  The image noise level and resolution of SPECT images are relatively poor
attributed to the limited number of detected counts and various physical
degradation factors during acquisitions. This study aims to apply and evaluate
the use of generative adversarial network (GAN) method in static SPECT image
denoising. A population of 4D extended cardiac-torso (XCAT) phantoms were used
to simulate 10 male and female patients with different organ sizes and activity
uptakes. An analytical projector was applied to simulate 120 projections from
right anterior oblique to left posterior oblique positions with two noise
levels. The first noise level was based on a standard clinical count rate of
987 MBq injection and 16 min acquisition (low noise) while the other was 1/8 of
the previous count rate (high noise). The high noise and low noise SPECT
reconstructed images of 9 patients, i.e., 1026 images (9*114 axial slices)
respectively, were paired for training. High noise SPECT images of 1 patient
were tested using the trained GAN. The noise level is substantially reduced in
high noise SPECT reconstructed images after using the GAN. Our method has the
potential to decrease the noise level of SPECT images, which could be traded
for a reduced injection dose or acquisition time while still maintaining the
similar image quality as compared to the original low noise images for clinical
diagnosis.
