论文标题
低剂量脑宠物图像重建和分析的资源有效的深度学习框架
A resource-efficient deep learning framework for low-dose brain PET image reconstruction and analysis
论文作者
论文摘要
18F氟脱氧葡萄糖（18F-FDG）正电子发射断层扫描（PET）成像通常需要全剂量的放射性示踪剂才能获得令人满意的诊断结果，这引起了人们对辐射暴露的潜在健康风险的担忧，尤其是针对儿科患者。将低剂量PET（L-PET）图像重建到高质量的全剂量PET（F-PET）的图像是一种有效的方式，可以降低辐射暴露并保持诊断精度。在本文中，我们提出了一个用于L-PET重建和分析的资源有效的深度学习框架，称为Transgan-SDAM，以从相应的L-Pet产生F-PET，并量化这些生成的F-PET的标准摄取值比（SUVR）。 Transgan-SDAM由两个模块组成：变压器编码的生成对抗网络（TransGAN）和空间可变形聚合模块（SDAM）。 TransGAN生成更高质量的F-PET图像，然后SDAM集成了一系列生成的F-PET切片的空间信息，以合成全脑F-PET图像。实验结果证明了我们方法的优势和合理性。
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.
