/PLA-GAN

Primary LanguagePython

LOW-DOSE CT RECONSTRCTION VIA OPTIMIZATION-INSPIRED GAN (ICASSP 2023)

Repository with code to reproduce the results for PLA-GAN. The comparisons with state-of-the-arts on the Mayo dataset validate the superiority of our proposed algorithm both numerically and visually. The advantages of generalizability and interpretability are also evident. If you encounter any doubts, please fell free to concact us: jjw@zjut.edu.cn or zjw@zjut.edu.cn

Visual results

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Quantitative results