Coronary Artery Stenosis Assessment in X-Ray Angiography Through Spatio-Temporal Attention for Non-Invasive FFR and iFR Estimation

Raffaele Mineo, Federica Proietto Salanitri, Giovanni Bellitto, Ovidio De Filippo, Fabrizio D’Ascenzo, Simone Palazzo, Concetto Spampinato

2024

Abstract

Determining the degree of stenosis in coronary arteries through X-ray angiography imaging is a multifaceted task, given their appearance variability, the overlapping of vessels, and their small size. Traditional automated approaches utilize 2D deep models processing multiple angiography views as well as key frames. In this research, we propose a new deep learning model to non-invasively evaluate the fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) of moderate coronary stenosis from angiographic videos to better analyze spatial and temporal correlation without manual preprocessing. Our strategy harnesses 3D Convolutional Neural Networks (CNNs) to learn local spatio-temporal features and integrates self-attention layers to understand broad correlations within the feature set. At training time, both FFR and iFR values are employed for supervision, with missing targets suitably handled through multi-branch outputs. The resulting model can be employed to predict the presence of a clinically-significant coronary artery stenosis and to directly determine the FFR and iFR values. We also include an explainability strategy to show which parts of a video the model focuses on in the assessment of FFR and iFR values. Our proposed model demonstrates superior results than competitors on a dataset of 778 angiography exams from 389 patients. Importantly, our model doesn’t require key frames, thus reducing the efforts required by clinicians.

Paper Citation

in Harvard Style

Mineo R., Proietto Salanitri F., Bellitto G., De Filippo O., D’Ascenzo F., Palazzo S. and Spampinato C. (2024). Coronary Artery Stenosis Assessment in X-Ray Angiography Through Spatio-Temporal Attention for Non-Invasive FFR and iFR Estimation. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING; ISBN 978-989-758-688-0, SciTePress, pages 305-312. DOI: 10.5220/0012449200003657

in Bibtex Style

@conference{bioimaging24,
author={Raffaele Mineo and Federica Proietto Salanitri and Giovanni Bellitto and Ovidio De Filippo and Fabrizio D’Ascenzo and Simone Palazzo and Concetto Spampinato},
title={Coronary Artery Stenosis Assessment in X-Ray Angiography Through Spatio-Temporal Attention for Non-Invasive FFR and iFR Estimation},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING},
year={2024},
pages={305-312},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012449200003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING
TI - Coronary Artery Stenosis Assessment in X-Ray Angiography Through Spatio-Temporal Attention for Non-Invasive FFR and iFR Estimation
SN - 978-989-758-688-0
AU - Mineo R.
AU - Proietto Salanitri F.
AU - Bellitto G.
AU - De Filippo O.
AU - D’Ascenzo F.
AU - Palazzo S.
AU - Spampinato C.
PY - 2024
SP - 305
EP - 312
DO - 10.5220/0012449200003657
PB - SciTePress