German Cancer Research Center (DKFZ), Division of Computer Assisted Medical Interventions (CAMI), He, Germany.
Heidelberg University, Faculty of Physics and Astronomy, Heidelberg, Germany.
J Biomed Opt. 2018 May;23(5):1-9. doi: 10.1117/1.JBO.23.5.056008.
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. Although photoacoustic (PA) imaging is a modality with great potential to measure optical absorption deep inside tissue, quantification of the measurements remains a major challenge. 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-qPAI enables highly accurate and robust quantification of the local fluence and thereby the optical absorption from PA images.
基于光学成象的功能组织参数（如局部血氧饱和度）的实时监测，可为各种疾病的诊断和介入治疗提供开创性的进展。虽然光声（PA）成象是一种具有很大潜力的测量组织内深部光吸收的方法，但对测量结果的定量仍然是一个主要的挑战。我们介绍了第一个基于机器学习的定量光声成象（qPAI）方法，它依赖于学习体素中的光密度来推断相应的光吸收。该方法将测量信号的相关信息和成像系统的特征编码在基于体素的特征向量中，允许从单个模拟 PA 图像生成数千个训练样本。全面的计算机实验表明，语境编码 qPAI 能够高度准确和稳健地从 PA 图像定量本地光密度，并由此定量光吸收。
