Multiple illumination learned spectral decoloring for quantitative optoacoustic oximetry imaging.

Thomas Kirchner,Martin Frenz

doi:10.1117/1.jbo.26.8.085001

Abstract

.Significance: Quantitative measurement of blood oxygen saturation () with optoacoustic (OA) imaging is one of the most sought after goals of quantitative OA imaging research due to its wide range of biomedical applications.Aim: A method for accurate and applicable real-time quantification of local with OA imaging.Approach: We combine multiple illumination (MI) sensing with learned spectral decoloring (LSD). We train LSD feedforward neural networks and random forests on Monte Carlo simulations of spectrally colored absorbed energy spectra, to apply the trained models to real OA measurements. We validate our combined MI-LSD method on a highly reliable, reproducible, and easily scalable phantom model, based on copper and nickel sulfate solutions.Results: With this sulfate model, we see a consistently high estimation accuracy using MI-LSD, with median absolute estimation errors of 2.5 to 4.5 percentage points. We further find fewer outliers in MI-LSD estimates compared with LSD. Random forest regressors outperform previously reported neural network approaches.Conclusions: Random forest-based MI-LSD is a promising method for accurate quantitative OA oximetry imaging.

Highlights

A robust and accurate quantitative measurement of blood oxygen saturation with optoacoustic (OA) imaging, called photoacoustic imaging, is one of the most sought after goals of quantitative OA imaging research due to its wide range of immediate applications
We further find fewer outliers in multiple illumination (MI)-learned spectral decoloring (LSD) estimates compared with LSD
Random forest-based MI-LSD is a promising method for accurate quantitative OA oximetry imaging

Summary

Introduction

A robust and accurate quantitative measurement of blood oxygen saturation (sO2) with optoacoustic (OA) imaging, called photoacoustic imaging, is one of the most sought after goals of quantitative OA imaging research due to its wide range of immediate applications. Quantitative OA imaging research aims to achieve an absolute quantification of optical properties, such as the absorption coefficient μa, from measured OA signals Sðd; tÞ recorded at times t at detector position d.1,2. Such a quantification of μa encompasses a solution of two illposed inverse problems. Quantitative OA imaging methods either depend on model-based inversion[2,3,4,5,6,7] or data-driven approaches.[8,9,10,11,12,13] These approaches perform well in silico but often struggle with the translation to real measurements in phantoms or in vivo

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