Development of MRI-based axillary numerical models and estimation of axillary lymph node dielectric properties for microwave imaging.
anthropomorphic models
axillary lymph nodes
axillary region
breast cancer
dielectric properties
magnetic resonance imaging
microwave imaging
Journal
Medical physics
ISSN: 2473-4209
Titre abrégé: Med Phys
Pays: United States
ID NLM: 0425746
Informations de publication
Date de publication:
Oct 2021
Oct 2021
Historique:
revised:
20
07
2021
received:
29
03
2021
accepted:
22
07
2021
pubmed:
3
8
2021
medline:
6
11
2021
entrez:
2
8
2021
Statut:
ppublish
Résumé
Microwave imaging (MWI) has been studied as a complementary imaging modality to improve sensitivity and specificity of diagnosis of axillary lymph nodes (ALNs), which can be metastasized by breast cancer. The feasibility of such a system is based on the dielectric contrast between healthy and metastasized ALNs. However, reliable information such as anatomically realistic numerical models and matching dielectric properties of the axillary region and ALNs, which are crucial to develop MWI systems, are still limited in the literature. The purpose of this work is to develop a methodology to infer dielectric properties of structures from magnetic resonance imaging (MRI), in particular, ALNs. We further use this methodology, which is tailored for structures farther away from MR coils, to create MRI-based numerical models of the axillary region and share them with the scientific community, through an open-access repository. We use a dataset of breast MRI scans of 40 patients, 15 of them with metastasized ALNs. We apply image processing techniques to minimize the artifacts in MR images and segment the tissues of interest. The background, lung cavity, and skin are segmented using thresholding techniques and the remaining tissues are segmented using a K-means clustering algorithm. The ALNs are segmented combining the clustering results of two MRI sequences. The performance of this methodology was evaluated using qualitative criteria. We then apply a piecewise linear interpolation between voxel signal intensities and known dielectric properties, which allow us to create dielectric property maps within an MRI and consequently infer ALN properties. Finally, we compare healthy and metastasized ALN dielectric properties within and between patients, and we create an open-access repository of numerical axillary region numerical models which can be used for electromagnetic simulations. The proposed methodology allowed creating anatomically realistic models of the axillary region, segmenting 80 ALNs and analyzing the corresponding dielectric properties. The estimated relative permittivity of those ALNs ranged from 16.6 to 49.3 at 5 GHz. We observe there is a high variability of dielectric properties of ALNs, which can be mainly related to the ALN size and, consequently, its composition. We verified an average dielectric contrast of 29% between healthy and metastasized ALNs. Our repository comprises 10 numerical models of the axillary region, from five patients, with variable number of metastasized ALNs and body mass index. The observed contrast between healthy and metastasized ALNs is a good indicator for the feasibility of a MWI system aiming to diagnose ALNs. This paper presents new contributions regarding anatomical modeling and dielectric properties' characterization, in particular for axillary region applications.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5974-5990Subventions
Organisme : FEDER-PT2020 Partnership Agreement
ID : UIDB/EEA/50008/2020
Organisme : MEC | Fundação para a Ciência e a Tecnologia (FCT)
ID : UIDB/00645/2020
Organisme : MEC | Fundação para a Ciência e a Tecnologia (FCT)
ID : SFRH/BD/129230/2017
Informations de copyright
© 2021 American Association of Physicists in Medicine.
Références
The Global Cancer Observatory - World Health Organization. Breast cancer fact sheets. 2020. Available online http://gco.iarc.fr/today.
DeSantis CE, Ma J, Goding Sauer A, Newman LA, Jemal A. Breast cancer statistics, 2017, racial disparity in mortality by state. CA Cancer J Clin, 2017, 67, 439-448.
American Joint Committee on Cancer. Breast cancer. In AJCC Cancer Staging Manual, 8th ed. Springer; 2018.
George R. Selective application of routine preoperative axillary ultrasonography reduces costs for invasive breast cancers. Oncologist. 2011;16:1069.
