We investigate fluorescence tomography approaches to reconstruct depth and concentration from a spatial-frequency domain imaging system for oral cancer surgery. Results compare analytical inversion and deep learning methods in phantom models.
Fluorescence Tomography in the Spatial Frequency Domain: From Analytical Inversion to Deep Learning
Franz L.;
2022
Abstract
We investigate fluorescence tomography approaches to reconstruct depth and concentration from a spatial-frequency domain imaging system for oral cancer surgery. Results compare analytical inversion and deep learning methods in phantom models.
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