A resource-efficient deep learning framework for low-dose brain PET image reconstruction and analysis

TL;DR

This paper introduces a resource-efficient deep learning framework, transGAN-SDAM, that reconstructs high-quality full-dose brain PET images from low-dose scans, reducing radiation exposure while maintaining diagnostic accuracy.

Contribution

The paper presents a novel deep learning framework combining transGAN and SDAM modules for low-dose PET image reconstruction, improving image quality and analysis efficiency.

Findings

Generated F-PET images show superior quality compared to existing methods.

The framework accurately quantifies SUVRs in whole-brain analysis.

Experimental results validate the approach's effectiveness and rationality.

Abstract

18F-fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) imaging usually needs a full-dose radioactive tracer to obtain satisfactory diagnostic results, which raises concerns about the potential health risks of radiation exposure, especially for pediatric patients. Reconstructing the low-dose PET (L-PET) images to the high-quality full-dose PET (F-PET) ones is an effective way that both reduces the radiation exposure and remains diagnostic accuracy. In this paper, we propose a resource-efficient deep learning framework for L-PET reconstruction and analysis, referred to as transGAN-SDAM, to generate F-PET from corresponding L-PET, and quantify the standard uptake value ratios (SUVRs) of these generated F-PET at whole brain. The transGAN-SDAM consists of two modules: a transformer-encoded Generative Adversarial Network (transGAN) and a Spatial Deformable Aggregation Module (SDAM). The transGAN generates higher quality F-PET images, and then the SDAM integrates the spatial information of a sequence of generated F-PET slices to synthesize whole-brain F-PET images. Experimental results demonstrate the superiority and rationality of our approach.