1. 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 7 th, 2006

2. 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)

3. SP2.3 ongoing activities Multi-spectral visualization SP 1.6 Particle visualization SP 1.6 Confocal Cell Imaging Volume measuring SP 2.1 Medical Imaging SP 1.4 Virtual Reality on the GRID SP 3.1 Distributed Scene Graphs SP 3.1

4. SP 2.3 status 25 international publications 2 spin-offs Foldyne (TU Delft) Personal Space Technologies (CWI) Projected output 4 PhD thesis At least 2 packages in PoC

5. Collaboration SP 1.6 DUTELLA Prof Ron Heeren (ALMOF) Topic: Mass Spectrometry for molecular imaging Motivation: need for better MS analysis tools Visualization Topics: Multi-spectral data visualization In-silico mass spectrometry Envisioned output: GRID enabled toolbox for MS analysis Applications according to VL-e methodology

6. Problem: aligning multi-spectral data cubes Multi-spectral data cube: 256x256x65k Multiple data cubes ±100 cubes in mosaic Current procedure: manual alignment on pixel values

7. Our novel approach Idea: Align spectral features in adjacent samples Approach: Compute spectral features using PCA For each feature, find a most optimal spatial alignment of the feature The overall spatial alignment is optimal for all features

8. MS beelden dijbeen muis

9. First Spectral Feature = Principal Component1

10. Second Spectral Feature Principal Component2

11. Minima landscape Minimization map of 2nd feature use the combination of 2 local minima Minimization map of 1st feature

12. 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

13. 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 causes Intra ion-cluster interactions?

14. Our novel approach Idea: simplify images with Statistical parameterized icons Semantic camera control Approach: Parameterized “comet-icons” Camera motion relative to comet dynamics

15. Example: icons Ions groups Statistical ion properties of group Ion density dynamics

16. Example: camera control Trapping motion Relative cyclotron frequency Tracks of Frenet frames

17. 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

18. 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!