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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 Overview Quantitative Imaging vs: Clinical Imaging Protocol Development Issues

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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

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

is there a use for ci in trials
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
examples of qi
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
issues for qi
ISSUES for QI
  • Study Protocol Design
  • Data Quality
  • Data Format Issues
  • Data “Cleaning”
  • Data Registration (serial studies)
  • Data Analysis
  • Data Archival
study protocol general
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
multiple sites platforms
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)
data quality assessment
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)
incoming data formats
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)
data storage formats
Data Storage Formats
  • Imaging Industry “standards”
    • DICOM (a flexible standard)
  • Alternate Standards
    • (e.g. Analyze, TIFF)
data cleaning
Data Cleaning
  • Prospective QC
    • Rescan if possible
    • Minimization of Lost Data
  • Data Rejection
    • Quantitative Basis!
  • Site Notification
data registration
Data Registration
  • Minimize positioning errors in protocol
  • Use Immobilizers to alleviate motion
  • REGISTER serial scans
data registration15
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
data analysis
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
data archival
Data Archival
  • Driven by needs of sponsor and regulatory agencies
  • Central Consolidation and Storage
  • Coordinating Center Archival
summary
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