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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 l.jpg
Overview Assurance Issues for Medical Imaging in Clinical Trials

  • Quantitative Imaging vs: Clinical Imaging

  • Protocol Development Issues

  • Centrally Monitored QC/QA in multi-center trials

  • Central Data Analysis/Archival Issues



Ci qi l.jpg

Visualization of lesions and/or disease From Clinical Imaging (CI)?

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 l.jpg
Is there a use for CI in trials From Clinical Imaging (CI)?

  • 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 l.jpg

Organ volumes or Subvolumes From Clinical Imaging (CI)?

Perfusion/Permeability/blood flow

Atrophy indices

Necrosis/Hypoxia Indices

Metabolic Indices (e.g. pH, energetics)

Ligand Binding

Vascular Indices

Examples of QI


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ISSUES for QI From Clinical Imaging (CI)?

  • Study Protocol Design

  • Data Quality

  • Data Format Issues

  • Data “Cleaning”

  • Data Registration (serial studies)

  • Data Analysis

  • Data Archival


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Study Protocol: General From Clinical Imaging (CI)?

  • 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


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Multiple Sites/Platforms From Clinical Imaging (CI)?

  • 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)


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Data Quality Assessment From Clinical Imaging (CI)?

  • 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 l.jpg
Incoming Data Formats From Clinical Imaging (CI)?

  • 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)


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Data Storage Formats From Clinical Imaging (CI)?

  • Imaging Industry “standards”

    • DICOM (a flexible standard)

  • Alternate Standards

    • (e.g. Analyze, TIFF)


Data cleaning l.jpg
Data Cleaning From Clinical Imaging (CI)?

  • Prospective QC

    • Rescan if possible

    • Minimization of Lost Data

  • Data Rejection

    • Quantitative Basis!

  • Site Notification


Data registration l.jpg
Data Registration From Clinical Imaging (CI)?

  • Minimize positioning errors in protocol

  • Use Immobilizers to alleviate motion

  • REGISTER serial scans


Data registration15 l.jpg
Data Registration From Clinical Imaging (CI)?

  • 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 l.jpg
Data Analysis From Clinical Imaging (CI)?

  • 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 l.jpg
Data Archival From Clinical Imaging (CI)?

  • Driven by needs of sponsor and regulatory agencies

  • Central Consolidation and Storage

  • Coordinating Center Archival


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Summary From Clinical Imaging (CI)?

  • Close Intellectual and technological relationship among the sponsor, imaging site(s) and imaging coordinating center

  • Ongoing QC/QA

  • Blinded quantitative data analysis

  • Regulatory compliance


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