A Versatile Noise Performance Metric for Electrical Impedance Tomography Algorithms.

Details

Serval ID
serval:BIB_43C404DEC606
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
A Versatile Noise Performance Metric for Electrical Impedance Tomography Algorithms.
Journal
IEEE transactions on bio-medical engineering
Author(s)
Braun F., Proenca M., Sola J., Thiran J.P., Adler A.
ISSN
1558-2531 (Electronic)
ISSN-L
0018-9294
Publication state
Published
Issued date
10/2017
Peer-reviewed
Oui
Volume
64
Number
10
Pages
2321-2330
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
Electrical impedance tomography (EIT) is an emerging technology for real-time monitoring of patients under mechanical ventilation. EIT has the potential to offer continuous medical monitoring while being noninvasive, radiation free, and low cost. Due to their ill-posedness, image reconstruction typically uses regularization, which implies a hyperparameter controlling the tradeoff between noise rejection and resolution or other accuracies. In order to compare reconstruction algorithms, it is common to choose hyperparameter values such that the reconstructed images have equal noise performance (NP), i.e., the amount of measurement noise reflected in the images. For EIT many methods have been suggested, but none work well when the data originate from different measurement setups, such as for different electrode positions or measurement patterns. To address this issue, we propose a new NP metric based on the average signal-to-noise ratio in the image domain. The approach is validated for EIT using simulation experiments on a human thorax model and measurements on a resistor phantom. Results show that the approach is robust to the measurement configuration (i.e., number and position of electrodes, skip pattern) and the reconstruction algorithm used. We propose this novel approach as a way to select optimized measurement configurations and algorithms.

Pubmed
Web of science
Open Access
Yes
Create date
16/10/2017 17:24
Last modification date
20/08/2019 14:47
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