scholarly journals Combining deep learning and 3D contrast source inversion in MR‐based electrical properties tomography

2019 ◽  
Author(s):  
Reijer Leijsen ◽  
Cornelis van den Berg ◽  
Andrew Webb ◽  
Rob Remis ◽  
Stefano Mandija
Keyword(s):  
Deep Learning ◽  
Electrical Properties ◽  
Source Inversion ◽  
Contrast Source Inversion

scholarly journals Transceive phase corrected 2D contrast source inversion‐electrical properties tomography

2020 ◽  
Author(s):  
Peter R. S. Stijnman ◽  
Stefano Mandija ◽  
Patrick S. Fuchs ◽  
Cornelis A. T. Berg ◽  
Rob F. Remis
Keyword(s):  
Electrical Properties ◽  
Source Inversion ◽  
Contrast Source Inversion

scholarly journals Enhancement of Multimodal Microwave-Ultrasound Breast Imaging Using a Deep-Learning Technique

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4050 ◽  
Author(s):  
Vahab Khoshdel ◽  
Ahmed Ashraf ◽  
Joe LoVetri
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Breast Imaging ◽  
Reconstruction Error ◽  
Tumor Segmentation ◽  
Source Inversion ◽  
The Neural Network ◽  
Contrast Source Inversion ◽  
Imaging Artifacts ◽  
Complex Valued

We present a deep learning method used in conjunction with dual-modal microwave-ultrasound imaging to produce tomographic reconstructions of the complex-valued permittivity of numerical breast phantoms. We also assess tumor segmentation performance using the reconstructed permittivity as a feature. The contrast source inversion (CSI) technique is used to create the complex-permittivity images of the breast with ultrasound-derived tissue regions utilized as prior information. However, imaging artifacts make the detection of tumors difficult. To overcome this issue we train a convolutional neural network (CNN) that takes in, as input, the dual-modal CSI reconstruction and attempts to produce the true image of the complex tissue permittivity. The neural network consists of successive convolutional and downsampling layers, followed by successive deconvolutional and upsampling layers based on the U-Net architecture. To train the neural network, the input-output pairs consist of CSI’s dual-modal reconstructions, along with the true numerical phantom images from which the microwave scattered field was synthetically generated. The reconstructed permittivity images produced by the CNN show that the network is not only able to remove the artifacts that are typical of CSI reconstructions, but can also improve the detectability of tumors. The performance of the CNN is assessed using a four-fold cross-validation on our dataset that shows improvement over CSI both in terms of reconstruction error and tumor segmentation performance.

scholarly journals 3-D Contrast Source Inversion-Electrical Properties Tomography

2018 ◽  
Vol 37 (9) ◽  
pp. 2080-2089 ◽  
Author(s):  
Reijer L. Leijsen ◽  
Wyger M. Brink ◽  
Cornelis A. T. van den Berg ◽  
Andrew G. Webb ◽  
Rob F. Remis
Keyword(s):  
Electrical Properties ◽  
Source Inversion ◽  
Contrast Source Inversion

scholarly journals Full 3D Microwave Breast Imaging Using a Deep-Learning Technique

2020 ◽  
Vol 6 (8) ◽  
pp. 80 ◽  
Author(s):  
Vahab Khoshdel ◽  
Mohammad Asefi ◽  
Ahmed Ashraf ◽  
Joe LoVetri
Keyword(s):  
Deep Learning ◽  
Breast Imaging ◽  
Synthetic Data ◽  
Roc Curves ◽  
Source Inversion ◽  
Operating Characteristics ◽  
3D Images ◽  
Size Number ◽  
Contrast Source Inversion ◽  
Learning Technique

A deep learning technique to enhance 3D images of the complex-valued permittivity of the breast obtained via microwave imaging is investigated. The developed technique is an extension of one created to enhance 2D images. We employ a 3D Convolutional Neural Network, based on the U-Net architecture, that takes in 3D images obtained using the Contrast-Source Inversion (CSI) method and attempts to produce the true 3D image of the permittivity. The training set consists of 3D CSI images, along with the true numerical phantom images from which the microwave scattered field utilized to create the CSI reconstructions was synthetically generated. Each numerical phantom varies with respect to the size, number, and location of tumors within the fibroglandular region. The reconstructed permittivity images produced by the proposed 3D U-Net show that the network is not only able to remove the artifacts that are typical of CSI reconstructions, but it also enhances the detectability of the tumors. We test the trained U-Net with 3D images obtained from experimentally collected microwave data as well as with images obtained synthetically. Significantly, the results illustrate that although the network was trained using only images obtained from synthetic data, it performed well with images obtained from both synthetic and experimental data. Quantitative evaluations are reported using Receiver Operating Characteristics (ROC) curves for the tumor detectability and RMS error for the enhancement of the reconstructions.

Finite-element contrast source inversion method for microwave imaging

2010 ◽  
Vol 26 (11) ◽  
pp. 115010 ◽  
Author(s):  
Amer Zakaria ◽  
Colin Gilmore ◽  
Joe LoVetri
Keyword(s):  
Finite Element ◽  
Microwave Imaging ◽  
Inversion Method ◽  
Source Inversion ◽  
Contrast Source Inversion
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