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Visual Optimality And Stability Analysis Of 3DCT Scan Positions

Visual Optimality And Stability Analysis Of 3DCT Scan Positions. Artem Amirkhanov 1,2 Michael Reiter 2 Christoph Heinzl 2 M. Eduard Gröller 1. 1 Institute of Computer Graphics and Algorithms Vienna University of Technology. 2 Upper Austrian University of Applied Sciences

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Visual Optimality And Stability Analysis Of 3DCT Scan Positions

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  1. Visual Optimality And Stability Analysis Of 3DCT Scan Positions Artem Amirkhanov 1,2 Michael Reiter 2 Christoph Heinzl 2 M. Eduard Gröller 1 1 Institute of Computer Graphicsand Algorithms Vienna University of Technology 2 Upper Austrian University of Applied Sciences Wels Campus, Austria

  2. Scanning Geometry Detector Specimen X-ray source Rotary plate Artem Amirkhanov

  3. Specimen Placement • Placement is crucial for • Measurements accuracy • Iso-surface quality • Requires knowledge, intuitive • Good placement is hard to find • For a complex specimen • For measurement features • Doing several scans is expensive Artem Amirkhanov

  4. CAD model is available Quality control – from manufacturer Reverse engineering – preliminary scan Use a CAD model to estimate a good placement Visual analysis tool is required for Simulation-based preview Optimal placement estimation Motivation Tool Optimal Placement 3 Artem Amirkhanov

  5. Why Placement is Important? Beam-hardening artifacts Cupping artifacts Streaks Bad planar faces artifacts Blurring 4 Artem Amirkhanov

  6. Good/Bad Placement Example Bad: Good: 5 Artem Amirkhanov

  7. Criteria and Methods • Shortest penetration lengths Method:ray casting • Smallest bad planar faces area Method:Radon-space analysis • Stable within a certain range of reliability Method:stability widget Artem Amirkhanov

  8. Ray casting • Radon-space analysis Simulation • Stability analysis • Data exploration and analysis Visual analysis Workflow CAD model Parameter spaces Optimal placement Artem Amirkhanov

  9. Simulation • Set of candidate placements • Placement is defined by the orientation • Orientation is defined by two Euler angles α and β β α Artem Amirkhanov

  10. Ray Casting Simulation • Reflects the scanning setup • Set of projections for every placement • Parameters • Maximum penetration length • Average penetration length Artem Amirkhanov

  11. Radon-Space Analysis • Supporting planes → points in Radon space • Finding bad points in Radon space • Parameter • Bad faces area percentage Artem Amirkhanov

  12. Parameters Representation Parameter value: Best Worst Artem Amirkhanov

  13. Stability Widget Parameter value: Better Same Worse Artem Amirkhanov

  14. Data Visualization and Exploration • Penetration-length histograms • Visualizing ray subsets. • Color coding bad faces. Artem Amirkhanov

  15. Evaluation Using Dimensional Measurements • Two test-part specimens • Measurement features • Radius • Distance • CALYPSO used to estimate measurement quality Artem Amirkhanov

  16. Evaluation Using Variance Comparison Complex real world specimen Best and worst placements Variance comparison of the iso-surfaces with the CAD model Artem Amirkhanov

  17. Evaluation Bad Placement Good Placement Position deviation spectrum in [mm] Position deviation spectrum in [mm] 2 1 -1 -2 0 2 1 -1 -2 0 Artem Amirkhanov

  18. Limitations and Future Work • Orientation only • Detect orientation and position in sequence • Still requires user interaction • Combining parameters Artem Amirkhanov

  19. Conclusions Thank you! VS Contact: artem.ogre@gmail.com Artem Amirkhanov

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