Full Body Virtual Autopsies using a State-of-the-art Volume Rendering Pipeline

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Full Body Virtual Autopsiesusing a
State-of-the-artVolume Rendering Pipeline

• Patric Ljung NVIS/VITA, LiU
• Calle Winskog RMV
• Anders Persson CMIV, LiU
• Claes Lundström CMIV, LiU Sectra Imtec AB
• Anders Ynnerman NVIS/VITA, LiU

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Forensic Autopsy Workflow
CT Scan
Crime Scene Investigation
Police
Radiology
DVR Visualization
Physical Autopsy
Forensics
Visual Exploration
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Siemens Definition CT Scanner
Scan length 2 m 10 GB under a minute
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Challenge of Virtual Autopsies

• Gigabytes of data do not fit in memory
• Full body and high resolution details
• Interaction and run-time TF changes

5 GB data set (512x512x3396), Data reduction 301
(170 MB)LOD distribution 177709, 19, 15487,
20292, 4605 (total 218112 blocks)
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DVR Pipeline
Out-of-Core Data Management
TF
Large Data Sets
Significant Data Reduction
STORAGE
LOADER
ANALYSER
RENDERER
Interactive
High Image Quality
LOD
Dual TF Raycasting
Flat Volume Blocking
Interblock Interpolation
TF-based LOD
Adaptive Volume Sampling
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Multiresolution Volumes

• Hierarchical blocking
• Constant data size
• Lower resolution blocks cover increasing spatial
  size

• Flat blocking structure
• Constant spatial size
• Lower resolution blocks have fewer samples

Level 0
Level 0
Level 1
Level 1
Level 2
Level 3
Level 2
Level 3
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Hierarchical vs Flat Blocking
Hierarchical blocking
Flat blocking
Final composition
Final composition
Data reduction 2.21
Data reduction 3.81
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TF Based Level-of-Detail

• Block resolution derived by TF content
• Content of a block in the current TF domain
• Empty All voxels are transparent
• Homogeneous Non-transparent voxels, similar
  color
• Varying Varying transparency, varying color
• Block significance based on visual error
• Color difference from CIE Luv, DE
• Perceptually uniform
• Optimize LOD to minimize visual error for a given
  memory limit

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TF Based LOD Data Reduction
CIE Luv DE error
Data Reduction
6411.6 (2.25 MB)
20010.5 (0.72 MB)
40010.25 (0.36 MB)
6411.6 (2.25 MB)
8112.5 (18 MB)
144 MB, 512x512x384
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Multiresolution Interblock Interpolation
Situation A blocked multiresolution
representation
Problem The domain between block sample
boundaries
Required Smooth interpolation of samples between
blocks
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Multiresolution Interblock Interpolation
Data reduction 801 (1.25 of original data size)
Without Interblock Interpolation
With Interblock Interpolation
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Adaptive Volume Sampling
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Adaptive Volume Sampling
1024x1024
Adaptive density ATI 14.1 FPS NV 6.0
FPS Speed-up 2.6-2.8
Full density ATI 5.1 FPS NV 2.3 FPS
- ATI X1800 XT 512MB - NV GF7800 GTX 256MB
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Dual TF Rendering
1024x1024
9.2 / 2.0 FPS
7.5 / 1.8 FPS
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Out-of-Core Data Management

• Disk performance
• Data transfer rates, about 60 MB/s
• Access density performance is still poor!!!
• Random access of many small blocks is bad
• Group of blocks GOBs
• Large pages 24 192 kb
• Spatial coherence
• Minimizes disk read requests
• Uncompressed storage
• Precomputed gradients

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Out-of-Core Data Management
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Man Killed by Police Officer

• Man acts aggressively when police arrives
• Police officer fires gun, man is dead on arrival
• Police officer accused for not warning
• Warning shot ricocheted as indicated by bullet
  fragments

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Routine Procedure Gone Wrong

• Routine procedure to control respiration for
  surgery
• A needle was inserted to remove air from the lung
  cavity (sack)
• Accidentally the heart stopped
• Physical autopsy fails to explain the cause of
  death
• Virtual autopsy reveals air in unexpected parts
  of the body

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Conclusions

• The Virtual Autopsy procedure
• Enables digital exploration of human cadavers
• Is already an important forensic tool
• May in some cases replace traditional autopsies
• Honors religious boundaries and demands from
  relatives
• Full body volume rendering on commodity PCs
• Graceful adaptation to available resources
• High quality LOD and interactivity at high
  resolutions
• Preserving full quality of volume data

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Thank You
5 GB data set, Data reduction 301 (170 MB),
Image resolution 1920x560
Efficient Methods for Direct Volume Rendering of
Large Data Sets PhD Thesis, P. Ljung, 2006
(Google Patric Ljung) http//www.itn.liu.se/plg/p
hdthesis/diss.pdf