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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

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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

  • Centrally Monitored QC/QA in multi-center trials

  • Central Data Analysis/Archival Issues


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


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

  • 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


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

  • Study Protocol Design

  • Data Quality

  • Data Format Issues

  • Data “Cleaning”

  • Data Registration (serial studies)

  • Data Analysis

  • Data Archival


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

  • 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

  • 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

  • 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

  • Imaging Industry “standards”

    • DICOM (a flexible standard)

  • Alternate Standards

    • (e.g. Analyze, TIFF)


Data Cleaning

  • Prospective QC

    • Rescan if possible

    • Minimization of Lost Data

  • Data Rejection

    • Quantitative Basis!

  • Site Notification


Data Registration

  • Minimize positioning errors in protocol

  • Use Immobilizers to alleviate motion

  • REGISTER serial scans


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

  • 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

  • Driven by needs of sponsor and regulatory agencies

  • Central Consolidation and Storage

  • Coordinating Center Archival


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


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