Skip to main content

Advertisement

SpringerLink
Log in

Diagnostic accuracy of stress-only myocardial perfusion SPECT improved by deep learning

  • Original Article
  • Published:
European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

Deep convolutional neural networks (CNN) for single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been used to improve the diagnostic accuracy of coronary artery disease (CAD). This study was to design and evaluate a deep learning (DL) approach to automatic diagnosis of myocardial perfusion abnormalities from stress-only MPI.

Methods

The new DL approach developed for this study was compared to a conventional quantitative perfusion defect size (DS) method. A total of 37,243 patients (51.5% males) undergone stress 99mTc-Tetrofosmin or 99mTc-Sestamibi MPI were selected retrospectively from Yale New Haven Hospital. Patients were dichotomized as studies with normal (75.4%) or abnormal (24.6%) myocardial perfusion based on final diagnoses of clinical nuclear cardiologists. Stress myocardial perfusion defect size was calculated using Yale quantitative analytic software. A deep CNN was trained using the circumferential count profile maps derived from SPECT MPI and was evaluated for the diagnosis of perfusion abnormality with a 5-fold cross-validation approach. In each fold, 27,933, 1862 and 7448 patients were used as training, validation and testing datasets, respectively. The area under the receiver-operating characteristic curve (AUC) was calculated and analyzed for all patients as well as for the eight sub-groups classified based on patient genders, quantitative algorithms, radioactive tracers and SPECT cameras.

Results

The AUC value resulted from the DL method was significantly higher than that from the DS method (0.872 ± 0.002 vs. 0.838 ± 0.003, p < 0.01). Across the eight sub-groups, the DL method provided more consistent AUC values in terms of smaller standard deviation and higher diagnostic accuracy and specificity, but slightly lower sensitivity than the DS method (AUC: 0.865 ± 0.010 vs. 0.838 ± 0.019, Accuracy: 82.7% ± 2.5% vs. 78.5% ± 3.6%, Specificity: 84.9% ± 3.7% vs. 77.5% ± 6.5%, Sensitivity: 74.4% ± 4.2% vs. 79.8% ± 5.8%).

Conclusions

The incorporation of deep learning for stress-only MPI has a considerable potential to improve the diagnostic accuracy and consistency in the detection of myocardial perfusion abnormalities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Hansen CL, Goldstein RA, Akinboboye OO, Berman DS, Botvinick EH, Churchwell KB, et al. Myocardial perfusion and function: single photon emission computed tomography. J Nucl Cardiol. 2007;14:e39–60.

    Article  Google Scholar 

  2. Yokota S, Mouden M, Ottervanger JP, Engbers E, Knollema S, Timmer JR, et al. Prognostic value of normal stress-only myocardial perfusion imaging: a comparison between conventional and CZT-based SPECT. Eur J Nucl Med Mol Imaging. 2016;43:296–301.

    Article  Google Scholar 

  3. Henzlova MJ, Duvall WL, Einstein AJ, Travin MI, Verberne HJ. ASNC imaging guidelines for SPECT nuclear cardiology procedures: stress, protocols, and tracers. J Nucl Cardiol. 2016;23:606–39.

    Article  Google Scholar 

  4. Spadafora M, Salvatore M, Cuocolo A. Stress protocol and accuracy of myocardial perfusion imaging: is it better to start from the end? Springer; 2016.

  5. Gowd BP, Heller GV, Parker MW. Stress-only SPECT myocardial perfusion imaging: a review. J Nucl Cardiol. 2014;21:1200–12.

    Article  Google Scholar 

  6. Henzlova MJ, Croft LB, Duvall WL. Stress-only imaging: Faster, cheaper, less radiation. So what’s the hold up? : Springer; 2013.

  7. Liu Y-H, Sinusas AJ, DeMan P, Zaret BL, Frans J. Quantification of SPECT myocardial perfusion images: methodology and validation of the Yale-CQ method. J Nucl Cardiol. 1999;6:190–203.

    Article  CAS  Google Scholar 

  8. Slomka PJ, Nishina H, Berman DS, Akincioglu C, Abidov A, Friedman JD, et al. Automated quantification of myocardial perfusion SPECT using simplified normal limits. J Nucl Cardiol. 2005;12:66–77.

    Article  Google Scholar 

  9. Liu Y-H. Quantification of nuclear cardiac images: the Yale approach. J Nucl Cardiol. 2007;14:483–91.

    Article  Google Scholar 

  10. Nakanishi R, Gransar H, Slomka P, Arsanjani R, Shalev A, Otaki Y, et al. Predictors of high-risk coronary artery disease in subjects with normal SPECT myocardial perfusion imaging. J Nucl Cardiol. 2016;23:530–41. https://doi.org/10.1007/s12350-015-0150-3.

    Article  Google Scholar 

  11. Slomka P, Hung G-U, Germano G, Berman DS. Novel SPECT technologies and approaches in cardiac imaging. Cardiovascular innovations and applications. 2016;2:31.

    Article  Google Scholar 

  12. Rubeaux M, Xu Y, Germano G, Berman DS, Slomka PJ. Normal databases for the relative quantification of myocardial perfusion. Current cardiovascular imaging reports. 2016;9:22.

