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Biomarkers captured by medical images are increasingly used as indicators for the efficacy or safety of a certain drug or
treatment for clinical trials. For example, medical images such as CT or MR are often used for extracting quantitative
measurements for the assessment of tumor treatment response while evaluating a chemotherapy drug for therapeutic
cancer trials. Quality assurance is defined as "All those planned and systematic actions that are established to ensure that
the trial is performed and the data are generated, documented (recorded), and reported in compliance with good clinical
practice (GCP) and the applicable regulatory requirement(s)" [1]. Our objective is to build a generalized and an
automated framework for quality assurance within the clinical trials workflow. In order to reach this goal, a set of
standardized software tools have been developed for quality assurance. Furthermore, we outline some guidelines as
recommendations for the users handling the image data within the research workflow. The software tools developed
include tools for image selection, image pseudonymization and image quality conformance check. The export tools are
developed based on the specifications of the Integrating the Healthcare Enterprise (IHE) Teaching and Clinical Trial
Export (TCE) profile. A DICOM-based quality conformance approach has been developed by validating the DICOM
header attributes required for a certain imaging application (e.g. CAD, MPR, 3D) and comparing imaging acquisition
parameters against the protocol specification. A formal description language is used to represent such quality
requirements. For evaluation, imaging data collected from a clinical trial site were validated against Multi-Planar
Reconstruction (MPR). We found that out of 60 studies, about 30% of image series volumes failed the MPR check for
some common reasons.
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