TY - CHAP
T1 - Comparison of Reconstruction Algorithms for Brain Stroke Microwave Imaging
AU - Mariano, Valeria
AU - Vasquez, Jorge Alberto Tobon
AU - Scapaticci, Rosa
AU - Crocco, Lorenzo
AU - Kosmas, Panagiotis
AU - Vipiana, Francesca
N1 - Funding Information:
This work was supported by the Italian Ministry of University and Research under the PRIN project MiBraScan - Microwave Brain Scanner for Cerebrovascular Diseases Monitoring
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/12/14
Y1 - 2020/12/14
N2 - The aim of this paper is to describe and compare the performances of three image reconstruction algorithms that can be used for brain stroke microwave imaging. The algorithms belong to the class of non-linear iterative algorithms and are capable of providing a quantitative map of the imaged scenario. The first algorithm is the Contrast Source Inversion (CSI) method, which uses the Finite Element Method (FEM) to discretize the domain of interest. The second one is the Subspace-Based Optimization Method (SOM) that has some properties in common with the CSI method, and it also uses FEM to discretize the domain. The last one is the Distorted Born Iterative Method with the inverse solver Two-step Iterative Shrinkage/Thresholding (DBIM-TwIST), which exploits the forward Finite Difference Time Domain (FDTD) solver. The reconstruction examples are created with 3-D synthetic data modelling realistic brain tissues with the presence of a blood region, representing the stroke area in the brain, whereas the inversion step is carried out using a 2-D model.
AB - The aim of this paper is to describe and compare the performances of three image reconstruction algorithms that can be used for brain stroke microwave imaging. The algorithms belong to the class of non-linear iterative algorithms and are capable of providing a quantitative map of the imaged scenario. The first algorithm is the Contrast Source Inversion (CSI) method, which uses the Finite Element Method (FEM) to discretize the domain of interest. The second one is the Subspace-Based Optimization Method (SOM) that has some properties in common with the CSI method, and it also uses FEM to discretize the domain. The last one is the Distorted Born Iterative Method with the inverse solver Two-step Iterative Shrinkage/Thresholding (DBIM-TwIST), which exploits the forward Finite Difference Time Domain (FDTD) solver. The reconstruction examples are created with 3-D synthetic data modelling realistic brain tissues with the presence of a blood region, representing the stroke area in the brain, whereas the inversion step is carried out using a 2-D model.
KW - brain stroke imaging
KW - Contrast Source Inversion (CSI) method
KW - Distorted Born Iterative Method (DBIM)
KW - microwave imaging
KW - Subspace-Based Optimization Method (SOM)
UR - http://www.scopus.com/inward/record.url?scp=85104394991&partnerID=8YFLogxK
U2 - 10.1109/IMBIoC47321.2020.9385032
DO - 10.1109/IMBIoC47321.2020.9385032
M3 - Conference paper
AN - SCOPUS:85104394991
T3 - 2020 IEEE MTT-S International Microwave Biomedical Conference, IMBioC 2020
BT - 2020 IEEE MTT-S International Microwave Biomedical Conference, IMBioC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE MTT-S International Microwave Biomedical Conference, IMBioC 2020
Y2 - 14 December 2020 through 17 December 2020
ER -