FEM-based MRI deformation algorithm for breast deformation analysis

• Autorzy: Danch-Wierzchowska Marta , Borys Damian , Swierniak Andrzej

Identyfikatory

DOI

10.1016/j.bbe.2020.07.009

• Języki publikacji: EN

Abstrakty

EN

Breast tissue deformation has recently gained interest in various medical applications. The recovery of large deformations caused by gravity or compression loads and image registration is a non-trivial task. The most effective tool for breast cancer visualisation is Magnetic Resonance Imaging (MRI). However, for MRI scans the patient is in a prone position with the breast placed in signal enhancement coils, while other procedures, i.e. surgery, PET-CT (Positron Emission Tomography fused with Computer Tomography) are performed with the patient in a supine position. The need therefore arises to estimate the large breast deformations caused by natural body movement during examinations or surgery. There is no doubt that a patient's breast in both positions has a different shape and that this influences relationships between intra-breast structures. In this work, we present the fundamentals of a method for transformation of breast images based on Finite Element Methods (FEMs). This 2D model uses the simplest constitutive tissue description, which makes it easily applicable and fast. According to the Jaccard Index, the average accuracy obtained is 95%, the lowest is 87%, and the highest is 99%. The model parameter set is proposed for six different breast size classes, covering the whole population. The algorithm provides reliable breast images in a supine position in a few simple steps.

Słowa kluczowe

EN

breast deformation; finite element method; magnetic resonance imaging; PET-CT; breast cancer

PL

deformacja piersi; metoda elementów skończonych; rezonans magnetyczny; PET-CT; rak piersi

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 3
• Strony: 1304--1313
• Opis fizyczny: Bibliogr. 45 poz., rys., tab., wykr.

Twórcy

autor

Danch-Wierzchowska Marta

• Faculty of Biomedical Engineering, Silesian University of Technology, ul. Roosevelta 40, 41-800 Zabrze, Poland

