A Lightweight Low-dose PET Image Super-resolution Reconstruction Method based on Convolutional Neural Network

Abstract

Background: PET imaging is one of the most widely used neurological disease screening and diagnosis techniques.

Aims: Since PET involves the radiation and tolerance of different people, the improvement that has always been focused on is to cut down radiation, in the meantime, ensuring that the generated images with low-dose tracer and generated images with standard-dose tracer have the same details of images.

Methods: We propose a lightweight low-dose PET super-resolution network (SRPET-Net) based on a convolutional neural network. In this research, We propose a method for accurately recovering highresolution (HR) PET images from low-resolution (LR) PET images. The network learns the details and structure of the image between low-dose PET images and standard-dose PET images and, afterward, reconstructs the PET image by the trained network model.

Results: The experiments indicate that the SRPET-Net can achieve a superior peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) values. Moreover, our method has less memory consumption and lower computational cost.

Conclusion: In our follow-up work, the technology can be applied to medical imaging in many different directions.

Keywords: CBAM; Deep learning; PSNR; SSIM; convolutional neural network; low-dose PET; super-resolution.