Edinburgh mfEIT simulation and realworld dataset

Citation Author(s):
Hao
Fang
the SMART Group, Institute for Imaging, Data and Communications, School of Engineering, The University of Edinburgh
Zhe
Liu
the SMART Group, Institute for Imaging, Data and Communications, School of Engineering, The University of Edinburgh
Yunjie
Yang
the SMART Group, Institute for Imaging, Data and Communications, School of Engineering, The University of Edinburgh
Submitted by:
HAO FANG
Last updated:
Tue, 02/25/2025 - 05:54
DOI:
10.21227/7s1c-0t50
Research Article Link:
Multi-frequency Electrical Impedance Tomography Reconstruction with Multi-Branch Attention Image Prior
License:
Creative Commons Attribution
25 Views
Categories:
Artificial Intelligence
Signal Processing
Machine Learning
Biomedical and Health Sciences
Medical Imaging
Keywords:
Multi-frequency Electrical Impedance Tomography, Unsupervised Learning, Multi-Branch Attention Image Prior, inverse problem
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Abstract 

This dataset is designed for research on 2D Multi-frequency Electrical Impedance Tomography (mfEIT). It includes:

  • Projection Matrices: For coordinate transformation between EIT and rectangular grids, including thoracic models.
  • Sensitivity Matrices: For calculating conductivity variations in thoracic and circular models.
  • Voltage Measurements: Simulated and real-world data, including lung and zebrafish experiments.

This dataset supports the development and validation of mfEIT reconstruction algorithms. Please cite "Multi-frequency Electrical Impedance Tomography Reconstruction with Multi-Branch Attention Image Prior" if used in research.

Instructions: 

1. Projection Matrix

This directory contains projection matrices for coordinate transformation and projection operations.

 

pos: Electrode and mesh position data for the EIT simulation model.

pos_thorax: Electrode and mesh position data for the thoracic model.

projection_matrix_eit2rect.npy: Projection matrix for mapping from EIT coordinates to a rectangular grid.

projection_matrix_rect2eit.npy: Projection matrix for mapping from a rectangular grid to EIT coordinates.

projection_matrix_thorax_eit2rect.npy: In the thoracic model, projection matrix for mapping from EIT coordinates to a rectangular grid.

projection_matrix_thorax_rect2eit.npy: In the thoracic model, projection matrix for mapping from a rectangular grid to EIT coordinates.

 

2. Sensitivity Matrix

This directory contains sensitivity matrices used to calculate the relationship between voltage measurements and conductivity variations.

 

sensitivity_thorax: Sensitivity matrix for the thoracic model.

Sensitivity16_64: Sensitivity matrix for the circular model.

 

3. Voltage Measurements

This directory contains voltage measurement data for both simulated and real-world scenarios.

 

V_case1_sim, V_case2_sim, V_case3_sim: Voltage measurements for different simulation scenarios.

V_DBLung_sim: Simulated voltage data for deep breathing simulation.

V_Lung_realworld: Voltage measurements from real-world lung experiments.

V_zebrafish_realworld: Real-world voltage measurements from zebrafish experiments.

 

V_test_realworld: Voltage measurements from real-world test experiments.

V_test_sim: Simulated voltage measurements for testing purposes.

 

Usage

The Projection Matrix and Sensitivity Matrix are used for forward and inverse computations in EIT reconstruction algorithms.

Voltage Measurements are used to validate the performance of reconstruction algorithms, including both simulated and real-world datasets.

Citation and Acknowledgment

 

If you use this dataset in your research, please cite paper 'Multi-frequency Electrical Impedance Tomography Reconstruction with Multi-Branch Attention Image Prior'.

Dataset Files

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