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Medical image processing and finite element analysis

Medical image processing and finite element analysis. András Hajdu UNIVERSITY OF DEBRECEN HUNGARY. SSIP 2003 July 3-12 , 2003 Timişoara, Romania. MEDIP Platform independent software system for medical image processing.

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Medical image processing and finite element analysis

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  1. Medical image processing and finite element analysis András Hajdu UNIVERSITY OF DEBRECEN HUNGARY SSIP 2003 July 3-12, 2003 Timişoara, Romania

  2. MEDIPPlatformindependent software system for medical imageprocessing The aim of the project is to develop an informatical background to theoretical and applied studies in the field of multi-modal medical image processing,which results may lead to marketable products.

  3. Developers Test partners • Department of Information Technology,University of Debrecen • PET Center, University of Debrecen • Mediso Medical Imaging System Ltd. • Department of Orthopedic Surgery,University of Debrecen • Faculty of Health Sciences, Chair of Radiotherapy,Semmelweis University • Faculty of Medicine Dept. of Radiology and Oncotherapy,Semmelweis University MEDIPPlatformindependent software system for medical imageprocessing

  4. MEDIPPlatformindependent software system for medical imageprocessing 01.2003 01.2002 01.2004 • Survey, problem specification • Modelling, system plans • Implementation • Implementation,optimisation • Fine tuning,testing, presentation Pert diagram Dependence finished sessions current session Ses1 Ses2 Ses3 Ses4 Ses5 future sessions Feedback

  5. MEDIP – Demostration programsPlatformindependent software system for medical imageprocessing Finite element modelling for virtual surgery Selection of volume of interest based on image fusion 4D visualization of gated heart and lung inspections

  6. Demonstration program Finite element modelingfor virtual surgery Dept. of Information Tech., UD Dept. of Orthopaedy, UD

  7. Demonstration program Finite element modelingfor virtual surgery Connecting to the base libraries • File I/O (DICOM) • Segmentation techniques • Contour tracking, ROI selection • Morphological operations • Complex GUI • ROI and VOI 2D/3D visualization • 3D geometric navigation • Printing

  8. Demonstration program FEM surgery planning frame program Login (database opening) Launching (opening new/existing profile) DICOM file import Image manipulation (morphological filtering) Segmentation (automatic/manual) Creating geometric model

  9. Demonstration program Surgery planning (virtual osteotomy) Adjusting parameters FEM contact 3D visualization, selecting VOI

  10. Surgery planning (virtual osteotomy)Case study ANALYZIS OF A NEW FEMUR LEGTHENING SURGERY Zoltán Csernátony, Department of Orthopaedics, UD Szabolcs Molnár, Department of Orthopaedics, UD Sándor Manó, College Faculty of Engineering, UD AndrásHajdu, Institute of Informatics, UD ZoltánZörgő, Institute of Informatics, UD

  11. Lowerextremityinequality Shorter femur Shorter tibia

  12. Orthopaedic shoes Handling the problem (I.)

  13. Surgical intervention(after Wagner and Ilizarov) distancing ossification Handling the problem (II.) cutting

  14. A new lengthening method ! • Torsion and angulation could also be correnced

  15. Potencial instrumentation

  16. Past and present • In the past there existed no way of testing new interventions but to try it out in vivo • This days technology makes is possible to test and adjust new operative interventions before even one cut is made

  17. Validating new ideas • Laboratory tests • Finite element analysis

  18. How can we use FEM/FEA? CT slices imageprocessing geometricalreconstruction

  19. Importing images in CT firmware format (DICOM) Image enhancement (sharpening, filtering) Extracting ROIs Building up a basic model

  20. Applying contour splines (Euclidean geometry) Reconstructing solid model (Coons patches) Building up a basic model

  21. Based on the path of the cutting tool We need to determine: Cutting thickness Pitch Ending hole parameters Modeling intervention geometry (I.)

  22. We subtract the object representing the „removed tissue” from the femur model Modeling interventiongeometry (II.)

  23. The bone tissue should be modeled as a very complicated nonlinear anisotropic material We are using linearelasticizotropicandorthotropicmaterial models instead We mesh the model One end is fixed, on the other end a traction force is applied How big is the evolved stress? How much elongation can it support? Modeling intervention physics

  24. Some results There exists an optimal combination of parameter values

  25. Additional adjustments • The greatest stress values evolve near the ending holes. • The inward oriented conical bore appears to be the most suitable

  26. We have built a schematic model It is a cylindrical pipe with inner and outer diameters equaling the femur’s average diameters The same analysis were performed Checking the results • The stress values measured on the pipe-model were 1.61 (D=0.27) times lower under same conditions • There was a 91% correlation between the two datasets

  27. Future plans • More precise material modeling by using cylindrical layers, and other element types • In-vitro lab tests based on the results

  28. Thank you for your attention.

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