Accuracy of deep learning-based attenuation correction in 99mTc-GSA SPECT/CT hepatic imaging

Introduction

Attenuation correction (AC) is necessary for accurate assessment of radioactive distribution in single photon emission computed tomography (SPECT). The method of computed tomography-based AC (CTAC) is widely used because of its accuracy. However, patients are exposed to radiation during CT examination. The purpose of this study was to generate pseudo CT images for AC from non-AC SPECT images using deep learning and evaluate the effect of deep learning-based AC in ^99mTc-labeled galactosyl human serum albumin SPECT/CT imaging.

Methods

A cycle-consistent generative network (CycleGAN) was used to generate pseudo CT images. The test cohort consisted of each one patient with normal and abnormal liver function. SPECT images were reconstructed without AC (SPECT_NC), with conventional CTAC (SPECT_CTAC), and with deep learning-based AC (SPECT_GAN). The accuracy of each AC was evaluated using the total liver count and the structural similarity index (SSIM) of SPECT_CTAC and SPECT_GAN. The coefficient of variation (%CV) was used to assess uniformity.

Results

The total liver counts in SPECT_GAN were significantly improved over those in SPECT_NC and differed from those of SPECT_CTAC by approximately 7 % in both patients. The %CV in SPECT_CTAC and SPECT_GAN were significantly lower than those in SPECT_NC. The mean SSIM in SPECT_CTAC and SPECT_GAN for patients with normal and abnormal liver functions were 0.985 and 0.977, respectively.

Conclusions

The accuracy of AC with a deep learning-based method was similarly performed as the conventional CTAC. Our proposed method used only non-AC SPECT images for AC, which has great potential to reduce patient exposure by eliminating CT examination.

Implications for practice

AC of ^99mTc-GSA was achieved using pseudo CT images generated with CycleGAN. Further studies on changing liver morphology and various hepatic diseases are recommended.