A nonrigid image registration framework for identification of tissue mechanical parameters

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5242 LNCS, Issue PART 2, 2008, Pages 930-938

  Jordan, Petr ^a,c   Socrate, Simona ^b   Zickler, Todd E ^a   Howe, Robert D ^a,c  

^a HARVARD UNIVERSITY   (United States)

^b USA   (United States)

^c HARVARD MIT DIVISION OF HEALTH SCIENCES AND TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER GRAPHICS; IMAGE REGISTRATION; MEDICAL COMPUTING; MEDICAL IMAGING; MESH GENERATION; MOTION ESTIMATION; POTENTIAL ENERGY; SPRINGS (COMPONENTS); TISSUE; ULTRASONIC APPLICATIONS;

IDENTIFICATION OF MATERIAL PARAMETERS; INDENTATION METHOD; MECHANICAL FINITE ELEMENT MODELING; MECHANICAL PARAMETERS; MECHANICAL RESPONSE; NONLINEAR VISCO-ELASTIC; NONRIGID IMAGE REGISTRATION; REGULARIZATION ENERGY;

PARAMETER ESTIMATION;

ALGORITHM; ANIMAL; ARTICLE; BIOLOGICAL MODEL; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; ECHOGRAPHY; ELASTICITY; ELASTOGRAPHY; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; IN VITRO STUDY; LIVER; MECHANICAL STRESS; METHODOLOGY; PHYSIOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SWINE;

ALGORITHMS; ANIMALS; COMPUTER SIMULATION; ELASTICITY; ELASTICITY IMAGING TECHNIQUES; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LIVER; MODELS, BIOLOGICAL; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; STRESS, MECHANICAL; SUBTRACTION TECHNIQUE; SWINE;

EID: 58849083715     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85990-1_112     Document Type: Conference Paper

References (12)

1. Ferrant, M., et al.: Registration of 3-D Intraop. MR Images of the Brain using a Finite-Element Biomech. Model. IEEE T Med. Imaging 20(12), 1384-1397 (2001)
2. Skrinjar, O., et al: Model-driven Brain Shift Compensation. Med. Image Anal. 6(4), 361-373 (2002)
3. Clatz, O., et al.: Robust Nonrigid Registration to Capture Brain Shift from Intraoperative MRI. IEEE T Med. Imaging 24(11), 1417-1427 (2005)
4. Cash, D.M., et al.: Concepts and Preliminary Data Toward the Realization of Image-guided Liver Surgery. J. Gastrointest Surg. 11(7), 844-859 (2007)
5. Papademetris, X., et al.: Estimation of 3-D Left Ventricular Deformation from Med. Images using Biomech. Models. IEEE T Med. Imaging 21(7), 786-800 (2002)
6. Sermesant, M., et al.: Deformable Biomechanical Models: Application to 4D Cardiac Image Analysis. Med. Image Anal. 7(4), 475-488 (2003)
7. Roose, L., et al.: Biomechanically Based Elastic Breast Registration using Mass Tensor Simulation. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 718-725. Springer, Heidelberg (2006)
8. Alterovitz, R., et al.: Registration of MR Prostate Images with Biomechanical Modeling and Nonlinear Parameter Estimation. Med. Phys. 33(2), 446-454 (2006)
9. Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: Proc. IJCAI, pp. 674-679 (1981)
10. Zienkiewicz, O.C.: The Finite Element Method. McGraw-Hill, New York (1977)
11. Jordan, P.: Image-Based Mechanical Characterization of Soft Tissue using Three Dimensional Ultrasound. Ph.D. Thesis, Harvard University (2008)
12. SIMULIA, Providence, RI: ABAQUS Theory Manual (2007)