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; Jonathan C. Irish

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, Jonathan C. Irish

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

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.

Original language	English
Article number	OW4D.4
Journal	Optics InfoBase Conference Papers
State	Published - 1 Jan 2022
Externally published	Yes
Event	Optical Tomography and Spectroscopy, OTS 2022 - Fort Lauderdale, United States Duration: 24 Apr 2022 → 27 Apr 2022

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Mechanics of Materials

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

@article{86b339703c4e48858a76aede0cdcd345,

title = "Fluorescence Tomography in the Spatial Frequency Domain: From Analytical Inversion to Deep Learning",

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.",

author = "Daly, \{Michael J.\} and Arjun Jagota and Scott Holthouser and Stefanie Markevich and Leonardo Franz and Sharon Tzelnick and Wilson, \{Brian C.\} and Irish, \{Jonathan C.\}",

note = "Publisher Copyright: {\textcopyright} 2022 The Authors.; Optical Tomography and Spectroscopy, OTS 2022 ; Conference date: 24-04-2022 Through 27-04-2022",

year = "2022",

month = jan,

day = "1",

language = "English",

journal = "Optics InfoBase Conference Papers",

issn = "2162-2701",

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TY - JOUR

T1 - Fluorescence Tomography in the Spatial Frequency Domain

T2 - Optical Tomography and Spectroscopy, OTS 2022

AU - Daly, Michael J.

AU - Jagota, Arjun

AU - Holthouser, Scott

AU - Markevich, Stefanie

AU - Franz, Leonardo

AU - Tzelnick, Sharon

AU - Wilson, Brian C.

AU - Irish, Jonathan C.

PY - 2022/1/1

Y1 - 2022/1/1

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85136595959

M3 - Conference article

AN - SCOPUS:85136595959

SN - 2162-2701

JO - Optics InfoBase Conference Papers

JF - Optics InfoBase Conference Papers

M1 - OW4D.4

Y2 - 24 April 2022 through 27 April 2022

ER -

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

Abstract

ASJC Scopus subject areas

UN SDGs

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