Image Query (IQ) Project Update

1. Image Query (IQ) Project Update Building queries one question mark at a time March, 2009

2. Presenters Daniel Rubin M.D., M.S. Assistant Professor of Radiology Research Scientist, Center for Biomedical Informatics Research Stanford University David S. Channin, MD Associate Professor of Radiology Northwestern University Joel Saltz, MD, PhD and OSU Team Professor and Chair Biomedical Informatics The Ohio State University Brenda Young, BA American College of Radiology Imaging Network (ACRIN)

3. Image Query (IQ) Project Researchers need to intuitively search caBIG resources Holy Grail: Query across any and all models across any and all grid services for those models Current caBIG infrastructure does not focus on query Imaging Information: NCIA Model and in AIM Model NCIA Modelmodels some DICOM images meta-data Middleware provides access to images via this model AIM models annotation and markup data Need to be able to query and retrieve image (NCIA Model) and image annotation (AIM Model) data by many different criteria

4. Some info is in NCIA Model(DICOM metadata)

5. Some info is in AIM Model Anatomic Entity: Left Lung (Radlex:1326) Anatomic Entity: Upper lobe of left lung (RID1327 Observation: Mass (RID:3874) Characteristic: Microlobulated margin (RID5712) Geometric Shape: Polyline 2D coordinates: {(x,y),(x,y)….} Calculation: Largest diameter result: 2.8 cm

6. Why do we need Image Query? Retrieve data in from caGrid Services Use Case: query NCIA Retrieve data accessible via caGrid to a DICOM Workstation From NCIA images (DICOM header attributes) From “NCAA” (AIM metadata)

7. Overview of Query Formulation Project Purpose: Create query formulation/execution engine for images on caGrid Will show: Phase II Plans and initial work New developments: selected a use case for query based on LIDC and NCIA Biggest challenges: Limited budget for Phase II; need to scope project Plans: Will create working demonstration of query formulation/execution on caGrids

8. IQ Phase II Develop Working Prototype Working prototype of query federation and execution components of the IQ Tool Simple GUI for user to construct a cross-domain query Targets AIM and NCIA data services Retrieves the relevant image metadata and associated annotations Will use high-performance data transport, and leverage role-based authorization Will leverage the In Vivo Imaging Middleware and the caGrid federated query processing infrastructure

9. QF Project Deliverables GUI for users to create queries intuitively Query Formulation Engine translating user query to a DCQL query that runs on caGrid Query Execution Engine that processes the query and retrieves images Demonstrate concrete use case using NCIA data and AIM Grid Service

10. Project Tasks Collect/define use cases: Focus on current data in NCIA; selected one particular use case for querying NCIA data Define a single canonical query graph structure: Initially support just one graph Develop GUI for users to select query attributes: to enable users to specify the query attributes DCQL Query of DICOM header and AIM data (DCQL = query language for caGRID) Execute DCQL on NCIA and AIM Data Services: The AIM Data Service (“NCAA”) will store AIM image metadata and annotations Send retrieved AIM & DICOM objects to user A Miracle Occurs….

11. Use Case: NCIA and LIDC Query: “Find all images with slice thickness <= 1.5mm showing lung nodules < 3 cm diameter and that have a spiculation rating of at least 4” Query Parameters: Slice thickness <=1.5 mm (DICOM header) Lung nodules (AIM) Size < 3 cm in diameter (AIM) Radiologists’ subjective rating spiculation observation characteristic >= 4 (AIM) NB: This query requires searching two federated resources NCIA Model data in NCIA (from DICOM header) AIM Model data in Annotation Archive

12. Federated Query

13. caGrid Selected

14. Search Expression

15. Search Expression Detail

16. Example Canonical Query Graph DISEASE OBSERVATION CHARACTERISTIC DISEASE IMAGING DICOM Image DICOM Image has SOPInstanceUID has SliceThickness has ImagingObservation “<= 1.5 mm” Lung Nodule has ImagingObservation Characteristic has Size Spiculation >=4 “Find all images with slice thickness <= 1.5mm showing lung nodules < 3 cm diameter and that have a spiculation rating of at least 4” has Rating >=3

17. Ontology Driven Query Process Query is constructed in the Query Formulation UI using semantically meaningful and ontology anchored concepts Query is represented as SPARQL and submitted to the DCQL translator, along with the target Data Service URLs DCQL translator generates DCQL from SPARQL, and return it to Query Formulation UI DCQL is executed using a local instance of Federated Query Processing (FQP) Engine FQP Engine converts DCQL into CQL queries and coordinates the execution of the CQL queries against the target Data Services FQP Engine retrieves the results (images and annotations) and return to the Query Formulation UI Query Formulation UI displays the retrieved images and annotations

18. Ontology Driven Query Process Ontologies Query Translation and Processing Query Translation SPARQL DCQL Grid DICOM DCQL CQL Query Execution (FQP) AIM/ DICOM CQL Grid AIM User Input

19. Query Execution Engine Once a query is formulated as an ontology-based query graph, this query must be translated in such a way so that it can be executed on the diverse caBIG data sources. This is done in the Query Execution Engine

20. Summary of Phase II Create prototype Image Query tools and infrastructure Application ontology Represent kinds of information that users seek and granular data fields actually contained in various image-related databases User interface Allow users to select data elements and data element values and combine them with Boolean operators Query engine Execute the query that is formulated by the UI and application ontology

21. Questions?