# Context encoding enables machine learning-based quantitative photoacoustics

## TL;DR

This paper introduces a novel machine learning approach called context encoding (CE)-qPAI for quantitative photoacoustic imaging, enabling accurate measurement of tissue optical absorption by learning local fluence from PA signals.

## Contribution

It is the first to apply machine learning to quantify optical absorption in photoacoustic imaging using context encoding to improve accuracy and robustness.

## Key findings

- CE-qPAI achieves high accuracy in fluence and absorption quantification.
- The method is robust across various simulated tissue scenarios.
- Thousands of training samples can be generated from a single PA image.

## 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.