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

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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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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This study was partly supported by grants MOST107-2314-B-418-006-MY3 and 108-2314-B-418-002-MY3 from the Ministry of Science and Technology of Taiwan and FEMH107-2314-B-418-006-MY3 and 108-2314-B-418-002-MY3 from Far Eastern Memorial Hospital. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Author notes

  1. Chung-Ming Chen and Yen-Wen Wu contributed equally to his work.

Authors and Affiliations

  1. Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan

    Chi-Lun Ko, Shau-Syuan Lin, Cheng-Wen Huang, Yu-Hui Chang & Chung-Ming Chen

  2. Department of Nuclear Medicine, National Taiwan University Hospital, Taipei, Taiwan

    Chi-Lun Ko, Mei-Fang Cheng & Yen-Wen Wu

  3. College of Medicine, National Taiwan University, Taipei, Taiwan

    Chi-Lun Ko, Mei-Fang Cheng & Yen-Wen Wu

  4. Department of Nuclear Medicine, National Taiwan University Cancer Center, Taipei, Taiwan

    Kuan-Yin Ko

  5. Department of Nuclear Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan

    Shan-Ying Wang & Yen-Wen Wu

  6. Division of Cardiology, Cardiovascular Medical Center, Far Eastern Memorial Hospital, No. 21, Sec. 2, Nanya S. Rd., Banciao Dist., New Taipei City, 220, Taiwan

    Yen-Wen Wu

  7. School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan

    Yen-Wen Wu

  8. Graduate Institute of Medicine, Yuan Ze University, Taoyuan, Taiwan

    Yen-Wen Wu

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Contributions

All authors contributed to the study conception and design. Material preparation and data collection were performed by Chi-Lun Ko, Kuan-Yin Ko, and Mei-Fang Cheng. Algorithm design, model training, and data analysis were performed by Chung-Ming Chen, Yen-Wen Wu, Chi-Lun Ko, Shau-Syuan Lin, Cheng-Wen Huang, and Yu-Hui Chang. The first draft of the manuscript was written by Chi-Lun Ko and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yen-Wen Wu.

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An informed consent waiver was granted by the National Taiwan University Hospital Institutional Review Board for this retrospective analysis.

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Ko, CL., Lin, SS., Huang, CW. et al. Polar map-free 3D deep learning algorithm to predict obstructive coronary artery disease with myocardial perfusion CZT-SPECT. Eur J Nucl Med Mol Imaging 50, 376–386 (2023). https://doi.org/10.1007/s00259-022-05953-z

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