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“Virtual” attenuation correction: improving stress myocardial perfusion SPECT imaging using deep learning

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is widely used for coronary artery disease (CAD) evaluation. Although attenuation correction is recommended to diminish image artifacts and improve diagnostic accuracy, approximately 3/4ths of clinical MPI worldwide remains non-attenuation-corrected (NAC). In this work, we propose a novel deep learning (DL) algorithm to provide “virtual” DL attenuation–corrected (DLAC) perfusion polar maps solely from NAC data without concurrent computed tomography (CT) imaging or additional scans.

Methods

SPECT MPI studies (N = 11,532) with paired NAC and CTAC images were retrospectively identified. A convolutional neural network–based DL algorithm was developed and trained on half of the population to predict DLAC polar maps from NAC polar maps. Total perfusion deficit (TPD) was evaluated for all polar maps. TPDs from NAC and DLAC polar maps were compared to CTAC TPDs in linear regression analysis. Moreover, receiver-operating characteristic analysis was performed on NAC, CTAC, and DLAC TPDs to predict obstructive CAD as diagnosed from invasive coronary angiography.

Results

DLAC TPDs exhibited significantly improved linear correlation (p < 0.001) with CTAC (R2 = 0.85) compared to NAC vs. CTAC (R2 = 0.68). The diagnostic performance of TPD was also improved with DLAC compared to NAC with an area under the curve (AUC) of 0.827 vs. 0.780 (p = 0.012) with no statistically significant difference between AUC for CTAC and DLAC. At 88% sensitivity, specificity was improved by 18.9% for DLAC and 25.6% for CTAC.

Conclusions

The proposed DL algorithm provided attenuation correction comparable to CTAC without the need for additional scans. Compared to conventional NAC perfusion imaging, DLAC significantly improved diagnostic accuracy.

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Acknowledgements

The authors thank Ms. Keri M. Hiller, CNMT, RT(N) from the Department of Radiology at University of Michigan Health Systems for her assistance with imaging protocols.

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Author notes

  1. Edward P. Ficaro and Venkatesh L. Murthy share equal contribution as co-senior authors.

Authors and Affiliations

  1. INVIA Medical Imaging Solutions, 3025 Boardwalk St, Suite 200, Ann Arbor, MI, 48108, USA

    Tomoe Hagio, Alexis Poitrasson-Rivière, Jonathan B. Moody, Jennifer M. Renaud & Edward P. Ficaro

  2. Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA

    Liliana Arida-Moody, Edward P. Ficaro & Venkatesh L. Murthy

  3. Department of Cardiology, Massachusetts General Hospital, Boston, MA, USA

    Ravi V. Shah

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  1. Tomoe Hagio
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Corresponding author

Correspondence to Tomoe Hagio.

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Ethics approval

An informed consent waiver was granted by the University of Michigan Institutional Review Board for this retrospective analysis.

Competing interests

T. Hagio, A. Poitrasson-Rivière, J.B. Moody, and J.M. Renaud are employees of INVIA Medical Imaging Solutions. J.M. Renaud is a consultant for Jubilant DraxImage and receives royalties from sales and licensing of FlowQuant. E.P. Ficaro owns stock in INVIA Medical Imaging Solutions. V.L. Murthy is supported by R01AG059729 from the National Institute on Aging, U01DK123013 from the National Institute of Diabetes and Digestive and Kidney Disease, and R01HL136685 from the National Heart, Lung, and Blood Institute as well as the Melvyn Rubenfire Professorship in Preventive Cardiology. Dr. Murthy has received research grants and speaking honoraria from Siemens Medical Imaging. He serves as a scientific advisor for and owns stock options in Ionetix. Dr. Murthy owns stock in General Electric, Cardinal Health and nVidia. He has received expert witness payments on behalf of Jubilant DraxImage and a speaking honorarium from 2Quart Medical. Dr. Murthy receives non-financial research support from INVIA Medical Imaging Solutions.

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Hagio, T., Poitrasson-Rivière, A., Moody, J.B. et al. “Virtual” attenuation correction: improving stress myocardial perfusion SPECT imaging using deep learning. Eur J Nucl Med Mol Imaging 49, 3140–3149 (2022). https://doi.org/10.1007/s00259-022-05735-7

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