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Discrimination of Complex Activation Patterns in Near Infrared Optical Tomography with Artificial Neural Networks


Jiang, Jingjing; Ahnen, Linda; Lindner, Scott; Di Costanzo Mata, Aldo; Kalyanov, Alexander; Scholkmann, Felix; Wolf, Martin; Sánchez Majos, Salvador (2018). Discrimination of Complex Activation Patterns in Near Infrared Optical Tomography with Artificial Neural Networks. Advances in Experimental Medicine and Biology, 1072:313-318.

Abstract

Near-infrared optical tomography (NIROT) has great promise for many clinical problems. Here we focus on the study of brain function. During NIROT image reconstruction of brain activity, an inverse problem has to be solved that is sensitive to small superficial perturbations on the head such as e.g. birthmarks on the skin and hair. To consider these perturbations, standard physical modeling is unpractical, since it requires the implementation of detailed information that is generally unavailable. The aim here was to test whether artificial neural networks (ANN) are able to handle such perturbations and thus detect brain activity correctly. For simplicity, we created a virtual test model, where we simulated a pattern of activated and resting brain regions, which was covered by skin features like hair or melanin. We compared the performance of this ANN approach with that of an inverse problem based on a Monte Carlo (MC) model for light propagation. We conclude that ANNs tolerate substantially higher levels of skin perturbations than MC models and consequently are more suitable for detecting brain activity.

Abstract

Near-infrared optical tomography (NIROT) has great promise for many clinical problems. Here we focus on the study of brain function. During NIROT image reconstruction of brain activity, an inverse problem has to be solved that is sensitive to small superficial perturbations on the head such as e.g. birthmarks on the skin and hair. To consider these perturbations, standard physical modeling is unpractical, since it requires the implementation of detailed information that is generally unavailable. The aim here was to test whether artificial neural networks (ANN) are able to handle such perturbations and thus detect brain activity correctly. For simplicity, we created a virtual test model, where we simulated a pattern of activated and resting brain regions, which was covered by skin features like hair or melanin. We compared the performance of this ANN approach with that of an inverse problem based on a Monte Carlo (MC) model for light propagation. We conclude that ANNs tolerate substantially higher levels of skin perturbations than MC models and consequently are more suitable for detecting brain activity.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Neonatology
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Life Sciences > General Biochemistry, Genetics and Molecular Biology
Language:English
Date:2018
Deposited On:25 Oct 2018 10:32
Last Modified:29 Jul 2020 07:52
Publisher:Springer
ISSN:0065-2598
OA Status:Closed
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1007/978-3-319-91287-5_50
PubMed ID:30178364

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