Deep learning-based denoising in projection-domain and reconstruction-domain for low-dose myocardial perfusion SPECT.
J Nucl Cardiol
; 30(3): 970-985, 2023 06.
Article
em En
| MEDLINE
| ID: mdl-35982208
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.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article