# Novel methods for earlier detection of coronary artery calcium using CT

> **NIH NIH F31** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2022 · $12,721

## Abstract

Project Summary/Abstract
Coronary heart disease (CHD) is the most common form of heart disease, the leading cause of death worldwide.
Traditional risk prediction tools such as the Framingham Risk Score use clinical biomarkers to identify individuals
who are of higher risk for an adverse CHD event and would therefore benefit from preventive LDL-C lowering
therapy. However, these tools identify very few younger individuals who are at increased risk, despite the fact
that longer cumulative exposure to lower LDL-C shows increased protection from CHD events. Coronary artery
calcium (CAC) measured via CT is the best novel predictor of CHD events and is predictive of CHD events
among young individuals. Recent guideline updates recommend LDL-C lowering therapy in individuals with any
detectable CAC. Coronary calcification progresses with age, so scanning all younger individuals will result in a
very low positive rate of detection. Age independent risk factors, such as genetic risk, are needed to determine
which younger individuals will benefit the most from early calcium screening. Additionally, current CAC scoring
methods are based on outdated technology, and there is a critical need to understand scan sensitivity to non-
zero CAC detection. CAC detection is a function of both plaque size and calcium density, and the influence of
motion introduces an additional technical challenge. The focus of this work is to develop novel image scanning
and reconstruction methods for earlier CAC detection using CT and to apply innovative risk prediction tools to
develop a successful CAC screening strategy. This work will identify the appropriate age for each individual to
begin CAC screening with CT using the age independent risk factors of sex and genetics. Additionally, the
minimum detectable mass of CAC by CT will be assessed and improved by optimizing scan acquisition and
reconstruction methods. Finally, new image analysis techniques for discovery of non-zero CAC in standard
helical chest CT scans with motion artifact will be derived. The proposed project will establish novel scan
acquisition, image reconstruction, and risk prediction tools for CAC detection that will improve CAC measurement
as a screening strategy for CHD. These developments will permit earlier detection of CAC in young individuals
who have previously been misclassified as disease free.

## Key facts

- **NIH application ID:** 10460282
- **Project number:** 5F31HL151081-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Lauren Severance
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $12,721
- **Award type:** 5
- **Project period:** 2020-04-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10460282

## Citation

> US National Institutes of Health, RePORTER application 10460282, Novel methods for earlier detection of coronary artery calcium using CT (5F31HL151081-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10460282. Licensed CC0.

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