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Academic Journal of Computing & Information Science, 2021, 4(1); doi: 10.25236/AJCIS.2021.040108.

Super resolution reconstruction of PET images based on deep learning

Author(s)

Mingdong Liu1,*, Yunye Feng2, Chang Liu1, Mingyu Gao1, Chenglin Yan1

Corresponding Author:
​Mingdong Liu
Affiliation(s)

1College of Medical Biological Information Engineering, Northeastern University, Shenyang 110167, China

2College of Software, Northeastern University, Shenyang 110167, China

*Corresponding Author

Abstract

PET-CT is a high-end medical imaging technology that can detect tumors in the early stage, and has been widely used in clinical practice.Obtaining high-quality images is a prerequisite for doctors to make a correct diagnosis.For PET itself, how to continuously improve its resolution is a challenging problem.In this study, Generative Adversarial Networks (GAN) combined with Wasserstein distance and gradient penalty technology was used to achieve PET image super-resolution reconstruction.The experimental results show that the structural similarity of the final image reaches more than 60%.

Keywords

PET, super resolution, deep learning, GAN

Cite This Paper

Mingdong Liu, Yunye Feng, Chang Liu, Mingyu Gao, Chenglin Yan. Super resolution reconstruction of PET images based on deep learning. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 1: 48-52. https://doi.org/10.25236/AJCIS.2021.040108.

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