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93 results for “EIT”
Simulated EIT circular targets with noise and blur
<p>Companion data for the paper "Post-processing electrical impedance tomography reconstructions with incomplete data using convolutional neural networks".</p> <p>See <a href="https://github.com/robert-abc/KTC2023-ABC1">https://github.com/robert-abc/KTC2023-ABC1</a> for more information. </p> <p>Files:</p> <ul> <li>CNN_input.mat: CNN input, the noisy data</li> <li>CNN_output.mat: CNN output, the clean data</li> <li><em>cnn_training.py: </em>Describes the CNN training using Keras</li> <li><em>CNN_training.ipynb: </em>Notebook after the CNN training</li> <li><em>ultimate_cnn1.h5: </em>CNN (keras) file after training</li> </ul> <p>We uploaded the files to Google Drive and executed the code using Google Colab.</p> <p>Before executing the code, one should change the current working directory to the folder where the files are.</p>
EIT-EIC COLLAB PILOT
<p>In the framework of this project we have collected 162 applications from different start-ups across Europe that are looking for our support and aim to join and participate in a selected set of our programmes. From them, 40 start-ups have been selected.</p>
Videos: EIT Food - SmartBreakfast project
<p>Videos about EIT-Food SmartBreakfast project, 3D printed breakfast bar and a senior consumer test carried out at AZTI.</p> <p><strong>YouTube links</strong>:</p> <p><a href="http://: https://www.youtube.com/watch?v=UplSyeEv-2s&t=3s">SmartBreakfast project – EIT Food: partners, objectives and results</a></p> <p><a href="https://www.youtube.com/watch?v=G2ps2qSgN88">3D printer Breakfast Bar</a></p> <p><a href="https://www.youtube.com/watch?v=N9Uck-ImBms">Senior consumer test of a 3D printed breakfast bar</a></p>
Experimental Electrical Impedance Tomography Data Set Using the Spectra Bioimpedance and EIT Complete Kit
<p><strong>Abstract</strong></p> <p>The amount of open-source EIT measurement data is low. This data set concerns the acquisition and processing of different measurement data using the SpectraEIT-Kit as well as information about the deposited files.</p> <p><strong>Aknoelegenment</strong><br> Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1270/2 - 299150580.</p>
Semi-Siamese U-Net for separation of lung and heart bioimpedance images: a simulation study of thorax EIT
<p><span>Electrical impedance tomography (EIT) is widely used for bedside monitoring of lung ventilation status. Its goal is to reflect the internal conductivity changes and estimate the electrical properties of the tissues in the thorax. However, poor spatial resolution affects EIT image reconstruction to the extent that the heart and lung-related impedance images are barely <a name="_Hlk56106607">distinguishable</a>. Several studies have attempted to tackle this problem, and approaches based on decomposition of EIT images using linear transformations have been developed, and recently, U-Net has become a prominent architecture for semantic segmentation. In this paper, we propose a novel semi-Siamese U-Net specifically tailored for EIT application. It is <a name="_Hlk48748756">based on the state-of-the-art U-Net</a>, whose structure is modified and extended, forming shared encoder with parallel decoders and has multi-task weighted losses added to adapt to the individual separation tasks. The trained semi-Siamese U-Net model was evaluated with a test dataset, and the results were compared with those of the classical U-Net in terms of Dice similarity coefficient and mean absolute error. </span></p> <p><span>Results showed that compared with the classical U-Net, semi-Siamese U-Net exhibited performance improvements of 11.37% and 3.2% in Dice similarity coefficient, and 3.16% and 5.54% in mean absolute error, in terms of heart and lung-impedance image separation, respectively.</span></p>
EIT data of a human arm
<p>Authors:Brian Brown Date:27th November 1981Brief Description:Data were recorded from Rod Smallwoood's arm on the 27th November 1981; the dot matrix image which shows the ulna and radius bones. We made a 'radiotherapy type' mould of the arm and then put drawing pins through the plastic (pin head inwards) as electrodes. There two sets of data. One is recorded from the arm and the other is with saline filling the mould.</p> <p>The data were published in:<br /> D.C. Barber, B.H. Brown, and I.L. Freeston, "Imaging spatial distributions of resistivity using applied potential tomography", <em>Electronics Letters</em>, 19(22):933-935, 1983 http://digital-library.theiet.org/content/journals/10.1049/el_19830637</p> <p>License:Creative Commons Artistic License (with Attribution)Attribution Requirement:Use or presentation of these data reference this publication: D.C. Barber, B.H. Brown, and I.L. Freeston, "Imaging spatial distributions of resistivity using applied potential tomography", <em>Electronics Letters</em>, 19(22):933-935, 1983</p> <p>Format:Data are handwritten and scanned into the linked pdf file. The adjacent drive/receive data sets for both the Uniform(Saline) and Arm data and these are included in the attached Excel file. The are 6 columns of data in the xls file. The first three are for the uniform case and give the two reciprocal data sets and the mean of the two. Columns 4-6 are for the arm. I did a quick reconstruction using columns 3 and 6 as ref and data respectively and it looked OK.</p> <p>Methods:The pdf file that is attached shows the line printer output of the data we recorded from Rod Smallwoood's arm on the 27th November 1981 and the dot matrix image which shows the ulna and radius bones. We made a 'radiotherapy type' mould of the arm and then put drawing pins through the plastic (pin head inwards) as electrodes. There two sets of data. One is recorded from the arm and the other is with saline filling the mould. The pdf file also shows my plot of the XY position of the electrodes.