Deep Learning-Based Low-Dose Cardiac Gated Spect: Implementation in Projection Space Vs. Image Space

Deep Learning-Based Low-Dose Cardiac Gated Spect: Implementation in Projection Space Vs. Image Space 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. Tehran University of Medical Sciences, Research Center for Nuclear Medicine, Shariati Hospital, Tehran, Iran
3. Tehran University of Medical Sciences, Imam Khomeini Hospital Complex, Department of Nuclear Medicine, 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 reduction of radiation exposure in SPECT-MPI is an important research topic. However, lowering the injected activity degrades image quality, thus impacting the diagnostic accuracy of this modality. In this study, we enrolled a total of 335 clinical gated SPECT-MPI images from a dedicated cardiac SPECT scanner acquired in list-mode format. All patients underwent a two-day rest/stress protocol and the obtained gated images were retrospectively used to convert low-dose to standard-dose images in both projection and image spaces. A deep generative adversarial network was employed to predict standard-dose images from 50% low-dose images. The proposed network was evaluated using quantitative metrics, such as the peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), and structural similarity index measure (SSIM). Moreover, a Pearson correlation coefficient analysis was performed on the half-dose and predicted standard-dose images with respect to the reference standard-dose images. The results demonstrated that the highest PSNR (46.30 ± 2.23) and SSIM (0.98 ± 0.01), and the lowest RMSE (1.32 ± 0.54) were obtained from the image space implementation. Pearson analysis showed that the predicted standard-dose images yielded ρ = 0.960 ± 0.011 and ρ = 0.947 ± 0.027 in the image and projection spaces, respectively. Overall, considering the quantitative metrics, the noise was effectively suppressed in the predicted standard-dose images for both implementations. Yet, standard-dose image estimation in the image space resulted in superior quantitative accuracy and image quality. © 2021 IEEE.

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Mohammad Reza Ay (5)

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Style	Citing Format
MLA	Olia NA, et al.. "Deep Learning-Based Low-Dose Cardiac Gated Spect: Implementation in Projection Space Vs. Image Space." 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). Deep Learning-Based Low-Dose Cardiac Gated Spect: Implementation in Projection Space Vs. Image Space. 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. "Deep Learning-Based Low-Dose Cardiac Gated Spect: Implementation in Projection Space Vs. Image Space." 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) 'Deep Learning-Based Low-Dose Cardiac Gated Spect: Implementation in Projection Space Vs. Image Space', 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.. Deep Learning-Based Low-Dose Cardiac Gated Spect: Implementation in Projection Space Vs. Image Space. 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 = {Deep Learning-Based Low-Dose Cardiac Gated Spect: Implementation in Projection Space Vs. Image Space}, 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 - Deep Learning-Based Low-Dose Cardiac Gated Spect: Implementation in Projection Space Vs. Image Space 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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