Generative Adversarial Network Applied to the Elimination of Noise and Artifacts in Optoacoustic Tomography Sinograms
The goal of this work is to study a preprocessing method for the data measured by a two-dimensional optoacoustic tomograph in order to reduce or eliminate artifacts introduced by the low number of detectors in the experimental setup and their limited bandwidth. A generative adversarial deep neural n...
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| Autores principales: | , , |
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| Formato: | Artículo publishedVersion |
| Lenguaje: | Español |
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FIUBA
2023
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| Materias: | |
| Acceso en línea: | https://elektron.fi.uba.ar/elektron/article/view/180 https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=elektron&d=180_oai |
| Aporte de: |
| Sumario: | The goal of this work is to study a preprocessing method for the data measured by a two-dimensional optoacoustic tomograph in order to reduce or eliminate artifacts introduced by the low number of detectors in the experimental setup and their limited bandwidth. A generative adversarial deep neural network was used to accomplish this task and its performance was compared with a reference U-Net neural network. In most of the test cases carried out, a slight improvement was found by applying the proposed network when measuring the Pearson correlation and the peak signal noise ratio between the reconstructed image product of the data processed by the model and the high-resolution reference image. |
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