Applied Anatomical Diagnostic Framework for Visualizing the

Human Body in a 3-D, Immersive, Navigable and

Interactive VR Environment

May 4, 2012Steven Beaudoin, BS

George D. Lecakes Jr., MSTony Aita, BS

Lawrence Weisberg, MDMichael Goldberg, MD

Vijay Rajput, MDShreekanth Mandayam, PhD

Contributing Authors

Steven Beaudoin

George Lecakes, MS

Shreekanth Mandayam, PhD

Michael Goldberg, MD

Vijay Rajput, MD

Lawrence Weisberg, MD

Overview

• Background• Objectives• Approach• Results• Future Goals• Conclusions• Acknowledgements

-US Department of Education, 2010

“ 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”

Cooper Medical School

CAVE® at Medical School

• Basic anatomy

• Clinical correlation

• Systems based practice educational tool

• Simulation scenarios (simulated patients / conditions)

BackgroundThe Rowan University CAVE®

BackgroundThe Rowan University CAVE®

• 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

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

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

Data Preparation

Data Preprocessi

ng

Data Visualizatio

nReal-Time Processing

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)

Data Preparation

Data Preprocessi

ng

Data Visualizatio

nReal-Time Processing

Approach

Data Preparation

Data Preprocessi

ng

Data Visualizatio

nReal-Time Processing

Raw data

Processed Data

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 Data

PreparationData

Preprocessing

Data Visualizatio

nReal-Time Processing

Approach

Pixel Values

• Values from a CT scan represent different densities

• Radiolucent regions are low density – air

• Radiodense regions are higher density – bone

Data Preparation

Data Preprocessi

ng

Data Visualizatio

nReal-Time Processing

Approach

Data Manipulation and Interface

1. Grayscale histogram – Cull pixel values from the histogram using a range slider.

2. Pseudo-color histogram – Cull pixel values and color the ranges with red, green, or blue ranges.

3. Clip Plane Mode – Clip the polygonal planes with an OpenGL clipping plane in any orientation.

Xbox 360® Controller – All functionality mapped to buttons

Data Preparation

Data Preprocessi

ng

Data Visualizatio

nReal-Time Processing

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

Other uses of CAVE®

Work In Progress

Fuse multiple image data sets (MRI, CT, PET) to distinguish redundant and complementary information for clinically applied anatomical education

Work In Progress

Acknowledgments

We gratefully acknowledge the assistance of:

Dr. H. Warren Goldman, MD, PhDProfessor and Chair of Neurosurgery

Cooper Hospital, Camden, NJ

Mr. Anthony AitaNeurosurgery Department

Cooper Hospital, Camden, NJ

Approach

Approach