Title: Context encoding enables machine learning-based quantitative photoacoustics,
Abstract: Real-time monitoring of functional tissue parameters, such as local blood
oxygenation, based on optical imaging could provide groundbreaking advances in
the diagnosis and interventional therapy of various diseases. While
photoacoustic (PA) imaging is a novel modality with great potential to measure
optical absorption deep inside tissue, quantification of the measurements
remains a major challenge. In this paper, we introduce the first machine
learning based approach to quantitative PA imaging (qPAI), which relies on
learning the fluence in a voxel to deduce the corresponding optical absorption.
The method encodes relevant information of the measured signal and the
characteristics of the imaging system in voxel-based feature vectors, which
allow the generation of thousands of training samples from a single simulated
PA image. Comprehensive in silico experiments suggest that context encoding
(CE)-qPAI enables highly accurate and robust quantification of the local
fluence and thereby the optical absorption from PA images.