TY - CHAP
T1 - Effect of Varying Prior Information in Axillary 2D Microwave Tomography
AU - Savazzi, Matteo
AU - Karadima, Olympia
AU - Felicio, Joao M.
AU - Fernandes, Carlos A.
AU - Kosmas, Panagiotis
AU - Conceicao, Raquel C.
N1 - Funding Information:
This work is also supported by Fundac¸ão para a Ciência e a Tecnologia-FCT, FCT/MEC (PIDDAC) under the Strategic Programme UIDB/00645/2020, and UIDB/50008/2020.
Funding Information:
This work was supported by the EMERALD project funded from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 764479.
Publisher Copyright:
© 2022 European Association for Antennas and Propagation.
PY - 2022/5/11
Y1 - 2022/5/11
N2 - We numerically assess the potential of microwave tomography (MWT) for the detection and dielectric properties estimation of axillary lymph nodes (ALNs), and we study the robustness of our system using prior information with varying levels of accuracy. We adopt a 2-dimensional MWT system with 8 antennas (0.5-2.5 GHz) placed around the axillary region. The reconstruction algorithm implements the distorted Born iterative method. We show that: (i) when accurate prior knowledge of the axillary tissues (fat and muscle) is available, our system successfully detects an ALN; (ii) ±30% error in the prior estimation of fat and muscle dielectric properties does not affect image quality; (iii) ±7mm error in muscle position causes slight artifacts, while ± 14mm error in muscle position affects ALN detection. To the best of our knowledge, this is the first paper in the literature to study the impact of prior information accuracy on detecting an ALN using MWT.
AB - We numerically assess the potential of microwave tomography (MWT) for the detection and dielectric properties estimation of axillary lymph nodes (ALNs), and we study the robustness of our system using prior information with varying levels of accuracy. We adopt a 2-dimensional MWT system with 8 antennas (0.5-2.5 GHz) placed around the axillary region. The reconstruction algorithm implements the distorted Born iterative method. We show that: (i) when accurate prior knowledge of the axillary tissues (fat and muscle) is available, our system successfully detects an ALN; (ii) ±30% error in the prior estimation of fat and muscle dielectric properties does not affect image quality; (iii) ±7mm error in muscle position causes slight artifacts, while ± 14mm error in muscle position affects ALN detection. To the best of our knowledge, this is the first paper in the literature to study the impact of prior information accuracy on detecting an ALN using MWT.
KW - axillary lymph node imaging
KW - breast cancer
KW - distorted Born iterative method (DBIM)
KW - microwave tomography
KW - prior information
UR - http://www.scopus.com/inward/record.url?scp=85130625902&partnerID=8YFLogxK
U2 - 10.23919/EuCAP53622.2022.9769372
DO - 10.23919/EuCAP53622.2022.9769372
M3 - Conference paper
AN - SCOPUS:85130625902
T3 - 2022 16th European Conference on Antennas and Propagation, EuCAP 2022
BT - 2022 16th European Conference on Antennas and Propagation, EuCAP 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th European Conference on Antennas and Propagation, EuCAP 2022
Y2 - 27 March 2022 through 1 April 2022
ER -
