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Volume Flow Determination in the Cranial Vessel Tree Based on Quantitative Magnetic Resonance Data

Volume Flow Determination in the Cranial Vessel Tree Based on Quantitative Magnetic Resonance Data. by Jürgen Sotke. Advisor:. Prof. Dr. Navab. Supervisor (TUM):. Andreas Keil. Supervisors (BrainLAB):. Thomas Seiler,. Fritz Vollmer. Agenda. Goal

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Volume Flow Determination in the Cranial Vessel Tree Based on Quantitative Magnetic Resonance Data

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  1. Volume Flow Determination in the Cranial Vessel Tree Based on Quantitative Magnetic Resonance Data by Jürgen Sotke Advisor: Prof. Dr. Navab Supervisor (TUM): Andreas Keil Supervisors (BrainLAB): Thomas Seiler, Fritz Vollmer

  2. Agenda • Goal • Quantitative Magnetic Resonance Angiography (QMRA) • State of the Art • New Approach • Results

  3. Goal

  4. Diagnosis (stenosis, ischemia) • Review of operation results Quantitative information about volume flow rates (either abstract or graphicaly) Purposes: Goal

  5. QMRA

  6. So far there exists only one MR technique which allows to directly measure flow velocities: phase contrast MR In a phase contrast image, the grey level is linearly dependent to the velocity of the blood. Phase Contrast Image: bright = high velocities in the direction of the scan dark = high velociteis in the opposite direction QMRA

  7. Two undesired effects: 1. Limited velocity range 2. Works only for blood flow in one given direction Lotz J., Meir C., Leppert A. et al.: “Cardiovascular Flow Meaurement with Phase-Contrast MR Imaging: Basic Facts and Implementation”, RSNA, 2002 QMRA

  8. State of the Art or... State of the Art

  9. Visite http://www.vassolinc.com/QuickTourNOVA.cfm or http://www.youtube.com/watch?v=a7rBJWhCkF8&feature=related for a video about the current use of QMRA. State of the Art

  10. Pre-planed slices • inefficient workflow • requires registration • only flow information for a few samples http://www.vassolinc.com State of the Art

  11. Agenda • Goal • QMRA • State of the Art • The New Approach • Results

  12. Combining an abstract model of the vessel tree with flow information. New Approach

  13. Agenda • Goal • QMRA • State of the Art • The New Approach • Results • Data Acquisition • Segmentation • Creation of an Abstract Model of the Vessel Tree • Adding Flow Information to the Abstract Tree • Improving Flow Information by the Use of Topological Information

  14. Data Acquisition New Approach/Data Acquisition

  15. Only one session with PCA scans in at least three orientations over the whole volume. New Approach/Data Acquisition

  16. Each plane consist of a set of PCA slices depicting flow during different intervals of the (ECG-triggered) heart beat cycle. Because of pulsatile fluctuations, some kind of averaging over the heart beat is necessary: New Approach/Data Acquisition

  17. Agenda • Goal • QMRA • State of the Art • The New Approach • Results • Data Acquisition • Segmentation • Creation of an Abstract Model of the Vessel Tree • Adding Flow Information to the Abstract Tree • Improving Flow Information by the Use of Topological Information New Approach/Segmentation

  18. Segmentation directly from the phase contrast data requires combining the three orthogonal scans due to the directional sensitivity of phase contrast MR. Phase contrast images only depict vessels which run roughly parallel to the scan direction New Approach/Segmentation

  19. Three major segmentation steps • Combining PC-images • Region Growing • Closing Eiho, Sekiguchi, S.H., Sugimoto, N. et al.: “Branch-Based Region Growing Method For Blood Vessel Segmentation”, Systems and Computers in Japan, 2005 New Approach/Segmentation

  20. Segmentation Result New Approach/Segmentation

  21. Agenda • Goal • QMRA • State of the Art • The New Approach • Results • Data Acquisition • Segmentation • Creation of an Abstract Model of the Vessel Tree • Adding Flow Information to the Abstract Tree • Improving Flow Information by the Use of Topological Information

  22. Creation of an Abstract Model of the Vessel Tree Segmentation result Topological Model New Approach/Abstract Tree Model

  23. { Centerline { Skeleton { 3.1) Topological Structure of the Vessel Tree New Approach/Abstract Tree Model

  24. 3.1) Topological Structure of the Vessel Tree => Centerline-Extraction Two common techniques: • Distance based approaches • Thinning New Approach/Abstract Tree Model/Topological Structure

