Deep learning-based denoising in projection-domain and reconstruction-domain for low-dose myocardial perfusion SPECT

Jingzhang Sun, Han Jiang, Yu Du, Chien Ying Li, Tung Hsin Wu, Yi Hwa Liu, Bang Hung Yang*, Greta S.P. Mok*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Background: Low-dose (LD) myocardial perfusion (MP) SPECT suffers from high noise level, leading to compromised diagnostic accuracy. Here we investigated the denoising performance for MP-SPECT using a conditional generative adversarial network (cGAN) in projection-domain (cGAN-prj) and reconstruction-domain (cGAN-recon). Methods: Sixty-four noisy SPECT projections were simulated for a population of 100 XCAT phantoms with different anatomical variations and 99mTc-sestamibi distributions. Series of LD projections were obtained by scaling the full dose (FD) count rate to be 1/20 to 1/2 of the original. Twenty patients with 99mTc-sestamibi stress SPECT/CT scans were retrospectively analyzed. For each patient, LD SPECT images (7/10 to 1/10 of FD) were generated from the FD list mode data. All projections were reconstructed by the quantitative OS-EM method. A 3D cGAN was implemented to predict FD images from their corresponding LD images in the projection- and reconstruction-domain. The denoised projections were reconstructed for analysis in various quantitative indices along with cGAN-recon, Gaussian, and Butterworth-filtered images. Results: cGAN denoising improves image quality as compared to LD and conventional post-reconstruction filtering. cGAN-prj can further reduce the dose level as compared to cGAN-recon without compromising the image quality. Conclusions: Denoising based on cGAN-prj is superior to cGAN-recon for MP-SPECT.

Original languageEnglish
Pages (from-to)970-985
Number of pages16
JournalJournal of Nuclear Cardiology
Volume30
Issue number3
DOIs
StatePublished - Jun 2023

Keywords

  • deep learning
  • low dose
  • myocardial perfusion SPECT
  • projection
  • reconstruction

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