Highlights
- •A method for detection of perfusion defect in dynamic CT is presented.
- •Deep learning automates generation of myocardial blood flow maps.
- •Color-coded maps are used as input to model training and validation.
- •Cross-validation demonstrates high AUCs in identifying lesion location.
Abstract
Purpose
The purpose of this study was to develop and evaluate deep convolutional neural network
(CNN) models for quantifying myocardial blood flow (MBF) as well as for identifying
myocardial perfusion defects in dynamic cardiac computed tomography (CT) images.
Methods
Adenosine stress cardiac CT perfusion data acquired from 156 patients having or being
suspected with coronary artery disease were considered for model development and validation.
U-net-based deep CNN models were developed to segment the aorta and myocardium and
to localize anatomical landmarks. Color-coded MBF maps were obtained in short-axis
slices from the apex to the base level and were used to train a deep CNN classifier.
Three binary classification models were built for the detection of perfusion defect
in the left anterior descending artery (LAD), the right coronary artery (RCA), and
the left circumflex artery (LCX) territories.
Results
Mean Dice scores were 0.94 (±0.07) and 0.86 (±0.06) for the aorta and myocardial deep
learning-based segmentations, respectively. With the localization U-net, mean distance
errors were 3.5 (±3.5) mm and 3.8 (±2.4) mm for the basal and apical center points,
respectively. The classification models identified perfusion defects with the accuracy
of mean area under the receiver operating curve (AUROC) values of 0.959 (±0.023) for
LAD, 0.949 (±0.016) for RCA, and 0.957 (±0.021) for LCX.
Conclusion
The presented method has the potential to fully automate the quantification of MBF
and subsequently identify the main coronary artery territories with myocardial perfusion
defects in dynamic cardiac CT perfusion.
Keywords
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Article info
Publication history
Published online: March 04, 2023
Accepted:
February 18,
2023
Received in revised form:
February 14,
2023
Received:
June 20,
2022
Identification
Copyright
© 2023 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.
