Authors:
Raffaele Mineo
1
;
Federica Proietto Salanitri
1
;
Giovanni Bellitto
1
;
Ovidio De Filippo
2
;
Fabrizio D’Ascenzo
2
;
Simone Palazzo
1
and
Concetto Spampinato
1
Affiliations:
1
PeRCeiVe Lab, University of Catania, Catania, Italy
;
2
Department of Medical Sciences, University of Turin, Turin, Italy
Keyword(s):
Attention Methods, Coronary Angiography, Medical Imaging Analysis.
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 p
resence 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.
(More)