial ischemia and have a normal or near normal coronary angiogram. Termed
coronary microvascular dysfunction (MVD) this disorder is not benign with cardiovascular event rates
similar to those with established coronary artery disease. Clinical tools are therefore needed to both
identify MVD patients and better understand the mechanisms causing myocardial ischemia. There is
evidence that myocardial contrast echocardiography (MCE) provides incremental information in the
evaluation of patients with coronary artery disease, myocardial viability, or diseases of the
microvasculature. Despite data demonstrating the diagnostic and prognostic benefit of MCE in
evaluating patients with MVD, its clinical use has been limited to only a handful of experts in the field,
because there are currently no widely available clinical tools to support MCE quantitative analysis and
interpretation. The overall aim of this Phase I proposal is to provide clinicians with a new tool to
evaluate the myocardial flow-function relationship that is critical to identifying patients with MVD by
using echocardiography. We will develop clinical software that can rapidly process MCE data into a
standardized, quantitative and easy- to- interpret format. In Aim 1, the power of image averaging and
computer aided assessment of radial wall thickening will be used to enhance the current standard of care
which relies solely on readers' visual estimation of segmental function. An algorithm will be developed to
rearrange the order of images so that images representing the same phase of the cardiac cycle are
grouped together. Functional analysis will then be developed using computer-aided tracings of epicardial
and endocardial borders. In Aim 2, a software module for quantitative analysis of real-time MCE
perfusion will be developed that will incorporate statistical confidence, derived from the performance of
image processing algorithms to inform the interpreter about the data strength. Machine learning will be
utilized to train and deploy a neural network for the pixel-by-pixel assessment of myocardial perfusion.
In Aim 3, we will combine myocardial perfusion and function modules into a novel, perfusion-function
mode of imaging (PF-mode). This new mode will be applied to an archival sample of clinically diagnosed
MVD cases to demonstrate the feasibility to detect abnormalities in the myocardial flow-function
relationship. The composite PF-mode will include a cine-loop rendered for one cardiac cycle where
parametric images (perfusion) are superimposed over averaged ultrasound images with an overlay of
graphic representation of wall thickness (function). This novel mode of imaging provides the means to
diagnose MVD in a single clinical study.

## Key facts

- **NIH application ID:** 10155647
- **Project number:** 1R43HL152939-01A1
- **Recipient organization:** NARNAR, LLC
- **Principal Investigator:** Brian Perez Davidson
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $252,399
- **Award type:** 1
- **Project period:** 2021-03-01 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10155647

## Citation

> US National Institutes of Health, RePORTER application 10155647, Reading workstation for clinical contrast echocardiography (1R43HL152939-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10155647. Licensed CC0.

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