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What are the right questions?

The Data Explosion How can we achieve interoperability? James Williams Siemens Corporate Research Princeton, NJ. ?. What are the right questions?. Size : data is growing, how do we cope? Repeatability : how do we standardize and normalize image generation?

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What are the right questions?

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  1. The Data ExplosionHow can we achieve interoperability?James WilliamsSiemens Corporate ResearchPrinceton, NJ

  2. ? What are the right questions? • Size: data is growing, how do we cope? • Repeatability: how do we standardize and normalize image generation? • Availability: how do we get what we want, where we want, in time to make a difference? • Processing: how do we make algorithms comparable and portable? • Validation: what are the right mechanisms for validation? • Context: how can we convert data into knowledge?

  3. The challengeof size • Data sizes grow relentlessly • Spatial resolution: CT • Temporal Resolution: US • Multiple modalities, PET/CT… • Follow-up studies • Network bandwidth as the bottleneck: • Faster networks or… • Move processing near the data! • When we can, compress: What is diagnostic quality? Where do we need it & when?

  4. The challengeof repeatability • Sources of variation in acquisition • Hardware • Calibration • Protocol • Reconstruction • Automation of MR scan-rescan protocol: AutoAlign & Phoenix

  5. Goalsof availability • The imaging workspace available anywhere • reading room • office • home • All data relevant to the patient in one click • All data relevant to the protocol available and searchable (teaching cases etc..) • Some steps in the right direction: web clients, hanging protocols

  6. Towards the compatibility andcomparability of processing • Visualization, detection, segmentation, registration measured with respect to reference standards (VRD) • Cross platform executables • Plug-in processing, DICOM WG23 • ITK, a reference implementation? • Open the platforms, but we must assure: • Safety • Privacy • Stability

  7. QSR The challengeof algorithm validation • Create reference databases as open challenges • Should part of the database be hidden to avoid over-training? • Database must grow and change as acquisition evolves • Fast-tracking through the FDA • Validation & liability as a 3rd party business? CE

  8. Data in context • Beyond images: patient record, lab data, genomic, proteomic data • The universal medical record: technology is not the problem. Individual privacy, ethics and economics are the key drivers. • The anonymous database of everything. The benefit is clear, how do we cover the cost? • Making knowledge from data. The structure has to support exploration for anecdote, and testing of hypothesis.

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