Valente SA, Levine GM, Silverstein MJ, et al. Accuracy of predicting axillary lymph node positivity by physical examination, mammography, ultrasonography, and magnetic resonance imaging. Ann Surg Oncol. 2012;19:1825-1830.
Veronesi U, Paganelli G, Viale G, et al. A randomized comparison of sentinel-node biopsy with routine axillary dissection in breast cancer. N Engl J Med. 2003;349:546-553.
Krag DN, Anderson SJ, Julian TB, et al. Technical out-comes of sentinel-lymph-node resection and conventional axillary-lymph-node dissection in patients with clinically node-negative breast cancer: results from the NSABP B-32 randomised phase III trial. Lancet Oncol. 2007;8:881-888.
Shere M, Lyburn I, Sidebottom R, Massey H, Gillett C, Jones L. MARIA ® M5: a multicentre clinical study to evaluate the ability of the Micrima radio-wave radar breast imaging system (MARIA ®) to detect lesions in the symptomatic breast. Eur J Radiol. 2019;116:61-67.
Vasquez JA, Scapaticci R, Turvani G, et al. A prototype microwave system for 3D brain stroke imaging. Sensors. 2020;20:1-16.
Eleutério RJN. Microwave Imaging of the Axilla to Aid Breast Cancer Diagnosis, Master’s dissertation, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2014.
Savazzi M, Abedi S, Is̆tuk N, et al. Conceição, Development of an anthropomorphic phantom of the axillary region for microwave imaging assessment. Sensors (Switzerland). 2020;20:1-20.
Godinho DM, Felicio JM, Fernandes CA, Conceição RC. Experimental evaluation of an axillary microwave imaging system to aid breast cancer staging. IEEE J Electromagn RF Microw Med Biol, 2021. Available online 10.1109/JERM.2021.3097877 (Article in press).
Gosselin MC, Neufeld E, Moser H, et al. Development of a new generation of high-resolution anatomical models for medical device evaluation: the Virtual Population 3.0. Phys Med Biol. 2014;59:5287-5303.
COST European Cooperation in Science & Technology. Memorandum of Understanding for the implementation of the COST Action “European network for advancing Electro- magnetic hyperthermic medical technologies” (MyWAVE) CA17115 Technical report. 2018.
O'Loughlin D, O'Halloran M, Moloney BM, Glavin M, Jones E, Elahi MA. Microwave breast imaging: clinical advances and remaining challenges. IEEE Trans Biomed Eng. 2018;65:2580-2590.
Joines WT, Zhang Y, Li C, Jirtle RL. The measured electrical properties of normal and malignant human tissues from 50 to 900 MHz. Med Phys. 1994;21:547-550.
Gabriel S, Lau RW, Gabriel C. The dielectric properties of biological tissues: III parametric models for the dielectric spectrum of tissues. Phys Med Biol. 1996;41:2271-2293.
Lazebnik M, McCartney L, Popovic D, et al. A large-scale study of the ultrawideband microwave dielectric properties of normal breast tissue obtained from reduction surgeries. Phys Med Biol. 2007;52:2637-3656.
Choi JW, Cho J, Lee Y, et al. Microwave detection of metastasized breast cancer cells in the lymph node; potential application for sentinel lymphadenectomy. Breast Cancer Res Treat. 2004;86:107-115.
Cameron TR, Okoniewski M, Fear EC. A preliminary study of the electrical properties of healthy and diseased lymph nodes. In International Symposium on Antenna Technology and Applied Electromagnetics & the American Electromagnetics Conference (ANTEM-AMEREM). IEEE; 2010:1-3.
Yu X, Sun Y, Cai K, et al. Dielectric properties of normal and metastatic lymph nodes ex vivo from lung cancer surgeries. Bioelectromagnetics. 2020;41:148-155.
Standring S. Blood, lymphoid tissues and haemopoiesis. In Gray’s Anatomy: The Anatomical Basis of Clinical Practice, 41st ed. Elsevier Limited; 2016:68-80.
