Tomographic Image Reconstruction Using Content-Adaptive Mesh Modeling

1. Tomographic Image Reconstruction Using Content-Adaptive Mesh Modeling H. Can Aras Final Presentation December 20, 2004

2. Overview • What is the problem? • What has been done before the second presentation? • What has been done after the second presentation? Enhancements Alternative Approaches • Conclusion

3. CT-Imaging • A special radiographic technique that uses a computer to assimilate multiple X-ray images into a 2D cross-sectional image • Important for viewing the human body and diagnostic purposes. • Quality of CT-Images is important.

4. The Procedure • The CT machine rotates 180 degrees around the patients body, sending out an X-ray beam. • Sensors positioned at the opposite points of the beam record the absorption rates of the varying thicknesses of tissue and bone. • These data are then relayed to a computer that turns the information into a picture (CT-Image) on a screen.

5. Projection Data Sinogram:All projections Projection:All ray-sums in a direction  y P(t) t p  x f(x,y) t X-rays Sinogram

6. Problem & Aim • Reconstruction of CT-Images from Projection Data • There are many algorithms for reconstruction (ART, FBP) • FBP is the most well-known, but not perfect especially in case of noisy or incomplete data. • The aim is to get a better reconstruction of the CT image by using a mesh model instead of the traditional pixel-based models.

7. Overview • What is the problem? • What has been done before the second presentation? • What has been done after the second presentation? Enhancements Alternative Approaches • Conclusion

8. Approach • Content-Adaptive Mesh Generation Feature Map Extraction (Second derivative) Placement of Mesh Nodes (Floyd-Steinberg) Connecting Mesh Nodes (Delaunay) • Estimation of Mesh Nodal Values MESH-EM algorithm • Reconstruction of the Image Approximation over each mesh element (triangle)

9. Result

10. Result (cont.) • It is difficult to judge which result looks better. A physician can tell you. • Even different physicians can tell you different things. • SNR can be used. • Other validation techniques can be used, e.g. cardiac perfusion detection. This is out of the scope of this project.

11. Overview • What is the problem? • What has been done before the second presentation? • What has been done after the second presentation? Enhancements Alternative Approaches • Conclusion

12. Better Filter used for FBP (θ = 5)

13. More Projections (θ = 2)

14. Less Projections (θ = 10)

15. With Smoothing (θ = 10)

16. More Iterations (θ = 10)

17. Synthetic (θ = 10)

18. Brain (θ = 10)

19. Spine (θ = 10)

20. Overview • What is the problem? • What has been done before the second presentation? • What has been done after the second presentation? Enhancements Alternative Approaches • Conclusion

21. Approach • Content-Adaptive Mesh Generation Feature Map Extraction Placement of Mesh Nodes Connecting Mesh Nodes • Estimation of Mesh Nodal Values MESH-EM algorithm • Reconstruction of the Image Approximation over each mesh element

22. Barycentric Coordinates • Why not use Barycentric Coordinates instead of using a Master Element? • They are doing the same thing…

23. Approach • Content-Adaptive Mesh Generation Feature Map Extraction Placement of Mesh Nodes Connecting Mesh Nodes • Estimation of Mesh Nodal Values MESH-EM algorithm • Reconstruction of the Image Approximation over each mesh element

24. Using Gradient Image (θ = 5)

25. Comparison (θ = 5)

26. Using Watershed Segmentation-I (θ = 5)

27. Using Watershed Segmentation-II (θ = 5)

28. Using Watershed Segmentation-III (θ = 5)

29. Overview • What is the problem? • What has been done before the second presentation? • What has been done after the second presentation? Enhancements Alternative Approaches • Conclusion

30. Thank you… Questions?