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European Journal of Nuclear Medicine and Molecular Imaging

Erschienen in:

14.09.2022 | Original Article

Polar map-free 3D deep learning algorithm to predict obstructive coronary artery disease with myocardial perfusion CZT-SPECT

verfasst von: Chi-Lun Ko, Shau-Syuan Lin, Cheng-Wen Huang, Yu-Hui Chang, Kuan-Yin Ko, Mei-Fang Cheng, Shan-Ying Wang, Chung-Ming Chen, Yen-Wen Wu

Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging | Ausgabe 2/2023

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Abstract

Purpose

Deep learning (DL) models have been shown to outperform total perfusion deficit (TPD) quantification in predicting obstructive coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, previously published methods have depended on polar maps, required manual correction, and normal database. In this study, we propose a polar map-free 3D DL algorithm to predict obstructive disease.

Methods

We included 1861 subjects who underwent MPI using cadmium-zinc-telluride camera and subsequent coronary angiography. The subjects were divided into parameterization and external validation groups. We implemented a fully automatic algorithm to segment myocardium, perform registration, and apply normalization. We further flattened the image based on spherical coordinate system transformation. The proposed model consisted of a component to predict patent arteries and a component to predict disease in each vessel. The model was cross-validated in the parameterization group, and then further tested using the external validation group. The performance was assessed by area under receiver operating characteristic curves (AUCs) and compared with TPD.

Results

Our algorithm preprocessed all images accurately as confirmed by visual inspection. In patient-based analysis, the AUC of the proposed model was significantly higher than that for stress-TPD (0.84 vs 0.76, p < 0.01). In vessel-based analysis, the proposed model also outperformed regional stress-TPD (AUC = 0.80 vs 0.72, p < 0.01). The addition of quantitative images did not improve the performance.

Conclusions

Our proposed polar map-free 3D DL algorithm to predict obstructive CAD from MPI outperformed TPD and did not require manual correction or a normal database.

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Titel: Polar map-free 3D deep learning algorithm to predict obstructive coronary artery disease with myocardial perfusion CZT-SPECT
verfasst von: Chi-Lun Ko
Shau-Syuan Lin
Cheng-Wen Huang
Yu-Hui Chang
Kuan-Yin Ko
Mei-Fang Cheng
Shan-Ying Wang
Chung-Ming Chen
Yen-Wen Wu
Publikationsdatum: 14.09.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging / Ausgabe 2/2023
Print ISSN: 1619-7070
Elektronische ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-022-05953-z

Springer Medizin

Abstract

Purpose

Methods

Results

Conclusions

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