Deep learning-enabled fluorescence tomography in the spatial frequency domain: pre-clinical oral cancer surgery studies

Michael J. Daly; Scott Holtshousen; Stefanie Markevich; Arjun Jagota; Mandolin L. Bartling; Brian C. Wilson; Jonathan C. Irish

doi:10.1117/12.2650541

17 March 2023 Deep learning-enabled fluorescence tomography in the spatial frequency domain: pre-clinical oral cancer surgery studies

Michael J. Daly, Scott Holtshousen, Stefanie Markevich, Arjun Jagota, Mandolin L. Bartling, Brian C. Wilson, Jonathan C. Irish

Author Affiliations +

Proceedings Volume PC12361, Molecular-Guided Surgery: Molecules, Devices, and Applications IX; PC123610A (2023) https://doi.org/10.1117/12.2650541
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

Fluorescence imaging during to oral cancer surgery is typically 2D, yielding limited information on tumor depth. Here, we continue the development of a spatial frequency domain imaging (SFDI) system for 3D fluorescence imaging. A deep convolutional neural network takes as inputs SFDI-computed absorption, scattering and spatial-frequency fluorescence images, and yields images of fluorescence concentration and tumour depth. The model is trained using in silico data from Monte Carlo simulations of geometric tumor shapes (e.g., cylinder, spherical harmonics). Initial results yield average depth errors of <0.1 mm. Experiments are conducted in agar phantoms based on patient imaging.

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Michael J. Daly, Scott Holtshousen, Stefanie Markevich, Arjun Jagota, Mandolin L. Bartling, Brian C. Wilson, and Jonathan C. Irish "Deep learning-enabled fluorescence tomography in the spatial frequency domain: pre-clinical oral cancer surgery studies", Proc. SPIE PC12361, Molecular-Guided Surgery: Molecules, Devices, and Applications IX, PC123610A (17 March 2023); https://doi.org/10.1117/12.2650541

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