Context encoding enables machine learning-based quantitative photoacoustics

• Verfasst von: Kirchner, Thomas [VerfasserIn]
• Verfasst von: Gröhl, Janek [VerfasserIn]
• Verfasst von: Maier-Hein, Lena [VerfasserIn]
• Titel: Context encoding enables machine learning-based quantitative photoacoustics
• Verf.angabe: Thomas Kirchner, Janek Gröhl, and Lena Maier-Hein
• E-Jahr: 2018
• Jahr: May 18, 2018
• Teil: volume:23
• Teil: year:2018
• Teil: number:5
• Fussnoten: Gesehen am 22.10.2019
• Titel Quelle: Enthalten in: Journal of biomedical optics
• Ort Quelle: Bellingham, Wash. : SPIE, 1996
• Jahr Quelle: 2018
• Band/Heft Quelle: 23(2018,5) Artikel-Nummer 056008, 9 Seiten
• ISSN Quelle: 1560-2281
• DOI: doi:10.1117/1.JBO.23.5.056008
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 167929072X

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. 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.