La Gioia A, O'Halloran M, Porter E. Challenges of Post-measurement Histology for the Dielectric Characterisation of Heterogeneous Biological Tissues. Sensors. 2020;20:3290-3304.
Godinho DM, Felicio JM, Castela T, et al. Extracting dielectric properties for MRI-based phantoms for axillary microwave imaging device. In 14th European Conference on Antennas and Propagation, EuCAP 2020. IEEE; 2020:3-6.
Zastrow E, Davis SK, Lazebnik M, Kelcz F, Veen BDV, Hagness SC. Development of anatomically realistic numerical breast phantoms with accurate dielectric properties for modeling microwave interactions with the human breast. IEEE Trans Biomed Eng. 2008;55:2792-2800.
Tuncay AH, Akduman I. Realistic microwave breast models through T1-weighted 3-D MRI data. IEEE Trans Biomed Eng. 2015;62:688-698.
Omer M, Fear EC. Automated 3D method for the construction of flexible and reconfigurable numerical breast models from MRI scans. Med Biol Eng Compu. 2018;56:1027-1040.
Lu M, Xiao X, Song H, Liu G, Lu H, Kikkawa T. Accurate construction of 3-D numerical breast models with anatomical information through MRI scans. Comput Biol Med. 2021;130:104205.
Unal G, Slabaugh G, Ess A, et al. Semi-automatic lymph node segmentation in LN-MRI. In International Conference on Image Processing, IEEE; 2006:77-80.
Barbu A, Suehling M, Xu X, Liu D, Zhou SK, Comaniciu D. Automatic detection and segmentation of lymph nodes from CT data. IEEE Trans Med Imaging. 2012;31:240-250.
Ha R, Chang P, Karcich J, et al. Axillary lymph node evaluation utilizing convolutional neural networks using MRI dataset. J Digit Imaging. 2018;31:851-856.
Mori N, Tsuchiya K, Sheth D, et al. Diagnostic value of electric properties tomography (EPT) for differentiating benign from malignant breast lesions: comparison with standard dynamic contrast-enhanced MRI. Eur Radiol. 2019;29:1778-1786.
Leijsen R, van den Berg C, Webb A, Remis R, Mandija S. Combining deep learning and 3D contrast source inversion in MR-based electrical properties tomography. NMR Biomed. 2019;e4211:1-7.
Woodard HQ, White DR. The composition of body tissues. Br J Radiol. 1986;59:1209-1219.
Yaniv Z, Lowekamp BC, Johnson HJ, Beare R. SimpleITK image-analysis notebooks: a collaborative environment for education and reproducible research. J Digit Imaging. 2018;31:290-303.
Tustison NJ, Gee JC. N4ITK: Nick's N3 ITK implementation for MRI bias field correction. InsightJournal. 2009;9:1-8.
Otsu N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 1979;9:62-66.
Arthur D, Vassilvitskii S. k-means++: the advantages of careful seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics; 2007:1027-1035.
Bazhin A Ellipsoid fit python. 2020. Available online: https://github.com/aleksandrbazhin/ellipsoid_fit_python.
Turner DA, Anderson IJ, Mason JC, Cox MG. An Algorithm for Fitting an Ellipsoid to Data, Technical Report RR9803. National Physical Laboratory; 1999.
Godinho DM, Felicio JM, Castela T, et al. Axillary region models repository for electromagnetic applications. 2021. Available online: https://github.com/dmgodinho/axillary-region-models-repository.
Thomas LW. The chemical composition of adipose tissue of man and mice. Q J Exp Physiol Cogn Med Sci. 1962;47:179-188.
Askew E. Water. In: Ziegler E, Filer L, eds. Present Knowledge in Nutrition. ILSI Press; 1996:98-108.
Huwe LW, Brown WE, Hu JC, Athanasiou KA. Characterization of costal cartilage and its suitability as a cell source for articular cartilage tissue engineering. J Tissue Eng Regen Med. 2018;12:1163-1176.