Image Query (IQ) Project Update

Building queries one question mark at a time

March, 2009

Daniel Rubin M.D., M.S.Assistant Professor of Radiology

Research Scientist, Center for Biomedical Informatics Research

Stanford University

David S. Channin, MDAssociate Professor of Radiology

Northwestern University

Joel Saltz, MD, PhD and OSU TeamProfessor and Chair

Biomedical InformaticsThe Ohio State University

Brenda Young, BAAmerican College of Radiology

Imaging Network (ACRIN)

Presenters

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 Model models 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

Some info is in NCIA Model(DICOM metadata)

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

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)

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

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

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

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….

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

Federated Query

caGrid Selected

Search Expression

Search Expression Detail

Example Canonical Query Graph

Lung Nodule

DICOM Image

Spiculation

“<= 1.5 mm”

OBSERVATION

DISEASE

CHARACTERISTIC

DISEASE IMAGING

has SOPInstanceUID

has ImagingObservation

has ImagingObservation Characteristic

DICOM Image

has SliceThickness

has Rating

>=3

has Size

>=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”

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

Ontology Driven Query Process

Query Translation

and Processing

Query TranslationQuery Translation

Query

Execution (FQP)

Query

Execution (FQP)

Grid DICOMGrid DICOM

Grid AIMGrid AIM

DCQL

User InputUser Input

DCQL

AIM/

DICOM

CQL

CQL

SPARQL

OntologiesOntologies

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

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

Questions?