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 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.

MEDIP Platform independent software system for medical image processing Department of Information Technology, University of Debrecen PET Center, University of Debrecen Mediso Medical Imaging System Ltd. Developers 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 Test partners

MEDIP Platform independent software system for medical image processing finished sessions current session future sessions 1.Survey, problem specification 2.Modelling, system plans 3.Implementation 4.Implementation, optimisation 5.Fine tuning, testing, presentation Ses1 Ses2Ses3 Ses4 Ses5 Dependence Feedback Pert diagram

MEDIP – Demostration programs Platform independent software system for medical image processing Finite element modelling for virtual surgery Selection of volume of interest based on image fusion 4D visualization of gated heart and lung inspections

Dept. of Information Tech., UD Dept. of Orthopaedy, UD Demonstration program Finite element modeling for 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 Demonstration program Finite element modeling for virtual surgery

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

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

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

Lower extremity inequality Shorter femurShorter tibia

Handling the problem (I.)   Orthopaedic shoes

Handling the problem (II.)   Surgical intervention (after Wagner and Ilizarov) cutting distancingossification

! A new lengthening method  Torsion and angulation could also be correnced

Potencial instrumentation

 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 Past and present

Validating new ideas  Laboratory tests  Finite element analysis

How can we use FEM/FEA? CT slices image processing geometrical reconstruction

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

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

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

Modeling intervention geometry (II.)  We subtract the object representing the „removed tissue” from the femur model

Modeling intervention physics  The bone tissue should be modeled as a very complicated nonlinear anisotropic material  We are using linear elastic izotropic and orthotropic material 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?

Some results There exists an optimal combination of parameter values

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

 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 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 Checking the results

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

Thank you for your attention.