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May 4, 2012 Steven Beaudoin , BS George D. Lecakes Jr., MS Tony Aita , BS

Applied Anatomical Diagnostic Framework for Visualizing the Human Body in a 3-D, Immersive, Navigable and Interactive VR Environment. May 4, 2012 Steven Beaudoin , BS George D. Lecakes Jr., MS Tony Aita , BS Lawrence Weisberg, MD Michael Goldberg, MD Vijay Rajput, MD

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May 4, 2012 Steven Beaudoin , BS George D. Lecakes Jr., MS Tony Aita , BS

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  1. Applied Anatomical Diagnostic Framework for Visualizing the Human Body in a 3-D, Immersive, Navigable and Interactive VR Environment May 4, 2012 Steven Beaudoin, BS George D. Lecakes Jr., MS Tony Aita, BS Lawrence Weisberg, MD Michael Goldberg, MD Vijay Rajput, MD ShreekanthMandayam, PhD

  2. Contributing Authors Steven Beaudoin Michael Goldberg, MD Vijay Rajput, MD George Lecakes, MS ShreekanthMandayam, PhD Lawrence Weisberg, MD

  3. Overview • Background • Objectives • Approach • Results • Future Goals • Conclusions • Acknowledgements

  4. Cooper Medical School “ To consider integration of 21st century competencies, expertise, such as critical thinking, complex problem solving, collaboration, multimedia communication, and technological competencies demonstrated by professional disciplines” -US Department of Education, 2010

  5. CAVE® at Medical School • Basic anatomy • Clinical correlation • Systems based practice educational tool • Simulation scenarios (simulated patients / conditions)

  6. Background The Rowan University CAVE®

  7. Background • 7.5'h x 10'w x 10'd tracked virtual environment covering 3-walls and a floor • 4- active stereo DLP projectors, 3,000 ANSI lumens, 1400x1050 resolution, with short-throw zoom lens • CAVE® structure fabricated with extruded & powdercoated 80/20 aluminum, • 8-camera WorldViz IR position tracking system • CrystalEyes® active shutter glasses • 6 - PC computer cluster • WorldViz Vizard, Autodesk 3D Studio Max / Maya The Rowan University CAVE®

  8. Objectives • Provide intuitive correlation of clinical information • Utilize a CAVE® virtual reality (VR) environment to enhance viewing of data sets • Ask “what if?” questions and simulate multiple scenarios for pathological/anatomical diagnosis • Utilize multiple diagnostic imaging procedures for prognostic capability

  9. Approach • Data preparation • CT- dataset of volunteer torso from the skull to the pelvis • Consisted of 909 images at 1mm slices • Provided in DICOM data format with each image having a different window center and window width

  10. Approach • Data preprocessing • Utilize image processing tool (MATLAB) to convert images into a standard window center and window width • Converted the various ranges into standard grayscale range (0-255) • 0 – Black • 255 – White • Export images to CAVE compatible file format (tga,png,jpeg)

  11. Approach Raw data Processed Data

  12. Approach • Data Visualization • Load images (textures) sequentially • Textures are applied to a series of evenly spaced polygonal planes • Each plane represents one cross-sectional slice of patient data • Images are given transparency • Images are rendered back to front

  13. Approach • Pixel Values • Values from a CT scan represent different densities • Radiolucent regions are low density – air • Radiodense regions are higher density – bone

  14. Approach • Data Manipulation and Interface • Grayscale histogram – Cull pixel values from the histogram using a range slider. • Pseudo-color histogram – Cull pixel values and color the ranges with red, green, or blue ranges. • Clip Plane Mode – Clip the polygonal planes with an OpenGL clipping plane in any orientation. • Xbox 360® Controller – All functionality mapped to buttons

  15. Educational / Future Medical Applications • Learn anatomy / clinically applied anatomy in 3D virtual reality • Anatomic pathology and radiological integration • Correlation of surface and organ anatomy for surgical procedure • Application of 3D virtual reality to understand system based practice – e.g. Assess Home environment for safety for geriatric patient

  16. Other uses of CAVE®

  17. Work In Progress • Fuse multiple image data sets (MRI, CT, PET) to distinguish redundant and complementary information for clinically applied anatomical education

  18. Work In Progress

  19. Acknowledgments • We gratefully acknowledge the assistance of: • Dr. H. Warren Goldman, MD, PhD • Professor and Chair of Neurosurgery • Cooper Hospital, Camden, NJ • Mr. Anthony Aita • Neurosurgery Department • Cooper Hospital, Camden, NJ

  20. Approach

  21. Approach

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