The CardioVascular Research Grid Cardiac Imaging

Transcription

1 The CardioVascular Research Grid Cardiac Imaging Helping the community to share and analyze cardiovascular data Presented by: Stephen Granite, MD Author: Raimond L. Winslow, MD Institute for Computational Medicine Johns Hopkins University

2 The CardioVascular Research Grid NHLBI-Funded R24 Resource Mission: Provide NHLBI researchers with informatics tools that enable their science Approach: Carefully identify the core needs of these studies Use or extend existing software whenever possible Focus resources on developing technology meeting special needs of the CV community When possible, CVRG technology should be interoperable with other grid projects (e.g., cabig)

3 Participating Institutions The Johns Hopkins University Institute for Computational Medicine D. Geman, D. Naiman, M. Miller, R. Winslow, L. Younes Emory University Center for Comprehensive Informatics J. Saltz, T. Kurc, A. Post Wake Forest University TSI Biomedical Informatics Center J. Carr, Y. Ge University of Chicago Computation Institute I. Foster, R. Madduri

4 Driving Biomedical Projects Atlanta Cardiomyopathy Consortium Coronary Artery Risk Development in Young Adults (CARDIA) Association Analysis in Essential Hypertension Study Emory-Morehouse Partnership to Reduce CV Disparities Hypertrophic Cardiomyopathy Consortium Jackson Heart Study Multi-Ethnic Study of Atherosclerosis (MESA) Pediatric Heart Network Prospective Observational Study of the ICD

5 Core Use Cases (1-3) 1. Secure, interactive access to all study data Data federation and querying 2. Electrocardiography data Searchable database Capture waveforms, analysis results, and meta-data Analyze, visualize, annotate waveforms Common terminology 3. Image data Real-time transfer of image data from Field to Reading Centers Semi-automated procedures for quality control Integrate commercial image analysis workstations Capture image analysis results and meta-data

6 Core Use Cases (4-5) 4. Clinical data Ability to access and query different clinical information sources Common terminology across different information sources Common security procedures 5. Workflows Implement common data analysis procedures Analyses must be easy to perform Assistance in creating & modifying workflows

7 CVRG Architecture

8 Data Services and Federation Proteomics Data Service XNAT-CVI ECG Data Service mrna Data Service CVRG User Interface CRFs (OpenClinica) GWAS Data Service Developed by CVRG Developed by cabig Developed by BIRN Other Open Source

9 The CVRG User Interface

10 Analysis Services Heart Shape & Motion Analyses Machine Learning Algorithms (RWebServices, geworkbench) CVRG User Interface ECG Analysis Services

11 Image Data Transfer

12 Image Quality Control Auto QC check for Gad scans in No Gad cohort 4 subjects with Gad protocol (but No Gad)

13 Heart Shape and Motion Analysis LDDMM/PTA Analysis Workflow DICOM Image Mgmt Workflow 3DSlicer (Visualization) XNAT-CVI Seg3D (Segmentation) XNAT (Analyze) LDDMM/ PTA Analysis QC Upload

14 Common Coordinate System Beg et al (2004). Computational cardiac anatomy using MRI. Magn. Reson. Med. 52(5): 1167

15 Statistical Inference on Heart Shape New Patient Heart H 1 Global Average Heart H av T(H 1 ) H av (preserves dot products and norms) Class 1 = ischemic cardiomyopathy Class 2 = non-ischemic cardiomyopathy Is the transformation T( ) from class 1? If yes, then Else ischemic cardiomyopathy non-ischemic cardiomyopathy

16 Statistical Inference on Heart Shape septum Ant. Inferior Pvalue Statistically reliable (p=.008) shape difference in ICM vs NICM Supports classification on novel test data Ardekani et al (2009). Computational method for identifying and quantifying shape features of human left ventricular remodeling. Ann Biomed Eng DOI: /s

17 Statistical Inference on Heart Motion Examine deformation from ED to ES ES (NICM) ES (ICM) Question Are their deformation differences that discriminate ICM from NICM? Parallel Transport Algorithm (PTA) Younes et al (2008). Transport of Relational Structures in Groups of Diffeomorphisms. J. Math. Imaging Vis. 32:41-56 Statistically Significant Difference in Wall Thickening From ED to ES PTA Preserves Strain Tensor at Each Image Voxel

18 The CVRG Team Johns Hopkins University Don Geman Mike Miller Dan Naiman Rai Winslow Laurent Younes Siamak Ardekani Timothy Brown Bill Girten Steve Granite David Hopkins Luan Le Kyle Reynolds Michael Shipway Matt Toerper NHLBI Emory University Joel Saltz Tahsin Kurc Mark Grand Tony Pan Michel Monsour cagrid KC Steve Langella Justin Permar Wake Forest University J Jeffrey Carr Yaorong Ge Jennifer Larkin

19 Questions and Discussion