Partners: TU Delft, VU, CWI

SP2.3: UI and VR Based Visualization

• Ongoing Activities and progress• Collaboration Highlight with SP 1.6 DUTELLA

R. van Liere April 7th, 2006

SP 2.3 people

4 PhD students: Broersen, Burakiew, Kruszynski (CWI)van der Schaaf (VU)

3 PD: Botha, Koutek (TUD)de Leeuw (CWI)

4 supervision: van Liere (CWI) Post, Jansen (TUD)Bal (VU)

SP2.3 ongoing activities

Multi-spectral visualization SP 1.6Particle visualization SP 1.6Confocal Cell ImagingVolume measuring SP 2.1Medical Imaging SP 1.4Virtual Reality on the GRID SP 3.1Distributed Scene Graphs SP 3.1

SP 2.3 status

25 international publications 2 spin-offs

Foldyne (TU Delft)Personal Space Technologies (CWI)

Projected output4 PhD thesisAt least 2 packages in PoC

Collaboration SP 1.6 DUTELLA

Prof Ron Heeren (ALMOF)

Topic: Mass Spectrometry for molecular imagingMotivation: need for better MS analysis toolsVisualization Topics:

Multi-spectral data visualizationIn-silico mass spectrometry

Envisioned output:GRID enabled toolbox for MS analysisApplications according to VL-e methodology

Problem: aligning multi-spectral data cubes

Multi-spectral data cube: 256x256x65kMultiple data cubes

±100 cubes in mosaicCurrent procedure: manual alignment on pixel values

Our novel approach

Idea: Align spectral features in adjacent samples

Approach:Compute spectral features using PCAFor each feature, find a most optimal spatial alignment of the featureThe overall spatial alignment is optimal for all features

MS beelden dijbeen muis

First Spectral Feature = Principal Component1

Second Spectral Feature Principal Component2

Minima landscape

Minimization map of 2nd feature

use the combination of 2 local minima

Minimization map of 1st feature

Impact ? Generic ? GRID?

Faster, unsupervised objective reproducible alignment combined with VL inspection tools for SP1.6

Method can also be applied to multi-spectral data cubes from other types of microscopes/telescopes.

Data-cube:256x256x65K. 100 cubes. Alignment:15min in Matlab. Combinations: (100 2) * 15

Problem: Meaningful ion dynamics

Ion clouds: ~50k ions x 1M steps Current visualizations are low level, eg.:

But how about: Intra ion-cluster interactions and their causesIntra ion-cluster interactions?

Our novel approach

Idea: simplify images withStatistical parameterized iconsSemantic camera control

Approach:Parameterized “comet-icons”Camera motion relative to comet dynamics

Example: icons

Ions groupsStatistical ion properties of groupIon density dynamics

Example: camera control

Trapping motionRelative cyclotron frequencyTracks of Frenet frames

Impact ? Generic ? GRID?

Improvement of mass accuracy understanding/control leads to enhanced protein ID in proteomics

Software framework is targeted towards particle visualization. Semantics of icons/cameras can be added/changed/enhanced

Near-future: optimization of simulation initial conditions

Final SP 2.3 comments

SP 2.3 is well on track

Projected output:GRID enabled toolbox SP2 layer Applications using toolbox SP1 layer

However: visualization PhDs are not mass spectrometry scientists!