Quantitative Imaging: Protocol Development and Quality Assurance Issues for Medical Imaging in Clinical Trials

1. Quantitative Imaging: Protocol Development and Quality Assurance Issues for Medical Imaging in Clinical Trials H. Cecil Charles, Ph.D. Director Duke Image Analysis Laboratory Duke University Medical Center

2. Overview • Quantitative Imaging vs: Clinical Imaging • Protocol Development Issues • Centrally Monitored QC/QA in multi-center trials • Central Data Analysis/Archival Issues

3. What Is Quantitative Imaging (QI) and How Is It Different From Clinical Imaging (CI)?

4. Visualization of lesions and/or disease Radiologic interpretation Rule-out or rule-in a diagnosis Diagnostic tree/1°,2°,3° diagnosis Determination of tissue characteristics from imaging parameters Algorithm/SOP/scaled interpretation Numeric output Incorporation in hypothesis testing or goal driven evaluation CI QI Effect Monitoring Diagnosis

5. Is there a use for CI in trials • If imaging is part of the diagnostic inclusion or exclusion criteria, a screening scan may be required • The screening scan may or may not be according to the QI protocol • Subsequent imaging sessions (including a baseline scan) are based on the QI protocol

6. Organ volumes or Subvolumes Perfusion/Permeability/blood flow Atrophy indices Necrosis/Hypoxia Indices Metabolic Indices (e.g. pH, energetics) Ligand Binding Vascular Indices Examples of QI

7. ISSUES for QI • Study Protocol Design • Data Quality • Data Format Issues • Data “Cleaning” • Data Registration (serial studies) • Data Analysis • Data Archival

8. Study Protocol: General • Driven by Study goals and QI Algorithm(s) (Analyses) • Maximize Information Content per unit time • Deterministic Figure of Merit (FOM) • CNR/(unit resolution * unit time) • Patient Comfort/Compliance

9. Multiple Sites/Platforms • Imaging protocol cross-validation • Rationalize Nomenclature • “Uniform” site training tailored to manufacturer/HW/SW status • Retrain with upgrades if necessary • Centrally monitored protocol compliance (QC)

10. Data Quality Assessment • SNR/CNR • Artifacts (e.g. Motion) [quantitative criteria: clutter/noise] • Protocol Adherence • Scan Parameters • Schedule • Technical Parameters (e.g. contrast dose and rate) • System Performance • Spatial Fidelity! (esp. in serial studies)

11. Incoming Data Formats • Native data from multiple manufacturers and multiple S/W releases • Varying Media Formats • MOD’s, DAT(s), CDROM(s) • Varying File Formats • DICOM(s) • Proprietary Formats • ACR/NEMA • “Local” formats (non-commercial PACS)

12. Data Storage Formats • Imaging Industry “standards” • DICOM (a flexible standard) • Alternate Standards • (e.g. Analyze, TIFF)

13. Data Cleaning • Prospective QC • Rescan if possible • Minimization of Lost Data • Data Rejection • Quantitative Basis! • Site Notification

14. Data Registration • Minimize positioning errors in protocol • Use Immobilizers to alleviate motion • REGISTER serial scans

15. Data Registration • Even with on site training and quality technologists, some misalignment will occur in serial studies • Alignment of the datasets minimizes the impact of this problem

16. Data Analysis • Prospective Criteria based on needs of study • Optimize FOM and QC criteria to match needs of algorithm • SOP • Replicate analysis to address drift

17. Data Archival • Driven by needs of sponsor and regulatory agencies • Central Consolidation and Storage • Coordinating Center Archival

18. Summary • Close Intellectual and technological relationship among the sponsor, imaging site(s) and imaging coordinating center • Ongoing QC/QA • Blinded quantitative data analysis • Regulatory compliance