Two-Phase Multi-Dose-Level PET Image Reconstruction with Dose Level Awareness

Autor:	Fei, Yuchen, Luo, Yanmei, Wang, Yan, Cui, Jiaqi, Xu, Yuanyuan, Zhou, Jiliu, Shen, Dinggang
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	To obtain high-quality positron emission tomography (PET) while minimizing radiation exposure, a range of methods have been designed to reconstruct standard-dose PET (SPET) from corresponding low-dose PET (LPET) images. However, most current methods merely learn the mapping between single-dose-level LPET and SPET images, but omit the dose disparity of LPET images in clinical scenarios. In this paper, to reconstruct high-quality SPET images from multi-dose-level LPET images, we design a novel two-phase multi-dose-level PET reconstruction algorithm with dose level awareness, containing a pre-training phase and a SPET prediction phase. Specifically, the pre-training phase is devised to explore both fine-grained discriminative features and effective semantic representation. The SPET prediction phase adopts a coarse prediction network utilizing pre-learned dose level prior to generate preliminary result, and a refinement network to precisely preserve the details. Experiments on MICCAI 2022 Ultra-low Dose PET Imaging Challenge Dataset have demonstrated the superiority of our method. Comment: Accepted by ISBI2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.01563 Zobrazit plný text záznamu View this record from Arxiv