# Multi-Center Implementation and Validation of Efficient Magnetic Resonance Imaging and Analysis of Atherosclerotic Disease of the Cervical Carotid

> **NIH NIH R01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2021 · $1,331,073

## Abstract

Abstract:
Numerous investigations over the past decades have yielded substantial innovations in MR methods for the
characterization of extracranial carotid atherosclerosis. Images obtained with these innovations under ideal
conditions have given clinicians rich information about disease in the arterial wall and the hope for tools
critically needed for adequate management of this insidious disease. Despite this, the great potential power of
this technology has not made it into the routine clinical armamentarium. Indeed, because of the need for
gadolinium-based contrast agents (GBCA), the long exam time (typically about 45 minutes to obtain the
multiple contrasts in the 5 or 6 necessary sequences), and the steep learning curve required to interpret multi-
contrast MRI most practitioners still revert to the simplified metric of diameter stenosis in assessing risk. After
many collective years of investigations, the consortium of investigators collaborating on this proposal believes
that the time is right to address these remaining limitations and ultimately shift the clinical paradigm.
Overarching hypothesis: To achieve the great potential in the management of cervical carotid disease, a
highly efficient and easily used MRI technique is required. Our hypothesis is that this can be accomplished
using multi-parametric non-contrast MRI sequences coupled with the latest high signal to noise ratio (SNR)
neck-shape-specific (NSS) RF coils and innovative machine learning (deep neural network) analysis methods.
Aim 1: We will install identical RF coils, MRI sequences, and protocols at each of our 5 participating centers as
well as rigorously test the accuracy of measurements and reproducibility of image quality from all centers. Aim
2: We will develop, train, and validate a user friendly, deep learning neural network system for the quantitative
analysis of several key components considered to be present in the vulnerable atherosclerotic plaque. Aim 3:
We will apply the analysis to a cohort of carotid disease subjects to establish the repeatability of the
quantitative measures, as well as the accuracy of characterization in comparison to histopathology. Although
we will develop and test the image quality, reproducibility and reliability in a network of highly skilled academic
centers, we will design these methods to be applicable in the community hospital setting. At the conclusion of
this project, we propose to have an integrated solution that can be used in subsequent investigations such as:
the effect of pharmacologic intervention in modifying the composition of the plaque; studying the evolution of
features of the untreated atheromatous disease over time; and, eventually, investigating the metrics that are
predictive of deleterious outcomes, and that can be used in improving intervention strategies in this population.
On successful completion, the RF coils and MRI sequences and analysis methods will be made available to
other imaging centers in a manner t...

## Key facts

- **NIH application ID:** 10280858
- **Project number:** 1R01HL159200-01
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Maria I. Altbach
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,331,073
- **Award type:** 1
- **Project period:** 2021-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10280858

## Citation

> US National Institutes of Health, RePORTER application 10280858, Multi-Center Implementation and Validation of Efficient Magnetic Resonance Imaging and Analysis of Atherosclerotic Disease of the Cervical Carotid (1R01HL159200-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10280858. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*