TY - GEN
T1 - EIT-CDAE
T2 - 2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019
AU - Gao, Yue
AU - Lu, Yewangqing
AU - Li, Hui
AU - Liu, Boxiao
AU - Li, Yongfu
AU - Chen, Mingyi
AU - Wang, Guoxing
AU - Lian, Yong
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Electrical Impedance Tomography is considered to be an alternative substitution to CT and MRI technologies as it is a non-invasive, safe medical imaging technology, and free of ionizing or heating radiation. Similar to CT and MRI technologies, reconstructing a two-dimensional EIT image is also considered an ill-posed and non-linear inverse problem, where the image quality is highly sensitive to the measurement data, and often random noise artifacts appear in the image with the different non-linear algorithms. Therefore, in this work, we have proposed a new EIT image reconstruction algorithm based on the convolution denoising autoencoder (CDAE) deep learning algorithm. Our EIT-CDAE used a convolutional neural network in the encoder and decoder network. From our experimental data using phantom data, our EIT-CDAE model has reconstructed a better EIT image quality, removing any noise artifacts, making it more robust compared to the conventional stacked autoencoder and traditional non-linear algorithms. The source code is available in the github: https://github.com/yongfu-li/eit-cdae-Algorithm
AB - Electrical Impedance Tomography is considered to be an alternative substitution to CT and MRI technologies as it is a non-invasive, safe medical imaging technology, and free of ionizing or heating radiation. Similar to CT and MRI technologies, reconstructing a two-dimensional EIT image is also considered an ill-posed and non-linear inverse problem, where the image quality is highly sensitive to the measurement data, and often random noise artifacts appear in the image with the different non-linear algorithms. Therefore, in this work, we have proposed a new EIT image reconstruction algorithm based on the convolution denoising autoencoder (CDAE) deep learning algorithm. Our EIT-CDAE used a convolutional neural network in the encoder and decoder network. From our experimental data using phantom data, our EIT-CDAE model has reconstructed a better EIT image quality, removing any noise artifacts, making it more robust compared to the conventional stacked autoencoder and traditional non-linear algorithms. The source code is available in the github: https://github.com/yongfu-li/eit-cdae-Algorithm
KW - Electrical impedance tomography
KW - autoencoder
KW - convolutional neural network
KW - deep learning
KW - image reconstruction
UR - https://www.scopus.com/pages/publications/85077074037
U2 - 10.1109/BIOCAS.2019.8918979
DO - 10.1109/BIOCAS.2019.8918979
M3 - 会议稿件
AN - SCOPUS:85077074037
T3 - BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings
BT - BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 October 2019 through 19 October 2019
ER -