    Article  Google Scholar 

  13. Dey D, Slomka PJ, Leeson P, Comaniciu D, Shrestha S, Sengupta PP, et al. Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. J Am Coll Cardiol. 2019;73:1317–35.

    Article  Google Scholar 

  14. Al’Aref SJ, Anchouche K, Singh G, Slomka PJ, Kolli KK, Kumar A, et al. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J. 2018;40:1975–86.

    Article  Google Scholar 

  15. Gomez J, Doukky R, Germano G, Slomka P. New trends in quantitative nuclear cardiology methods. Current Cardiovascular Imaging Reports. 2018;11:1.

    Article  CAS  Google Scholar 

  16. Nakajima K, Kudo T, Nakata T, Kiso K, Kasai T, Taniguchi Y, et al. Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: a Japanese multicenter study. Eur J Nucl Med Mol Imaging. 2017;44:2280–9.

    Article  Google Scholar 

  17. Betancur J, Otaki Y, Motwani M, Fish MB, Lemley M, Dey D, et al. Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning. JACC Cardiovasc Imaging. 2017;2406.

  18. Shrestha S, Sengupta PP. Machine learning for nuclear cardiology: the way forward: Springer; 2018.

  19. Alonso DH, Wernick MN, Yang Y, Germano G, Berman DS, Slomka P. Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning. J Nucl Cardiol. 2019;26:1746–54.

    Article  Google Scholar 

  20. Arsanjani R, Dey D, Khachatryan T, Shalev A, Hayes SW, Fish M, et al. Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population. J Nucl Cardiol. 2015;22:877–84.

    Article  Google Scholar 

  21. Chen X-W, Lin X. Big data deep learning: challenges and perspectives. IEEE access. 2014;2:514–25.

    Article  Google Scholar 

  22. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44. https://doi.org/10.1038/nature14539.

    Article  CAS  PubMed  Google Scholar 

  23. Henglin M, Stein G, Hushcha PV, Snoek J, Wiltschko AB, Cheng S. Machine learning approaches in cardiovascular imaging. Circulation: Cardiovascular Imaging. 2017;10:e005614.

    Google Scholar 

  24. Betancur J, Commandeur F, Motlagh M, Sharir T, Einstein AJ, Bokhari S, et al. Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: a multicenter study. JACC Cardiovasc Imaging. 2018;11:1654–63.

    Article  Google Scholar 

  25. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 770–8.

  26. Maas AL, Hannun AY, Ng AY. Rectifier nonlinearities improve neural network acoustic models. Proc icml; 2013. p. 3.

  27. Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, et al. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems; 2019. p. 8026–37.

  28. Loshchilov I, Hutter F. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:160803983. 2016.

  29. Sun X, Xu W. Fast implementation of DeLong’s algorithm for comparing the areas under correlated receiver operating characteristic curves. IEEE Signal Processing Letters. 2014;21:1389–93.

    Article  Google Scholar 

  30. Fluss R, Faraggi D, Reiser B. Estimation of the Youden index and its associated cutoff point. Biometrical Journal: Journal of Mathematical Methods in Biosciences. 2005;47:458–72.

    Article  Google Scholar 

Download references

Acknowledgments

This material was supported by the State of Connecticut under the Connecticut Bioscience Innovation Fund (16-00248, LIU). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the State of Connecticut or Connecticut Innovations, Inc. No potential conflicts of interest relevant to this work. The GPU card was supported by a grant from NVDIA.

Author information

Authors and Affiliations

  1. Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, 06520, USA

    Hui Liu, Edward J. Miller & Yi-Hwa Liu

  2. Department of Engineering Physics, Tsinghua University, Beijing, People’s Republic of China

    Hui Liu &  Liu

  3. Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, People’s Republic of China

    Hui Liu &  Liu

  4. Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA

    Hui Liu, Jing Wu, Edward J. Miller & Chi Liu

  5. Center for Advanced Quantum Studies and Department of Physics, Beijing Normal University, Beijing, CT, People’s Republic of China

    Jing Wu

  6. Department of Nuclear Cardiology, Heart and Vascular Center, Yale New Haven Hospital, New Haven, CT, USA

    Edward J. Miller & Yi-Hwa Liu

  7. Department of Biomedical Engineering, Chung Yuan Christian University, Taoyuan, Taiwan

    Yi-Hwa Liu

  8. Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan

    Yi-Hwa Liu

Authors
  1. Hui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Edward J. Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yaqiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yi-Hwa Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Hui Liu or Yi-Hwa Liu.

Ethics declarations

Conflict of interest

No potential conflicts of interest relevant to this work.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee (Yale Institutional Review Board protocol approval # 2000028863) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Cardiology.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, H., Wu, J., Miller, E.J. et al. Diagnostic accuracy of stress-only myocardial perfusion SPECT improved by deep learning. Eur J Nucl Med Mol Imaging 48, 2793–2800 (2021). https://doi.org/10.1007/s00259-021-05202-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00259-021-05202-9

Keywords

Search

Navigation