autor

Borys Damian

• Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland

autor

Swierniak Andrzej

• Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland

Bibliografia

• [1] Wild CP, Stewart BW. World cancer report 2014. Technical report. International Agency for Research on Cancer; 2014.
• [2] WHO GLOBOCAN. Technical Report. World Health Organisation; 2012. p. 2012.
• [3] Verma M. Personalized medicine and cancer. J Pers Med 2012;2(1):1–14.
• [4] Zhang T, hui Liu Y. Optimal design of bionic flexible fixation system for MRI-guided breast biopsy. J Bionic Eng 2019;16:1116–26.
• [5] Breast cancer facts and figures 2015–2016. Technical report. American Cancer Society, Inc.; 2015.
• [6] Green CA, Goodsitt MM, Roubidoux MA, Brock KK, Davis CL, Lau JH, Carson PL. Deformable mapping using biomechanical models to relate corresponding lesions in digital breast tomosynthesis and automated breast ultrasound images. Med Image Anal 2020;60:101599.
• [7] Rana S, Hampson R, Dobie G. Breast cancer: model reconstruction and image registration from segmented deformed image using visual and force based analysis. IEEE Trans Med Imaging 2019;13:1.
• [8] Dukov N, Bliznakova K, Feradov F, Buliev I, Bosmans H, Mettivier G, Russo P, Cockmartin L, Bliznakov Z. Models of breast lesions based on three-dimensional X-ray breast images. Phys Med 2019;57:80–7.
• [9] Pianigiani S, Ruggiero L, Innocenti B. An anthropometric- based subject-specific finite element model of the human breast for predicting large deformations. Front Bioeng Biotechnol 2015;3:1–9.
• [10] de Groot JE, Hopman IG, van Lier MG, Branderhorst W, Grimbergen CA, den Heeten GJ. Pressure-standardised mammography does not affect visibility, contrast and sharpness of stable lesions. Eur J Radiol 2017;86:289–95.
• [11] Hipwell JH, Vavourakis V, Han L, Mertzanidou T, Eiben B, Hawkes DJ. A review of biomechanically informed breast image registration. Phys Med Biol 2016;61:R1–31.
• [12] Azar FS, Metaxas DN, Schnall MD. Methods for modelling predicting mechanical deformations of the breast under external perturbations. Med Image Anal 2002;6:1–27.
• [13] del Palomar A, Calvo B, Herrero J, L’øpez J, Doblaré M. A finite element model to accurately predict real deformations of the breast. Med Eng Phys 2008;30(9):1089–97.
• [14] Samani A, Zubovits J, Plewes D. Elastic moduli of normal and pathological human breast tissues: an inversion-technique-based investigation of 169 samples. Phys Med Biol 2007;52(6):1565–76.
• [15] Wellman PS, Howe RD, Dalton E, Kern KA. Breast tissue stiffness in compression is correlated to histological diagnosis. J Biomech 1999.
• [16] Griesenauer RH, Weis JA, Arlinghaus LR, Meszoely IM, Miga MI. Breast tissue stiffness estimation for surgical guidance using gravity-induced excitation. Phys Med Biol 2017;62:4756–76.
• [17] Visentin F, Groenhuis V, Maris B, Dall'Alba D, Siepel F, Stramigioli S, Fiorini P. Iterative simulations to estimate the elastic properties from a series of MRI images followed by MRI-US validation. Med Biol Eng Comput 2018;913–24.
• [18] Groenhuis V, Visentin F, Siepel FJ, Maris BM, Dall'alba D, Fiorini P, Stramigioli S. Analytical derivation of elasticity in breast phantoms for deformation tracking. Int J Comput Assist Radiol Surg 2018;13:1641–50.
• [19] Chen JH, Chan S, Zhang Y, Li S, Chang RF, Su MY. Evaluation of breast stiffness measured by ultrasound and breast density measured by MRI using a prone-supine deformation model. Biomarker Res 2019;7:1–10.
• [20] Insana MF, Liu J, Sridhar M, Pellot-Barakat C. Ultrasonic mechanical relaxation imaging and the material science of breast cancer. IEEE Ultrasonics Symposium; 2005.
• [21] Han L, Hipwell JH, Tanner C, Taylor Z, Mertzanidou T, Cardoso J, Ourselin S, Hawkes DJ. Development of patient-specific biomechanical models for predicting large breast deformation. Phys Med Biol 2012;57:455–72.
• [22] Tanner C, Schnabel J, Smith AC, Sonoda L, Hill D, Hawkes D, Degenhard A, Hayes C, Leach M, Hose D. The comparison of biomechanical breast models: initial results. ANSYS Convergence 2002.
• [23] Alirezazadeh P, Hejrati B, Monsef-Esfahani A, Fathi A. Representation learning-based unsupervised domain adaptation for classification of breast cancer histopathology images. Biocybern Biomed Eng 2018;38: 671–83.
• [24] Kriti, Virmani J, Agarwal R. Effect of despeckle filtering on classification of breast tumors using ultrasound images. Biocybern Biomed Eng 2019;39:536–60.
• [25] Martínez-Martínez F, Rupérez-Moreno MJ, Martínez-Sober M, Solves-Llorens JA, Lorente D, Serrano-López AJ, Martínez-Sanchis S, Monserrat C, Martín-Guerrero JD. A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time. Comput Biol Med 2017;90:116–24.
• [26] Gamage TPB, Malcolm DT, Talou GM, Mîra A, Doyle A, Nielsen PM, Nash MP. An automated computational biomechanics workflow for improving breast cancer diagnosis and treatment. Interface Focus 2019;9:1–12.
• [27] García E, Diez Y, Diaz O, Lladó X, Martí R, Martí J, Oliver A. A step-by-step review on patient-specific biomechanical finite element models for breast MRI to X-ray mammography registration. Med Phys 2018;45:e6–31.
• [28] Wessel C, Schnabel JA, Brady M. Towards a more realistic biomechanical modelling of breast malignant tumours. Phys Med Biol 2012;57(3):631–48.
• [29] Azar FS, Metaxas DN, Schnall MD. A finite element model of the breast for predicting mechanical deformations during biopsy procedures. Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, vol. 1; 2000. p. 38–45.
• [30] Eiben B, Vavourakis V, Hipwell JH, Kabus S, Buelow T, Lorenz C, Mertzanidou T, Reis S, Williams NR, Keshtgar M, Hawkes DJ. Symmetric biomechanically guided prone-to- supine breast image registration. Ann Biomed Eng 2016;44:154–73.
• [31] Pathmanathan P, Gavaghan DJ, Whiteley JP, Chapman SJ, Brady JM. Predicting tumor location by modeling the deformation of the breast. IEEE Trans Biomed Eng 2008;55 (10):2471–80.
• [32] Russo J, Russo IH. Techniques and methodological approaches in breast cancer research. Springer; 2014.
• [33] Lapuebla-Ferri A, Cegoñ ino-Banzo J, Jiménez-Mocholí AJ, Del Palomar AP. Towards an in-plane methodology to track breast lesions using mammograms and patient-specific finite-element simulations. Phys Med Biol 2017;62:8720–38.
• [34] Kattan PI. MATLAB guide to finite elements, an interactive approach. 2nd ed. Berlin, Heidelberg: Springer-Verlag; 2008.
• [35] Bastir M, Garcia Martinez D, Recheis W, Barash A, Coquerelle M, Rios L, Pena-Melian A, Garcia Rio F, O'Higgins P. Differential growth and development of the upper and lower human thorax. PLOS ONE 2013;8.
• [36] Han J, Kamber M. Data mining, concepts and techniques. 2nd ed. Morgan Kaufmann Publishers; 2006.
• [37] Danch-Wierzchowska M, Borys D, Bobek-Billewicz B, Jarzab M, Swierniak A. Simplification of breast deformation modelling to support breast cancer treatment planning. Biocybern Biomed Eng 2016;36(4):531–6.
• [38] García E, Diez Y, Diaz O, Lladó X, Gubern-Mérida A, Martí R, Martí J, Oliver A. Breast MRI and X-ray mammography registration using gradient values. Med Image Anal 2019;54:76–87.
• [39] Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 1999;18(8):712–21.
• [40] Zou Y, Liu PX. A high-resolution model for soft tissue deformation based on point primitives. Comput Methods Progr Biomed 2017;148:113–21.
• [41] Lee AWC, Schnabel JA, Rajagopal V, Nielsen PMF, Nash MP. Breast image registration by combining finite elements and free-form deformations. Digit Mammogr Lect Notes Comput Sci 2010;736–43.
• [42] Lapuebla-Ferri A, Cegoñino-Banzo J, Jiménez-Mocholí A-J, del Palomar AP. Towards an in-plane methodology to track breast lesions using mammograms and patient-specific finite-element simulations. Phys Med Biol 2017;62:8720.
• [43] Myung Y, Lee JG, Cho M, Heo CY. Finite element analysis of long-term changes of the breast after augmentation mammoplasty: implications for implant design. Arch Plast Surg 2019;46:386–9.
• [44] Na GY, Yang J, Cho S. Development of a 3D breast shape generation and deformation system for breast implant fabrication. J Mech Sci Technol 2019;33:1293–303.
• [45] Jahani N, Cohen E, Hsieh MK, Weinstein SP, Pantalone L, Hylton N, Newitt D, Davatzikos C, Kontos D. Prediction of treatment response to neoadjuvant chemotherapy for breast cancer via early changes in tumor heterogeneity captured by DCE-MRI registration. Sci Rep 2019;9:1–12.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).