Red adversaria generativa aplicada a la eliminación de ruido y artefactos en sinogramas de tomografía optoacústica

Delfina Montilla, Martín German González, Leonardo Rey Vega

Resumen


El objetivo de este trabajo es el estudio de un método de pre-procesamiento de los datos medidos por un tomógrafo optoacústico bidimensional para reducir o eliminar los artefactos introducidos por la escasa cantidad de detectores en el sistema experimental y el acotado ancho de banda de estos. Para esta tarea, se utilizó una red neuronal profunda generativa adversaria y se comparó su rendimiento con una red neuronal de referencia U-Net. En la mayoría de los casos de testeo realizados, se encontró una leve mejora aplicando la red propuesta al medir la correlación de Pearson y la relación señal a ruido piso entre la imagen reconstruida producto de los datos procesados por el modelo y la imagen de alta resolución de referencia.

Palabras clave


tomografía optoacústica; aprendizaje profundo; GAN

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Referencias


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DOI: https://doi.org/10.37537/rev.elektron.7.1.180.2023

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