Investigation of Noise Reduction in Low-Dose Spect Myocardial Perfusion Images With a Generative Adversarial Network

Investigation of Noise Reduction in Low-Dose Spect Myocardial Perfusion Images With a Generative Adversarial Network Publisher

Olia NA¹ ; Kamaliasl A¹ ; Tabrizi SH¹ ; Geramifar P² ; Sheikhzadeh P³ ; Arabi H⁴ ; Zaidi H⁴

Show Affiliations

1. Shahid Beheshti University, Department of Medical Radiation Engineering, Tehran, Iran
2. Shariati Hospital, Tehran University of Medical Sciences, Research Center for Nuclear Medicine, Tehran, Iran
3. Tehran University of Medical Sciences, Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran, Iran
4. Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, Geneva, Switzerland

Source: 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record# NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors# RTSD 2022 Published:2021

Abstract

The purpose of this work was to investigate the possibility of administrated dose reduction while preserving crucial information and clinical value in SPECT-MPI images. In this study, we collected list-mode data from 330 consecutive patients with a dedicated cardiac SPECT scanner using a two-day Tc-99m sestamibi rest/stress protocol at the clinical standard-dose level. A supervised deep learning approach was adopted to predict standard-dose images from 50%, 25%, and 12.5% of the standard-dose level in the projection space. In order to evaluate the proposed deep learning-based framework quantitatively, the peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), and structural similarity index (SSIM) were measured at different dose reduction levels. In addition to the PSNR, SSIM, and RMSE quantitative metrics, we performed a Pearson correlation coefficient analysis on the derived parameters from the QPS package by Cedars-Sinai software. According to the quantitative analysis, reconstructed images at the half-dose level produced the highest PSNR (42.49 ± 2.37) and SSIM (0.99 ± 0.01), as well as the lowest RMSE (1.99 ± 0.63). Analysis of the predicted standard-dose images at the half-dose and quarter-dose revealed that the implemented deep learning model could improve image quality effectively. Although the underlying information in low-dose images beyond the quarter of the standard dose level is not recoverable due to the extremely high noise level. © 2021 IEEE.

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Style	Citing Format
MLA	Olia NA, et al.. "Investigation of Noise Reduction in Low-Dose Spect Myocardial Perfusion Images With a Generative Adversarial Network." 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record# NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors# RTSD 2022, vol. , no. , 2021, pp. -.
APA	Olia NA, Kamaliasl A, Tabrizi SH, Geramifar P, Sheikhzadeh P, Arabi H, Zaidi H (2021). Investigation of Noise Reduction in Low-Dose Spect Myocardial Perfusion Images With a Generative Adversarial Network. 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record# NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors# RTSD 2022, (), -.
Chicago	Olia NA, Kamaliasl A, Tabrizi SH, Geramifar P, Sheikhzadeh P, Arabi H, Zaidi H. "Investigation of Noise Reduction in Low-Dose Spect Myocardial Perfusion Images With a Generative Adversarial Network." 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record# NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors# RTSD 2022 , no. (2021): -.
Harvard	Olia NA et al. (2021) 'Investigation of Noise Reduction in Low-Dose Spect Myocardial Perfusion Images With a Generative Adversarial Network', 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record# NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors# RTSD 2022, (), pp. -.
Vancouver	Olia NA, Kamaliasl A, Tabrizi SH, Geramifar P, Sheikhzadeh P, Arabi H, et al.. Investigation of Noise Reduction in Low-Dose Spect Myocardial Perfusion Images With a Generative Adversarial Network. 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record# NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors# RTSD 2022. 2021;():-.
BibTex	@article{ author = {Olia NA and Kamaliasl A and Tabrizi SH and Geramifar P and Sheikhzadeh P and Arabi H and Zaidi H}, title = {Investigation of Noise Reduction in Low-Dose Spect Myocardial Perfusion Images With a Generative Adversarial Network}, journal = {2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record# NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors# RTSD 2022}, volume = {}, number = {}, pages = {-}, year = {2021} }
RIS	TY - JOUR AU - Olia NA AU - Kamaliasl A AU - Tabrizi SH AU - Geramifar P AU - Sheikhzadeh P AU - Arabi H AU - Zaidi H TI - Investigation of Noise Reduction in Low-Dose Spect Myocardial Perfusion Images With a Generative Adversarial Network JO - 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record# NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors# RTSD 2022 VL - IS - SP - EP - PY - 2021 ER -

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