# Motion-Resistant Background Subtraction Angiography with Deep Learning:  Real-Time, Edge Hardware Implementation and Product Development

> **NIH NIH R41** · CLEARVOYA LLC · 2023 · $256,879

## Abstract

Catheter Digital Subtraction Angiography (DSA) is an imaging technique that was developed in the 1980s to
allow physicians to visualize blood vessels. Today, this technology is utilized for minimally-invasive interventions that
treat numerous devastating pathologies, including stroke and myocardial infarction, diseases that disproportionally
impact underserved minority patient populations.
 Catheter angiography is performed by inserting a small catheter into an artery, injecting iodinated contrast
through the catheter, and recording a series of X-Ray images as the contrast traverses the patient’s blood vessels.
However, superimposed X-Ray densities from bones and soft tissues obscure the imaging details of the blood vessels. In
ideal conditions, DSA will provide an image of the vessels alone, unobscured by superimposed bone and soft tissue.
Indeed, during angiography of cooperative awake patients, who are instructed to hold their breath to reduce motion,
DSA can produce excellent images. However, DSA images are markedly degraded by all voluntary, respiratory, or cardiac
motion that occurs during the exam. During routine clinical practice, it is common to discard and repeat angiographic
acquisitions due to excessive motion. In situations where patients are unable to remain still, which may be due to
difficulty breathing or the distress of an acute stroke, the poor quality of motion-degraded DSA imaging increases the
risk of complex procedures such as stroke clot removal and cardiac stenting.
 We have developed a deep learning algorithm that can perform the task of DSA even in the setting of substantial
motion. We utilize a cutting edge Vision-Transformer-based network architecture, which is optimized to use the spatial
and temporal information in the images to identify the blood vessels and separate them from the other X-ray densities
such as bone and soft tissue. Furthermore, we have developed a novel data-augmentation mechanism to train this
data-hungry neural network to outperform DSA and alternative U-Net-based architectures during patient motion.
 In this grant application, we propose to implement our innovative algorithm on a product-oriented, low-latency,
edge hardware device for real-time application in minimally-invasive procedures. Second, we will validate the image
quality produced by of this edge hardware product. In the validation step, physicians in Neurology, Radiology, and
Neurosurgery will view the results of our Deep Learning Angiography technology side-by-side with DSA on real patient
data after the angiogram is complete. At the end of our funding period, we will deliver a validated, low-latency, edge
hardware implementation of our Deep Learning Angiography algorithm for real-time use during X-ray guided
interventions, which will be integrated into angiography machines in future work.

## Key facts

- **NIH application ID:** 10602275
- **Project number:** 1R41HL164298-01A1
- **Recipient organization:** CLEARVOYA LLC
- **Principal Investigator:** Sameer A Ansari
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $256,879
- **Award type:** 1
- **Project period:** 2023-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10602275

## Citation

> US National Institutes of Health, RePORTER application 10602275, Motion-Resistant Background Subtraction Angiography with Deep Learning:  Real-Time, Edge Hardware Implementation and Product Development (1R41HL164298-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10602275. Licensed CC0.

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