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Updated: Jul 10, 2026

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Published on: October 24, 2019
A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches
Alexandre Bousse1, Venkata Sai Sundar Kandarpa1, Kuangyu Shi2
1Univ. Brest, LATIM, INSERM UMR 1101, 29238 Brest, France.
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 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.
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