Does prior knowledge in the form of multiple low-dose PET images (at different dose levels) improve standard-dose PET prediction?

2022/02/22 by Behnoush Sanaei, Sanaei, Behnoush, Reza Faghihi +3
Engineering · Medicine · #Advanced X-ray and CT Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Physical sciences #Medical Imaging Techniques and Applications #Medical Physics (physics.med-ph) #Radiomics and Machine Learning in Medical Imaging

paper · pdf · doi:10.48550/arxiv.2202.10998

openalex publication_date 2022/02/22 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28

Abstract

Reducing the injected dose would result in quality degradation and loss of information in PET imaging. To address this issue, deep learning methods have been introduced to predict standard PET images (S-PET) from the corresponding low-dose versions (L-PET). The existing deep learning-based denoising methods solely rely on a single dose level of PET images to predict the S-PET images. In this work, we proposed to exploit the prior knowledge in the form of multiple low-dose levels of PET images (in addition to the target low-dose level) to estimate the S-PET images.

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