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Generating Big Data Repositories for Research in Medical Imaging
تولید داده های بزرگ مخازن برای پژوهش در تصویربرداری پزشکی-2016 The production of medical imaging data has grown
tremendously in the last decades. The proliferation of digital
acquisition equipment has enabled even small institutions to
produce considerable amounts of studies. Furthermore, the
general trend for new imaging modalities is to produce more data
per examinations. As a result, the design and implementation of
tomorrow’s storage and communication systems must deal with
Big Data issues. Moreover, new realities such as the outsourcing of
medical imaging infrastructures to the Cloud impose additional
pressure on these systems.
The research on technologies for coping with Big Data issues on
large scale medical imaging environments is still in its early stages.
This is mostly due to the difficulty of implementing and validating
new technological approaches in real environments, without
interfering with clinical practice. Therefore, it is crucial to create
test bed environments for research purposes.
This article proposes a methodology for creating simulated Big
Data repositories. The system is able to use a real world repository
to collect data from a representative time window and expand it
according to research needs. In addition, the solution provides
anonymization tools and allows exporting two types of simulated
data: a structured file repository containing DICOM objects and
a statistical summary of the archive content based on its
characteristics, such as the number of produced studies per day,
their modality and their associated data volumes.
Keywords : medical imaging | pacs, dicom | big data | repositories. |
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