Machine learning approach for rapid and accurate estimation of optical properties using spatial frequency domain imaging.

Abstract:

:Fast estimation of optical properties from reflectance measurements at two spatial frequencies could pave way for real-time, wide-field and quantitative mapping of vital signs of tissues. We present a machine learning-based approach for estimating optical properties in the spatial frequency domain, where a random forest regression algorithm is trained over data obtained from Monte-Carlo photon transport simulations. The algorithm learns the nonlinear mapping between diffuse reflectance at two spatial frequencies, and the absorption and reduced scattering coefficient of the tissue under consideration. Using this method, absorption and reduced scattering properties could be obtained over a 1 megapixel image in 450 ms with errors as low as 0.556% in absorption and 0.126% in reduced scattering.

journal_name

J Biomed Opt

authors

Panigrahi S,Gioux S

doi

10.1117/1.JBO.24.7.071606

subject

Has Abstract

pub_date

2018-12-01 00:00:00

pages

1-6

issue

7

eissn

1083-3668

issn

1560-2281

pii

JBO-180505SSLR

journal_volume

24

pub_type

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