Literature DB >> 18838373

Predicting tumor location by modeling the deformation of the breast.

Pras Pathmanathan1, David J Gavaghan, Jonathan P Whiteley, S Jonathan Chapman, J Michael Brady.   

Abstract

Breast cancer is one of the biggest killers in the western world, and early diagnosis is essential for improved prognosis. The shape of the breast varies hugely between the scenarios of magnetic resonance (MR) imaging (patient lies prone, breast hanging down under gravity), X-ray mammography (breast strongly compressed) and ultrasound or biopsy/surgery (patient lies supine), rendering image fusion an extremely difficult task. This paper is concerned with the use of the finite-element method and nonlinear elasticity to build a 3-D, patient-specific, anatomically accurate model of the breast. The model is constructed from MR images and can be deformed to simulate breast shape and predict tumor location during mammography or biopsy/surgery. Two extensions of the standard elasticity problem need to be solved: an inverse elasticity problem (arising from the fact that only a deformed, stressed, state is known initially), and the contact problem of modeling compression. The model is used for craniocaudal mediolateral oblique mammographic image matching, and a number of numerical experiments are performed.

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Year:  2008        PMID: 18838373     DOI: 10.1109/TBME.2008.925714

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  14 in total

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Authors:  Aristeidis Sotiras; Christos Davatzikos; Nikos Paragios
Journal:  IEEE Trans Med Imaging       Date:  2013-05-31       Impact factor: 10.048

Review 2.  Current progress in patient-specific modeling.

Authors:  Maxwell Lewis Neal; Roy Kerckhoffs
Journal:  Brief Bioinform       Date:  2009-12-02       Impact factor: 11.622

3.  Methodology based on genetic heuristics for in-vivo characterizing the patient-specific biomechanical behavior of the breast tissues.

Authors:  M A Lago; M J Rúperez; F Martínez-Martínez; S Martínez-Sanchis; P R Bakic; C Monserrat
Journal:  Expert Syst Appl       Date:  2015-11-30       Impact factor: 6.954

4.  Optimizing Design With Extensive Simulation Data: A Case Study of Designing a Vacuum-Assisted Biopsy Tool.

Authors:  Chi-Lun Lin; Dane Coffey; Daniel Keefe; Arthur Erdman
Journal:  J Med Device       Date:  2018-05-04       Impact factor: 0.582

5.  FEM-based 3-D tumor growth prediction for kidney tumor.

Authors:  Xinjian Chen; Ronald Summers; Jianhua Yao
Journal:  IEEE Trans Biomed Eng       Date:  2011-03       Impact factor: 4.538

Review 6.  Integrative physical oncology.

Authors:  Haralampos Hatzikirou; Arnaud Chauviere; Amy L Bauer; André Leier; Michael T Lewis; Paul Macklin; Tatiana T Marquez-Lago; Elaine L Bearer; Vittorio Cristini
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2011-08-18

7.  Validation of a method for measuring the volumetric breast density from digital mammograms.

Authors:  O Alonzo-Proulx; N Packard; J M Boone; A Al-Mayah; K K Brock; S Z Shen; M J Yaffe
Journal:  Phys Med Biol       Date:  2010-05-12       Impact factor: 3.609

8.  Mathematical Oncology: How Are the Mathematical and Physical Sciences Contributing to the War on Breast Cancer?

Authors:  Arnaud H Chauviere; Haralampos Hatzikirou; John S Lowengrub; Hermann B Frieboes; Alastair M Thompson; Vittorio Cristini
Journal:  Curr Breast Cancer Rep       Date:  2010-07-22

9.  Kidney tumor growth prediction by coupling reaction-diffusion and biomechanical model.

Authors:  Xinjian Chen; Ronald M Summers; Jianhua Yao
Journal:  IEEE Trans Biomed Eng       Date:  2012-10-02       Impact factor: 4.538

10.  Symmetric Biomechanically Guided Prone-to-Supine Breast Image Registration.

Authors:  Björn Eiben; Vasileios Vavourakis; John H Hipwell; Sven Kabus; Thomas Buelow; Cristian Lorenz; Thomy Mertzanidou; Sara Reis; Norman R Williams; Mohammed Keshtgar; David J Hawkes
Journal:  Ann Biomed Eng       Date:  2015-11-17       Impact factor: 3.934

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