# Next-Generation Cardiovascular MRI powered by Artificial Intelligence

> **NIH NIH R21** · NORTHWESTERN UNIVERSITY · 2022 · $230,441

## Abstract

Project Summary/Abstract: Despite the accuracy and versatility of cardiovascular MRI, its footprint is only
1% among cardiac imaging tests (SPECT, echocardiography, CT, MRI) in the US. While there are several
factors such as referral patterns favoring SPECT and echocardiography among cardiologists that account for
low utilization, the two addressable obstacles that preclude widespread adoption are lengthy scan time
(imaging facility operational cost) and reading (physician cost). These obstacles must be addressed for
community hospitals with limited resources to adopt cardiovascular MRI into clinical routine practice.
While compressed sensing (CS), since its introduction into the MRI world in 2007, has led to highly-accelerated
cardiovascular MRI acquisitions, the subsequent image reconstruction remains too slow (> 5 min for 2D time
series, > 1 hour for 3D time series) for clinical translation (unmet need 1). Downstream, image analysis for
cardiovascular MRI is notoriously labor intensive (e.g. 30- to 60-min) and limited (“circles” at two cardiac
phases for cine MRI, whereas perfusion and late gadolinium-enhanced (LGE) images are evaluated visually),
for what is essentially a basic computer vision task (unmet need 2). In direct response, we will address these
two unmet needs and unlock the enormous potential of CMR using deep learning (DL).
DL applications have exploded since advancements in optimization and GPU hardware. While several recent
studies have applied neural networks such as convolutional neural networks (CNNs), U-Nets, and Generative
Adversarial Nets (GANs) for reconstruction and segmentation, no study has implemented an inline end-to-end
pipeline that receives raw k-space from the MRI scanner and delivers both reconstructed images and fully
processed images automatically with high speed (< 1 min). The objectives of this study are: a) developing a
network for image reconstruction with maximal acceleration (aim 1), (b) developing a network for image
processing tasks (aim 2), and c) developing an integrated, end-to-end network that does both (aim 3). By
developing an architecture that can simultaneously learn maximal acceleration, fine tune end-to-end
performance, and perform reconstruction/inference using feed-forward networks, we anticipate a disruptive
technology that will lead to a paradigm shift in cardiovascular MRI and increase its footprint in community
hospitals. This 2-year study is doable because of the requisite database of raw k-space (not derived from
DICOM) data (N = 617) and annotated cardiac MR images (N=3,021) from over 3,000 patients existing at our
institution. Success of this proposal will deliver a disruptive technology that has potential to cause a paradigm
shift in cardiovascular MRI and enable widespread adoption of cardiovascular MRI into clinical routine practice.

## Key facts

- **NIH application ID:** 10385754
- **Project number:** 5R21EB030806-02
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** AGGELOS K KATSAGGELOS
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $230,441
- **Award type:** 5
- **Project period:** 2021-04-15 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10385754

## Citation

> US National Institutes of Health, RePORTER application 10385754, Next-Generation Cardiovascular MRI powered by Artificial Intelligence (5R21EB030806-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10385754. Licensed CC0.

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