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1. (WO2018001702) MACHINE LEARNING-BASED QUANTITATIVE PHOTOACOUSTIC TOMOGRAPHY (PAT)

Pub. No.:    WO/2018/001702    International Application No.:    PCT/EP2017/064174
Publication Date: Fri Jan 05 00:59:59 CET 2018 International Filing Date: Sat Jun 10 01:59:59 CEST 2017
IPC: G06K 9/00
Applicants: DEUTSCHES KREBSFORSCHUNGSZENTRUM STIFTUNG DES ÖFFENTLICHEN RECHTS
Inventors: MAIER-HEIN, Lena
KIRCHNER, Thomas
GRÖHL, Janek
Title: MACHINE LEARNING-BASED QUANTITATIVE PHOTOACOUSTIC TOMOGRAPHY (PAT)
Abstract:
A method for estimating an optical property of a tissue (10) from a photoacoustic image (20) of the tissue (10) or parts thereof (12) using a machine learning algorithm, wherein the photoacoustic image (20) is obtained with a photoacoustic setup (30) and wherein the machine learning algorithm is configured to infer the optical property at least at one domain of the photoacoustic image (20) by means of a descriptor for said at least one domain, wherein at least part of the photoacoustic image (20) is partitioned in a plurality of domains (22) with respect to at least one parameter, wherein the at least one parameter corresponds to a physical property of the tissue (10), and wherein the variation of the at least one parameter within each domain is limited to a pre-determined value, such that each domain corresponds to a limited range of said physical property of the tissue (10), and a descriptor is determined for each of said at least one domain, wherein the descriptor for a given domain (V) comprises information related to the photoacoustic image (20) for each contributing domain (v11,...,v33) of a set of contributing domains (Cv), wherein the set of contributing domains (Cv) comprises one or more domains other than said given domain (V).