Fluorescence Tomography in the Spatial Frequency Domain: From Analytical Inversion to Deep Learning

Michael J. Daly; Arjun Jagota; Scott Holthouser; Stefanie Markevich; Leonardo Franz; Sharon Tzelnick; Brian C. Wilson; Brian C. Wilson; Brian C. Wilson; Jonathan C. Irish; Jonathan C. Irish; Jonathan C. Irish

doi:10.1364/OTS.2022.OW4D.4

Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN)
Technical Digest Series (Optica Publishing Group, 2022),
paper OW4D.4
•https://doi.org/10.1364/OTS.2022.OW4D.4

Fluorescence Tomography in the Spatial Frequency Domain: From Analytical Inversion to Deep Learning

Michael J. Daly, Arjun Jagota, Scott Holthouser, Stefanie Markevich, Leonardo Franz, Sharon Tzelnick, Brian C. Wilson, and Jonathan C. Irish

Optical Tomography and Spectroscopy 2022

Fort Lauderdale, Florida United States
24–27 April 2022
ISBN: 978-1-957171-03-6

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Novel Processing Methods in Diffuse Optics (OW4D)

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M. J. Daly, A. Jagota, S. Holthouser, S. Markevich, L. Franz, S. Tzelnick, B. C. Wilson, and J. C. Irish, "Fluorescence Tomography in the Spatial Frequency Domain: From Analytical Inversion to Deep Learning," in Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN), Technical Digest Series (Optica Publishing Group, 2022), paper OW4D.4.

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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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