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Proceedings Paper

Three-dimensional finite element model for lesion correspondence in breast imaging
Author(s): Yan Qiu; Lihua Li; Dmitry Goldgof; Sudeep Sarkar; Sorin Anton; Robert A. Clark
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Paper Abstract

Predicting breast tissue deformation is of great significance in several medical applications such as biopsy, diagnosis, and surgery. In breast surgery, surgeons are often concerned with a specific portion of the breast, e.g., tumor, which must be located accurately beforehand. Also clinically it is important for combining the information provided by images from several modalities or at different times, for the detection/diagnosis, treatment planning and guidance of interventions. Multi-modality imaging of the breast obtained by X-ray mammography, MRI is thought to be best achieved through some form of data fusion technique. However, images taken by these various techniques are often obtained under entirely different tissue configurations, compression, orientation or body position. In these cases some form of spatial transformation of image data from one geometry to another is required such that the tissues are represented in an equivalent configuration. We propose to use a 3D finite element model for lesion correspondence in breast imaging. The novelty of the approach lies in the following facts: (1) Finite element is the most accurate technique for modeling deformable objects such as breast. The physical soundness and mathematical rigor of finite element method ensure the accuracy and reliability of breast modeling that is essential for lesion correspondence. (2) When both MR and mammographic images are available, a subject-specific 3D breast model will be built from MRIs. If only mammography is available, a generic breast model will be used for two-view mammography reading. (3) Incremental contact simulation of breast compression allows accurate capture of breast deformation and ensures the quality of lesion correspondence. (4) Balance between efficiency and accuracy is achieved through adaptive meshing. We have done intensive research based on phantom and patient data.

Paper Details

Date Published: 12 May 2004
PDF: 8 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.535719
Show Author Affiliations
Yan Qiu, H. Lee Moffitt Cancer Ctr. and Research Institute, Univ. of South Florida (United States)
Lihua Li, H. Lee Moffitt Cancer Ctr. and Research Institute, Univ. of South Florida (United States)
Dmitry Goldgof, Univ. of South Florida (United States)
Sudeep Sarkar, Univ. of South Florida (United States)
Sorin Anton, Univ. of South Florida (United States)
Robert A. Clark, H. Lee Moffitt Cancer Ctr. and Research Institute, Univ. of South Florida (United States)


Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)

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