1. SP1.3 - Medical Diagnosis and Imaging Robert G. BellemanUniversiteit van Amsterdam / Philips Research robbel@science.uva.nl

2. AMC and VUmc Philips Intera 3T MRI scannerAMC, Amsterdam MEG scannerVUmc, Amsterdam R.G. Belleman, robbel@science.uva.nl

3. Eddy current reduction • Shear, magnification and translation as a result of residual currents in DWI • 2D matching to correct • Computationally expensive • Parallelization throughdomain decomposition • Computing cycles via Grid • Integrated PACS solution Effects of residual eddy currents on Philips 3T Intera with DWI.Figure by Erik-Jan Vlieger, AMC. R.G. Belleman, robbel@science.uva.nl

4. Matched Masked Bone Elimination • MMBE method • Matching of CT scans • Computationally expensive • Within VL-E: • Computing cycles from the Grid • Integrated PACS solution R.G. Belleman, robbel@science.uva.nl

5. Brain Imaging and Fiber Tractography • Diffusion Weighted Imaging (DWI) • Restricted Brownian motion results in anisotropy that can be measured • >= 6 measurements, reduced to tensor per voxel • Largest eigenvectors give diffusion vector • Whole volume fiber tracking can takemany hours • Depends on size of volume and numberof measurements per voxel • Suitable for parallelization • Visualization techniques R.G. Belleman, robbel@science.uva.nl

6. MR Virtual Colonoscopy • CT virtual colonoscopy exists • Minimally invasive • Use of MR has strong and weakpoints: • No X-ray(more suitable for screening) • Worse Signal/Noise than CT(requires powerfull segmentationtechniques) R.G. Belleman, robbel@science.uva.nl

7. MEG data analysis • Inverse modeling and non-linear systems modeling of brain activity • Parallelization of iterative solver R.G. Belleman, robbel@science.uva.nl

8. Data storage, retrieval and sharing • fMRI and MEG are scarce but complementary modalities • Access to each other’s resources • Shared data access • Data sizes are 101 to 104 MB per scan • High capacity, reliable and dependable storage • Online and near-line access patterns • Time/location independent access • Collaborative scientific research • Information sharing • Metadata modeling • Ownership, privacy regulations, AAA&S R.G. Belleman, robbel@science.uva.nl

9. Interactive 3D medical data visualization • Innovative display solutions • Co-located data visualization throughaugmented reality (AR) • Longitudinal (4D) datarepresentation • Image guided surgery R.G. Belleman, robbel@science.uva.nl

10. MRI scanner LUMC AMC Patient’s vascular geometry(MRA) MRI scanner Simulated “Fem-Fem” bypass Diagnosis and surgical planning • 3T MRI in vascular disease • Computer Aided Diagnosis • Computational Fluid Dynamics • Surgical planning R.G. Belleman, robbel@science.uva.nl

11. Analyses-------------TypeDepartmentDatePatient ID… Scan-------------TypeDepartmentDatePatient ID… Patient-------------NameIDDate of birthSex… Processing steps andobjects definitions Process Flow Templatedefinitions Process Flow Instance Experiment topologies VL-eMedical Problem Solving Environment R.G. Belleman, robbel@science.uva.nl

12. 2D/3D visualization VL experiment topology Image processing,Data storage VL-eMedical Problem Solving Environment Data retrieval,acquisition Filtering, analyses,simulation R.G. Belleman, robbel@science.uva.nl

13. SP 1.3 requirements • Shared, high volume data storage • Secure, reliable, dependable • Opportune access to computational resources • Clinical relevance often depends on timeliness • Experimental and dependable VL environments • Resp. for development and real-life applications • Application prototyping environment • Medical Problem Solving Environment R.G. Belleman, robbel@science.uva.nl

14. Relations to other SPs • Interactive Problem Solving Environments (SP 2.1) • User Interfaces, VR based visualization (SP 2.3) • Virtual Laboratory and System Integration (SP 2.5) R.G. Belleman, robbel@science.uva.nl