Academic Journal of Computing & Information Science, 2021, 4(1); doi: 10.25236/AJCIS.2021.040108.
Mingdong Liu1,*, Yunye Feng2, Chang Liu1, Mingyu Gao1, Chenglin Yan1
1College of Medical Biological Information Engineering, Northeastern University, Shenyang 110167, China
2College of Software, Northeastern University, Shenyang 110167, China
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%.
PET, super resolution, deep learning, GAN
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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