</p> <p>Now the data set on the line printer is a complete data set i.e. Drive 1/2 then 1/3 then 1/4 etc for every combination. I could only find the print out for one of the data sets. However, I found my notebook with the adjacent drive/receive data set and this is page 7 of the pdf file.</p> <p>I have extracted the adjacent drive/receive data sets for both the Uniform(Saline) and Arm data and these are included in the attached Excel file. The are 6 columns of data in the xls file. The first three are for the uniform case and give the two reciprocal data sets and the mean of the two. Columns 4-6 are for the arm. I did a quick reconstruction using columns 3 and 6 as ref and data respectively and it looked OK.</p> <p>The first column of data is 104 point as follows.</p> <blockquote> <p>Drive 1/2 receive 3/4 Drive 1/2 receive 4/5 etc Drive 1/2 receive 16/1 Drive 2/3 receive 4/5 Drive 2/3 receive 5/6 etc Drive 2/3 receive 16/1 Drive 4/5 receive 6/7 Drive 4/5 receive 7/8 etc Drive 4/5 receive 16/1 etc etc Drive 14/15 receive 16/1</p> </blockquote> <p>The second column is the other reciprocal set.</p> <p>I think these data are the ones used to produce the image in the Electronics Letters paper of 1983 - page 1 of my pdf file.</p>
UCLH Stroke EIT Dataset - Subjects
<p><strong>The UCLH Stroke EIT Dataset - Subject Data</strong></p> <p>Each folder contains the raw data for each subject recording.</p> <p>Full details on the use of these files are given the repository https://github.com/EIT-team/Stroke_EIT_Dataset</p>
EIT-team/Stroke_EIT_Dataset: Publication release
<p>The version used to accompany publication of the data. This has minor DOI and file structure updates from previous version</p>
UCLH Stroke EIT Dataset - Radiology Data
<p><strong>The UCLH Stroke EIT Dataset - Radiology Reports</strong></p> <p>Each folder contains the anonymised radiology data, and clinical reports for all patients in the study. The latest version follows the BIDS structure</p> <p>Full details on the use of these files are given the repository https://github.com/EIT-team/Stroke_EIT_Dataset</p> <p><strong>Version 4 - Latest BIDS version. Use this version for NIFTI files</strong></p> <p>Version 3 - Initial BIDS version</p> <p><strong>Version 2 - Updated DICOM. Use this version if you wish to use the original DICOM files</strong></p> <p>Version 1 - Initial upload</p> <p> </p> <p> </p>
Electrical Impedance Tomography (EIT) measurement monitoring the respiratory status
<p>This dataset was used in a study titled "Structural priors represented by discrete cosine transform improve EIT functional imaging" which is currently pending publication. The dataset consists of Electrical Impedance Tomography (EIT) measurements that were collected during the monitoring of respiratory status in tested subjects. The data is stored as .get files, which can be imported into the Matlab® workspace using the codes provided on GitHub at https://github.com/rongqing-chen/DCT-EIT.</p> <p>Using the dataset requires the citation of the following publication:</p> <ul> <li>Lovas A, Chen R, Molnár T, Benyó B, Szlávecz Á, Hawchar F, et al. Differentiating Phenotypes of Coronavirus Disease-2019 Pneumonia by Electric Impedance Tomography. Frontiers in Medicine. 2022;9. doi: 10.3389/fmed.2022.747570</li> <li>R. Chen, S. Krueger-Ziolek, A. Lovas, B. Benyó, S.J. Rupitsch, K. Moeller (2023) Structural priors represented by discrete cosine transform improve EIT functional imaging. PLoS ONE 18(5): e0285619. 10.1371/journal.pone.0285619</li> </ul>
Testing the Implementation of EIT-4-BPSD
ClinicalTrials.gov study NCT03014570. IPD Sharing: NO. Countries: 1. Publications: 2.
Correlation Between EIT-based Pulse Wave Method for Pulmonary Perfusion Monitoring and Pulmonary Artery Catheter-based Stroke Volume Measurement
ClinicalTrials.gov study NCT07385963. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Semi-Siamese U-Net for separation of lung and heart bioimpedance images: a simulation study of thorax EIT
Open the record for dataset details and reuse information.
UCLH Stroke EIT Dataset Patients - Part 1
<p><strong>The UCLH Stroke EIT Dataset - Patient Data Part 1</strong></p> <p>Each folder contains the raw data for patients 1-14.</p> <p>Full details on the use of these files are given the repository https://github.com/EIT-team/Stroke_EIT_Dataset</p>
UCLH Stroke EIT Dataset Patients - Part 2
<p><strong>The UCLH Stroke EIT Dataset - Patient Data Part 2</strong></p> <p>Each folder contains the raw data for patients 15-26.</p> <p>Full details on the use of these files are given the repository https://github.com/EIT-team/Stroke_EIT_Dataset</p>
Effect of Early Mobilization on Regional Lung Ventilation Assessed by EIT
ClinicalTrials.gov study NCT04081129. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Monitorings the Physiological Mechanism of Airway Pressure Release Ventilation in ARDS Patients by EIT
ClinicalTrials.gov study NCT05406427. IPD Sharing: NO. Countries: 1. Publications: 5.
Effect of EIT-guided PEEP Titration on the Prognosis of Patients with Moderate to Severe ARDS
ClinicalTrials.gov study NCT05207202. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Optimizing Intraoperative Mechanical Ventilation Using EIT-titrated PEEP
ClinicalTrials.gov study NCT02314845. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Early PP Monitored by EIT in Patients With ARDS
ClinicalTrials.gov study NCT05822869. IPD Sharing: NO. Countries: 1. Publications: 10.
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