A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising With Neural Network Approaches
Alexandre Bousse, Venkata Sai Sundar Kandarpa, Kuangyu Shi, Kuang Gong, Jae Sung Lee, Chi Liu, Dimitris Visvikis
Abstract
Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the patient dose. However, the inherent randomness in the photon counting process is a source of noise which is amplified in low-dose ET. This review article provides an overview of existing post-processing techniques, with an emphasis on deep neural network (NN) approaches. Furthermore, we explore future directions in the field of NN-based low-dose ET. This comprehensive examination sheds light on the potential of deep learning in enhancing the quality and resolution of low-dose ET images, ultimately advancing the field of medical imaging.