Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Aug;49(8):5093-5106.
doi: 10.1002/mp.15707. Epub 2022 May 17.

Dual gating myocardial perfusion SPECT denoising using a conditional generative adversarial network

Jingzhang Sun  1 , Qi Zhang  1   2 , Yu Du  1 , Duo Zhang  1   3 , P Hendrik Pretorius  4 , Michael A King  4 , Greta S P Mok  1   5
Affiliations

Dual gating myocardial perfusion SPECT denoising using a conditional generative adversarial network

Jingzhang Sun et al. Med Phys. 2022 Aug.

Abstract

Purpose: Dual respiratory-cardiac gating reduces respiratory and cardiac motion blur in myocardial perfusion single-photon emission computed tomography (MP-SPECT). However, image noise is increased as detected counts are reduced in each dual gate (DG). We aim to develop a denoising method for dual gating MP-SPECT images using a 3D conditional generative adversarial network (cGAN).

Methods: Twenty extended cardiac-torso phantoms with various 99m Tc-sestamibi distributions, defect characteristics, and body and organ sizes were used in the simulation, modeling six respiratory and eight cardiac gates (CGs), that is, 48 DGs for ordered subset expectation maximization reconstruction. Twenty clinical 99m Tc-sestamibi SPECT/CT datasets were re-binned into 7 respiratory gates and 8 CGs, that is, 56 DGs for maximum likelihood expectation maximization reconstruction. We evaluated the use of (i) phantoms' own datasets (patient-specific denoising [PD]) or other phantoms' datasets (cross-patient denoising) for training; (ii) the CG or the static (non-gated [NG]) data as the training references for cGAN; and (iii) cGAN as compared to conventional 3D post-reconstruction filtering, cardiac gating methods, and convolutional neural network. Normalized mean squared error, noise as assessed by normalized standard deviation, spatial blurring measured as the full-width-at-half-maximum of left ventricular wall, ejection fraction, joint correlation histogram, and defect size were analyzed as metrics of image quality.

Results: Training using patients' own dataset is superior to conventional training based on other patients' data. Using CG image as training reference provides a better trade-off in terms of noise and image blur as compared to the use of NG. cGAN-CG-PD provides superior performance as compared to other denoising methods for all physical and diagnostic indices evaluated in both simulation and clinical studies.

Conclusions: cGAN denoising is promising for dual gating MP-SPECT based on the metrics mentioned earlier.

Keywords: conditional generative adversarial network; denoising; dual gating; myocardial perfusion SPECT.

PubMed Disclaimer

References

REFERENCES

    1. Underwood S, Anagnostopoulos C, Cerqueira M, et al. Myocardial perfusion scintigraphy: the evidence. Eur J Nucl Med Mol Imag. 2004; 31(2): 261-291.
    1. Zhang Q, Zhang D, Mok GS. Comparison of different attenuation correction methods for dual gating myocardial perfusion SPECT/CT. IEEE Trans Radiat Plasma Med Sci. 2019; 3(5): 565-571.
    1. Okuda K, Nakajima K. Has the era of dual-gated myocardial perfusion SPECT and PET arrived?. J Nucl Cardiol. 2018; 27(2): 648-650.
    1. Kortelainen MJ, Koivumäki TM, Vauhkonen MJ, et al. Respiratory motion reduction with a dual gating approach in myocardial perfusion SPECT: effect on left ventricular functional parameters. J Nucl Cardiol. 2018; 25(5): 1633-1641.
    1. Lyra M, Ploussi A. Filtering in SPECT image reconstruction. Int J Biomed Imaging. 2011; 2011: 2-43.

MeSH terms

Substances

LinkOut - more resources