# Cardiac CT Deblooming

> **NIH NIH R01** · GENERAL ELECTRIC GLOBAL RESEARCH CTR · 2020 · $900,092

## Abstract

PROJECT SUMMARY/ABSTRACT
Coronary artery disease (CAD) is the most common type of heart disease, killing over 370,000 Americans annu-
ally2. Cardiac CT is a safe, accurate, non-invasive method widely employed for diagnosis of CAD and planning
therapeutic interventions. With the current CT technology, calcium blooming artifacts severely limit the accuracy
of coronary stenosis assessment. Similarly, stent blooming artifacts lead to overestimation of in-stent restenosis.
As a result, many coronary CT angiography (CCTA) scans are non-diagnostic and result in patients receiving
costly and invasive coronary angiography (ICA) procedures.
 Based on extensive feasibility results, the goal of this project is to use deep learning innovations to fundamen-
tally eliminate blooming artifacts without costly redesign of the CT hardware. A consortium between GE Re-
search, Rensselaer Polytechnic Institute and Weill Cornell Medicine will develop dedicated imaging protocols
and machine learning methods to avoid or minimize blooming artifacts and evaluate the clinical impact of the
proposed solutions. In Aim 1, the CT scan protocol will be optimized and paired with deep learning reconstruc-
tion and post-processing algorithms to generate high-resolution CT images and prevent blooming artifacts. In
Aim 2, image-domain and raw-data-domain deep learning processing algorithms will be developed to correct for
residual blooming. After successful demonstration of the proposed methods on phantom scans and emulated
clinical datasets, in Aim 3 the proposed CT methods will be clinically demonstrated and optimized based on 100
patients with coronary artery disease, using intravascular ultrasound as the ground-truth reference.
 At the end of the project, we will have demonstrated and publicly disseminated a systematic methodology to
essentially remove blooming artifacts in cardiac CT without a costly hardware upgrade. This will be another suc-
cess of deep learning, enabling accurate coronary stenosis assessment and eliminating many unnecessary diag-
nostic catheterizations.

## Key facts

- **NIH application ID:** 9943684
- **Project number:** 1R01HL151561-01
- **Recipient organization:** GENERAL ELECTRIC GLOBAL RESEARCH CTR
- **Principal Investigator:** Bruno K De Man
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $900,092
- **Award type:** 1
- **Project period:** 2020-09-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9943684

## Citation

> US National Institutes of Health, RePORTER application 9943684, Cardiac CT Deblooming (1R01HL151561-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9943684. Licensed CC0.

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