Delayed PET imaging using image synthesis network and nonrigid registration without additional CT scan

Attenuation correction is critical for positron emission tomography (PET) image reconstruction. The standard protocol for obtaining attenuation information in a clinical PET scanner is via coregistered computed tomography (CT) images. Therefore, for delayed PET imaging, the CT scan is repeated twice, which increases the radiation dose for the patient. In this paper, we propose a zero-extradose delayed PET imaging method that requires no additional CT scans.

A deep learning-based synthesis network is designed to convert PET data into pseudo-CT images for delayed scans. Then, nonrigid registration is performed between this pseudo CT image and the CT image of the first scan, warping the CT image of the first scan to an estimated CT image for the delayed scan. Finally, the PET image attenuation correction in the delayed scan is obtained from this estimated CT image. Experiments with clinical datasets are implemented to assess the effectiveness of the proposed method with the well-recognized Generative Adversarial Networks (GAN) method. The average peak signal-to-noise ratio (PSNR) and the mean absolute percent error (MAPE) are used for comparison. We also use scoring from three experienced radiologists as subjective measurement means based on the diagnostic consistency of the PET images reconstructed from GAN and the proposed method with respect to the ground truth images.

The experiments show that the average PSNR is 47.04 dB (the proposed method) vs. 44.41 dB (the traditional GAN method) for the reconstructed delayed PET images in our evaluation dataset. The average MAPEs are 1.59% for the proposed method and 3.32% for the traditional GAN method across five organ regions of interest (ROIs). The scores for the GAN and the proposed method rated by three experienced radiologists are 8.08±0.60 and 9.02±0.52, indicating that the proposed method yields more consistent PET images with the ground truth.

This work proposes a novel method for CT-less delayed PET imaging based on image synthesis network and nonrigid image registration. The PET image reconstructed using the proposed method yields delayed PET images with high image quality without artifacts and is quantitatively more accurate than the traditional GAN method.