Title

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

UMMS Affiliation

Department of Radiology

Publication Date

2022-05-08

Document Type

Article

Disciplines

Biological and Chemical Physics | Medical Biophysics | Radiology

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

DOI of Published Version

10.1002/mp.15707

Source

Sun J, Zhang Q, Du Y, Zhang D, Pretorius PH, King MA, Mok GSP. Dual gating myocardial perfusion SPECT denoising using a conditional generative adversarial network. Med Phys. 2022 May 8. doi: 10.1002/mp.15707. Epub ahead of print. PMID: 35526225. Link to article on publisher's site

Journal/Book/Conference Title

Medical physics

Related Resources

Link to Article in PubMed

PubMed ID

35526225

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