  25. Distance Based Centerline Extraction Distance-Transform-Map 2D-object 0 minimal distance of the pixel to the object‘s bounds 1 2 In the case of symmetrical 2D-objects the maxima of the DTM already pose the centerline pixels. New Approach/Abstract Tree Model/Topological Structure/Distance Maps

  26. Distance Based Centerline Extraction In 3D only radially symmetrical objects pose such distinct maxima of the distance map. Not radially symmetrical objects possess multiple local maxima in their distance maps, which cannot be connected in a well defined way. New Approach/Abstract Tree Model/Topological Structure/Distance Maps

  27. Multiple Maxima in the DTM New Approach/Abstract Tree Model/Topological Structure/Distance Maps

  28. Multiple Maxima in the DTM New Approach/Abstract Tree Model/Topological Structure/Distance Maps

  29. Multiple Maxima in the DTM …can be avoided by filtering the DTM New Approach/Abstract Tree Model/Topological Structure/Distance Maps

  30. Multiple Maxima in the DTM …can be avoided by filtering the DTM, but this causes a loss of connectivity in thin vessel segments. New Approach/Abstract Tree Model/Topological Structure/Distance Maps

  31. Thinning … works by removing the voxels at the object bounds… … layer… … by layer… …until the remaining object poses only a thickness of one voxel.

  32. Thinning Result Lamy, J.: “Integrating digital topology in image-processing libraries”, Elsevier Ireland Ltd, 2005 New Approach/Abstract Tree Model/Topological Structure/Thinning

  33. Centerline Extraction Distance based approach Thinning + correctness + high connectivity - bad connectivity - faulty => combined approach using centerline voxels from thinning to connect local maxima from distance transform New Approach/Abstract Tree Model/Topological Structure

  34. Centerline Extraction: combined approach New Approach/Abstract Tree Model/Topological Structure/Combined Approach

  35. α α 1 2 3.2) Assignment of Volumetric Information to the Abstract Model A voxel in the vicinity of a centerline segment is added to the assigned volume, if the two intersection angles in the image are smaller then 90°. New Approach/Abstract Tree Model/Assignment of Volumetric Information

  36. Agenda • Goal • A little bit of MR-Physics • State of the Art • The New Approach • Results • Data Acquisition • Segmentation • Creation of an Abstract Model of the Vessel Tree • Adding Flow Information to the Abstract Tree • Improving Flow Information by the Use of Topological Information

  37. Since the three phase contrast scans are orthogonal, they can be considered as the three components of a velocity vector. New Approach/Abstract Tree Model/Adding Flow Information

  38. Flow Velocities in all Vessel Segments Total flow velocities in the vessel tree. New Approach/Abstract Tree Model/Adding Flow Information

  39. Intersection area The knowledge of length and volume of all segments of the abstract vessel tree allows to compute the average intersection angle in all of these segments. New Approach/Abstract Tree Model/Adding Flow Information

  40. Flow Rates in all Vessel Segments With knowledge of the intersection areas, flow rates can be computed for all segments of the abstract tree. New Approach/Abstract Tree Model/Adding Flow Information

  41. Agenda • Goal • A little bit of MR-Physics • State of the Art • The New Approach • Results • Data Acquisition • Segmentation • Creation of an Abstract Model of the Vessel Tree • Adding Flow Information to the Abstract Tree • Improving Flow Information by the Use of Topological Information

  42. Angular information allows • selecting the most suitable PC-slice Angle between sagittal plane and vessel segment 3 Angle between coronal plane and vessel segment 3 • or generating an intelligently weighted combination of the data from different slices in order to improve flow information. New Approach/Improving Flow Information

  43. 109 448 448 + - - 232 232 216 ? + - 181 - ? 51 51 + ? 98 - - + 109 - ? 72 - 48 48 + 50 50 - - 42 42 ? 30 Substitution of Unreliable Data in the Abstract Vessel Tree New Approach/Improving Flow Information

  44. Summary QMRA poses technical limitations which so far compelled an inefficient workflow requiring patient or image registration and supplying only flow information for a few selected slices. The new approach might allow to acquire the necessary data in a one-step workflow without the need for patient or image registration that supplies flow information for all parts of the vessel tree with an accuracy (nearly) equal to that of pre-planed slices.

  45. Results

  46. Segmentation from PC-Data possible? yes depends… advisable? future work? mutual improvement: • QMRA-Software would allow to detect and correct segmentation faults • Improved segmentation would lead to improved abstract model. Results

  47. Visualizing Flow possible? yes future work? visualizing in the abstract tree Results

  48. Substituting “unreliable” data in the vessel tree possible? not proved future work? would require better data Results

  49. Finding a corrective factor/function possible? not proved future work? would require more data Results

  50. Appendix

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