Deep learning-based attenuation correction method in ^99mTc-GSA SPECT/CT hepatic imaging: a phantom study

This study aimed to evaluate a deep learning-based attenuation correction (AC) method to generate pseudo-computed tomography (CT) images from non-AC single-photon emission computed tomography images (SPECT_NC) for AC in ^99mTc-galactosyl human albumin diethylenetriamine pentaacetic acid (GSA) scintigraphy and to reduce patient dosage. A cycle-consistent generative network (CycleGAN) model was used to generate pseudo-CT images. The training datasets comprised approximately 850 liver phantom images obtained from SPECT_NC and real CT images. The training datasets were then input to CycleGAN, and pseudo-CT images were output. SPECT images with real-time CT attenuation correction (SPECT_CTAC) and pseudo-CT attenuation correction (SPECT_GAN) were acquired. The difference in liver volume between real CT and pseudo-CT images was evaluated. Total counts and uniformity were then used to evaluate the effects of AC. Additionally, the similarity coefficients of SPECT_CTAC and SPECT_GAN were assessed using a structural similarity (SSIM) index. The pseudo-CT images produced a lower liver volume than the real CT images. SPECT_CTAC exhibited a higher total count than SPECT_NC and SPECT_GAN, which were approximately 60% and 7% lower, respectively. The uniformities of SPECT_CTAC and SPECT_GAN were better than those of SPECT_NC. The mean SSIM value for SPECT_CTAC and SPECT_GAN was 0.97. We proposed a deep learning-based AC approach to generate pseudo-CT images from SPECT_NC images in ^99mTc-GSA scintigraphy. SPECT_GAN with AC using pseudo-CT images was similar to SPECT_CTAC, demonstrating the possibility of SPECT/CT examination with reduced exposure to radiation.