Pix2Pix generative adversarial network for low dose myocardial perfusion SPECT denoising

Jingzhang Sun; Yu Du; ChienYing Li; Tung-Hsin Wu; BangHung Yang; Greta S P Mok

doi:10.21037/qims-21-1042

Pix2Pix generative adversarial network for low dose myocardial perfusion SPECT denoising

Quant Imaging Med Surg. 2022 Jul;12(7):3539-3555. doi: 10.21037/qims-21-1042.

Authors

Jingzhang Sun¹, Yu Du^{1

2}, ChienYing Li^{3

4}, Tung-Hsin Wu³, BangHung Yang^{3

4}, Greta S P Mok^{1

2}

Affiliations

¹ Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China.
² Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau, China.
³ Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei.
⁴ Department of Nuclear Medicine, Taipei Veterans General Hospital, Taipei.

Abstract

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 ^99mTc-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 ^99mTc-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.

Keywords: Generative adversarial network; Pix2Pix; denoising; low dose; myocardial perfusion (MP) SPECT.