Federating Standardized Clinical Brain Images Across Hospitals.

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Presentation transcript:

Federating Standardized Clinical Brain Images Across Hospitals

Clinical Structural MR Images What we can do with it? Consult with specialists treating similar cases Compare brain measures across diagnoses and lifespan, gender and ethnicity Cohort identification & recruitment Quantitative measures to aid in diagnosis, prognosis, and treatment effectiveness

Project Goals To acquire and share standardized, clinical MRI scans To acquire and share standardized, clinical MRI scans To build the infrastructure to share these scans with other pertinent information across hospitals. To build the infrastructure to share these scans with other pertinent information across hospitals. Develop a user interface for clinical radiologists Develop a user interface for clinical radiologists

Image Standarization Short Imaging Protocol Sequences Short Imaging Protocol Sequences – High-resolution T1 – Diffusion Tensor Imaging (DTI) – Collected on ALL clinical brain scans (18 + yrs, non- emergency) where feasible MagPhan Phantom Sequence MagPhan Phantom Sequence – Used for correcting spatial distortion and inhomogeneity in images – Collected weekly at each site

Challenges Implementing standardized data acquisitions across hospitals Implementing standardized data acquisitions across hospitals Sharing clinical data Sharing clinical data IRB approval IRB approval Data anonymization Data anonymization Data volume Data volume Federated data hosting and mediated queries across data sets Federated data hosting and mediated queries across data sets

NCRR Collaborative Communities through BIRN: Clinical Translational Science Institutes High Resolution Imaging US Hospitals Vision: Federating Standardized Clinical Brain Scans Across Hospitals Research-Ready Clinical Brain Scans Across US Hospitals EMR Demographic Data Diagnostic Data Treatment Data Derived Imaging Data Patient Willingness to Participate in Research Translational Science Applications: Cohort identification & recruitment Image processing algorithm development Large-scale genomic studies including brain measures Opportunities for patients and their relatives to participate in research Quantitative measures to aid in diagnosis, prognosis, and treatment effectiveness ~3,000 brain MRIs per scanner per year

System Architecture Chervenak, Van Erp et al. ACM International Health Informatics Symposium Under Review. De-identified EMR data De-identified EMR data

System Architecture Chervenak, Van Erp et al. ACM International Health Informatics Symposium Under Review. De-identified EMR data De-identified EMR data UCSD UCLA Scripps CTSIs eMERGE US Hospitals

Image Gateway Service Chervenak, Van Erp et al. ACM International Health Informatics Symposium Under Review.

Mediator UCI USC Chervenak, Van Erp et al. ACM International Health Informatics Symposium Under Review.

GENETICS BEHAVIOR BRAIN GENETICS BEHAVIOR BRAIN GENETICS BEHAVIOR BRAIN GENETICS BEHAVIOR BRAIN GENETICS BEHAVIOR BRAIN GENETICS BEHAVIOR BRAIN GENETICS BEHAVIOR BRAIN GENETICS BEHAVIOR BRAIN X 3000 INVESTIGATOR INITIATED PROTOCOL BRAIN IMAGING CAP + INVESTIGATOR INITIATED PROTOCOL BRAIN IMAGING CAP + INVESTIGATOR INITIATED PROTOCOL BRAIN IMAGING CAP + Brain Research Genomics Superstruct Project in Boston

User Interface Requirements How will the data be used? How will the data be used? What do radiologists want to see? What do radiologists want to see? – What information can we give them within the IRB constraints? Can we provide value-added information such as statistical differences of their case from some other cohort? Can we provide value-added information such as statistical differences of their case from some other cohort? – How would the user interact with this information? Is a mobile platform needed? Is a mobile platform needed?

Future Work User interface design Add diagnostic metadata from EMR Additional sites in federation Value added statistics and/or other analytics or metrics Data movement optimizations

THANK YOU!