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Pix2Pix generative adversarial network for low dose myocardial perfusion SPECT denoising
Sun, Jingzhang1; Du, Yu1,2; Li, Chien Ying3,4; Wu, Tung Hsin3; Yang, Bang Hung3,4; Mok, Greta S.P.1,2
Source PublicationQuantitative Imaging in Medicine and Surgery

Background: Myocardial perfusion (MP) SPECT is a well-established method for diagnosing cardiac disease, yet its radiation risk poses safety concern. This study aims to apply and evaluate the use of Pix2Pix generative adversarial network (Pix2Pix GAN) in denoising low dose MP SPECT images. Methods: One hundred male and female patients with different Tc-sestamibi activity distributions, organ and body sizes were simulated by a population of digital 4D Extended Cardiac Torso (XCAT) phantoms. Realistic noisy SPECT projections of full dose of 987 MBq injection and 16 min acquisition, and low dose ranged from 1/20 to 1/2 of the full dose, were generated by an analytical projector from the right anterior oblique (RAO) to the left posterior oblique (LPO) positions. Additionally, twenty patients underwent ~1,184 MBq Tc-sestamibi stress SPECT/CT scan were also retrospectively recruited for the study. For each patient, low dose SPECT images (7/10 to 1/10 of full dose) were generated from the full dose list mode data. Our Pix2Pix GAN model was trained with full dose and low dose reconstructed SPECT image pairs. Normalized mean square error (NMSE), structural similarity index (SSIM), coefficient of variation (CV), full-width-at-half-maximum (FWHM) and relative defect size differences (RSD) of Pix2Pix GAN processed images were evaluated along with a reference convolutional auto encoder (CAE) network and post-reconstruction filters. Results: NMSE values of 0.0233±0.004 vs. 0.0249±0.004 and 0.0313±0.007 vs. 0.0579±0.016 were obtained on 1/2 and 1/20 dose level for Pix2Pix GAN and CAE in the simulation study, while they were 0.0376±0.010 vs. 0.0433±0.010 and 0.0907±0.020 vs. 0.1186±0.025 on 7/10 and 1/10 dose level in the clinical study. Similar results were also obtained from the SSIM, CV, FWHM and RSD values. Overall, the use of Pix2Pix GAN was superior to other denoising methods in all physical indices, particular in the lower dose levels in the simulation and clinical study. Conclusions: The Pix2Pix GAN method is effective to reduce the noise level of low dose MP SPECT. Further studies on clinical performance are warranted to demonstrate its full clinical effectiveness.

KeywordDenoising Generative Adversarial Network Low Dose Myocardial Perfusion (Mp) Spect Pix2pix
URLView the original
Indexed BySCIE
WOS Research AreaRadiology ; Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000797591200001
Scopus ID2-s2.0-85131307826
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Cited Times [WOS]:3   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Corresponding AuthorMok, Greta S.P.
Affiliation1.Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macao
2.Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macao
3.Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
4.Department of Nuclear Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationFaculty of Science and Technology;  INSTITUTE OF COLLABORATIVE INNOVATION
Recommended Citation
GB/T 7714
Sun, Jingzhang,Du, Yu,Li, Chien Ying,et al. Pix2Pix generative adversarial network for low dose myocardial perfusion SPECT denoising[J]. Quantitative Imaging in Medicine and Surgery,2022,12(7):3539-3555.
APA Sun, Jingzhang,Du, Yu,Li, Chien Ying,Wu, Tung Hsin,Yang, Bang Hung,&Mok, Greta S.P..(2022).Pix2Pix generative adversarial network for low dose myocardial perfusion SPECT denoising.Quantitative Imaging in Medicine and Surgery,12(7),3539-3555.
MLA Sun, Jingzhang,et al."Pix2Pix generative adversarial network for low dose myocardial perfusion SPECT denoising".Quantitative Imaging in Medicine and Surgery 12.7(2022):3539-3555.
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