Deep learning-based attenuation correction method in 99m Tc-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 99m Tc-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 99m Tc-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.