LAUSR

Positron emission tomography (PET) is an important functional imaging technology in early disease diagnosis. Generally, the gamma ray emitted by standard-dose tracer inevitably increases the exposure risk to patients. To reduce dosage, a lower dose tracer is often used and injected into patients. However, this often leads to low-quality PET images. In this article, we propose a learning-based method to reconstruct total-body standard-dose PET (SPET) images from low-dose PET (LPET) images and corresponding total-body computed tomography (CT) images. Different from previous works focusing only on a certain part of human body, our framework can hierarchically reconstruct total-body SPET images, considering varying shapes and intensity distributions of different body parts. Specifically, we first use one global total-body network to coarsely reconstruct total-body SPET images. Then, four local networks are designed to finely reconstruct head-neck, thorax, abdomen-pelvic, and leg parts of human body. Moreover, to enhance each local network learning for the respective local body part, we design an organ-aware network with a residual organ-aware dynamic convolution (RO-DC) module by dynamically adapting organ masks as additional inputs. Extensive experiments on 65 samples collected from uEXPLORER PET/CT system demonstrate that our hierarchical framework can consistently improve the performance of all body parts, especially for total-body PET images with PSNR of 30.6 dB, outperforming the state-of-the-art methods in SPET image reconstruction.