Evaluation of a Bricked Volume Layout for a Medical Workstation based on Java Peter Kohlmann, Stefan...
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Transcript of Evaluation of a Bricked Volume Layout for a Medical Workstation based on Java Peter Kohlmann, Stefan...
Evaluation of a Bricked Volume Layout for a Medical
Workstation based on Java Peter Kohlmann, Stefan Bruckner, Armin Kanitsar, M. Eduard Gröller
Institute of Computer Graphics and Algorithms
Vienna University of Technology
Peter Kohlmann 2
Outline
Motivation
Multi-planar Reformatting (MPR)
Results for different access patternsMPR
Random access
Spherical access
Conclusions
Peter Kohlmann 3
Motivation
Most medical workstations: linear volume layout
Increasing size of medical volume data
Main memory: limiting factor for data visualization
Better memory utilization with subdivided volumes
Evaluation for company partner:
use of bricked volume layout for medical workstation implemented in Java
performance for common access patterns to medical volume data
Peter Kohlmann 4
Bricking in a Nutshell
Medical volumes data sets: stacks of 2D images (slices)
Linear volume layout: data values stored in single array
Problem: rendering of large data sets
Bricked volume layout: subdivision of volume into smaller parts (bricks)
Single brick: fixed number of data values in x-, y- and z-dimension
Peter Kohlmann 5
Multi-Planar Reformatting in a Nutshell
Important access pattern to medical volume data
Radiologists prefer to examine 2D slices
Arbitrary reformation of 2D image stack
Medical workstations display volume data in different views
Peter Kohlmann 6
Basic Algorithms
BrickGeneration
Image
BrickRasterization
Basic Ray Setup
Brick Prefetching
Brick-wise Processing
Ray Propagation
MPR Computation
Peter Kohlmann 7
Brick Generation
Efficient addressing: brick size power of two
Good choice: 64 KB (32x32x32 x 16 bit) (Grimm et al. 04, Law and Yagel 96)
Brick is simple data structure: unique ID
min- and max-value
padding
Peter Kohlmann 8
MPR Computation
Brick-wise resampling of the volume along scan lines (rays)
Ray Propagation
Brick-wise Processing
Brick Prefetching
Basic Ray Setup
BrickRasterization
Peter Kohlmann 9
MPR Computation
Ray Propagation
Brick-wise Processing
Brick Prefetching
Basic Ray Setup
BrickRasterization
Processing of a single brick
Image generation
Peter Kohlmann 10
Results
MPR Computation
Random access
Spherical access
PC configurationAMD Athlon 64 Dual Core Processor 4400+
2 GB of main memory
NVIDIA GeForce 7800 GTX with 256 MB of internal memory
Test data set: 512 x 512 x 333
Peter Kohlmann 11
Results – MPR Computation
axial
coronal
sagittal
arbitrary
Computation time for single slice (512 x 512)
Peter Kohlmann 12
Results – MPR Computation
Computation time for single slice (512 x 512)
Evaluation: Axial and coronal: -30%Sagittal: +30%Randomly oriented plane: -16%High performance gaps for linear volume layout: different memory access patternsUtilization of bricking better data locality
Peter Kohlmann 13
Results – Random Access
Worst case scenario to access data values concerning data locality
Time to access 512 x 512 randomly distributed values
21.4 ms (linear volume layout)
41.4 ms (bricked volume layout)
Calculation effort to access the data value at certain position
Linear volume layout: one-level calculation
Bricked volume layout: two-level calculation
Peter Kohlmann 14
Results – Spherical Access
Definition of parameterized sphere inside volume Simulation of region growingAccess 512 x 512 data values on parameterized sphere surface
Radius: 5 to 150Linear volume layout: 10.5 ms – 13.6 msBricked volume layout:
No brick prefetching: 32 ms – 260 msBrick prefetching: 15.5 ms (constant)
reasonable performance with intelligent prefetching
Peter Kohlmann 15
Conclusions
Evaluation results for different access patterns to medical volume data
MPR
Random access
Spherical access
Benefits of bricked volume layout more pronounced for larger data sets
We recommend the application of bricked volume layout to a medical workstation based on Java
Peter Kohlmann 16
Thanks for your Attention!