» Articles » PMID: 17551761

A Novel Simulation Algorithm for Soft Tissue Compression

Overview
Publisher Springer
Date 2007 Jun 7
PMID 17551761
Citations 11
Authors
Affiliations
Soon will be listed here.
Abstract

This paper presents a novel general approach to simulation of soft tissue compression. A theoretical framework of the compression algorithm has been developed and implemented, based on the concept of a simple spring. The volume subjected to compression is divided into a number of "model elements", each one consisting of 27 nodes. The nodes are connected with springs. The mechanical properties of the tissues are assumed to be linear and isotropic. The compressed volume remains constant due to the introduced concept of spring variable equilibrium lengths. Initial settings for compression simulation are introduced in order that the algorithm converges faster. The developed compression algorithm was used to model breast compression during a standard mammography examination. Specifically, the method was applied to a high-resolution three-dimensional software breast phantom, composed to have a medium glandularity and calcification abnormalities. The compression was set to 50%. Results showed that the abnormalities maintain their shape and dimensions during the compression, while the surrounding breast tissues undergo considerable deformation and displacement. A "decompression" algorithm was also applied to test the reversibility of the model.

Citing Articles

Internal breast dosimetry in mammography: Monte Carlo validation in homogeneous and anthropomorphic breast phantoms with a clinical mammography system.

Fedon C, Caballo M, Sechopoulos I Med Phys. 2018; .

PMID: 29956334 PMC: 6099211. DOI: 10.1002/mp.13069.


A Monte Carlo model for mean glandular dose evaluation in spot compression mammography.

Sarno A, Dance D, van Engen R, Young K, Russo P, Di Lillo F Med Phys. 2017; 44(7):3848-3860.

PMID: 28500759 PMC: 5534220. DOI: 10.1002/mp.12339.


Evaluation of the BreastSimulator software platform for breast tomography.

Mettivier G, Bliznakova K, Sechopoulos I, Boone J, Di Lillo F, Sarno A Phys Med Biol. 2017; 62(16):6446-6466.

PMID: 28398906 PMC: 5753580. DOI: 10.1088/1361-6560/aa6ca3.


Dosimetry in x-ray-based breast imaging.

Dance D, Sechopoulos I Phys Med Biol. 2016; 61(19):R271-R304.

PMID: 27617767 PMC: 5061150. DOI: 10.1088/0031-9155/61/19/R271.


Breast tomosynthesis with monochromatic beams: a feasibility study using Monte Carlo simulations.

Malliori A, Bliznakova K, Sechopoulos I, Kamarianakis Z, Fei B, Pallikarakis N Phys Med Biol. 2014; 59(16):4681-96.

PMID: 25082791 PMC: 4164851. DOI: 10.1088/0031-9155/59/16/4681.


References
1.
Bliznakova K, Kolitsi Z, Pallikarakis N . Dual-energy mammography: simulation studies. Phys Med Biol. 2006; 51(18):4497-515. DOI: 10.1088/0031-9155/51/18/004. View

2.
Azar F, Metaxas D, Schnall M . Methods for modeling and predicting mechanical deformations of the breast under external perturbations. Med Image Anal. 2002; 6(1):1-27. DOI: 10.1016/s1361-8415(01)00053-6. View

3.
Baumann R, Glauser D, Tappy D, Baur C, Clavel R . Force feedback for virtual reality based minimally invasive surgery simulator. Stud Health Technol Inform. 1995; 29:564-79. View

4.
Azar F, Metaxas D, SCHNALL M . A deformable finite element model of the breast for predicting mechanical deformations under external perturbations. Acad Radiol. 2001; 8(10):965-75. DOI: 10.1016/S1076-6332(03)80640-2. View

5.
Bliznakova K, Bliznakov Z, Bravou V, Kolitsi Z, Pallikarakis N . A three-dimensional breast software phantom for mammography simulation. Phys Med Biol. 2003; 48(22):3699-719. DOI: 10.1088/0031-9155/48/22/006. View