Multi-Institutional Medical Imaging Research Data Collection: Challenges of Standardization of Protocols and Header Information to Make an Imaging Biobank


Fattane Pourakpour 1 , * , Bahaareh Siahlou 1 , Shaghayegh Karimi 1 , Zeynab Khodakarami 1 , Mansoor Fatehi 1

1 National Brain Mapping Laboratory, Tehran, Iran

How to Cite: Pourakpour F, Siahlou B, Karimi S, Khodakarami Z, Fatehi M. Multi-Institutional Medical Imaging Research Data Collection: Challenges of Standardization of Protocols and Header Information to Make an Imaging Biobank, Iran J Radiol. 2019 ; 16(Special Issue):e99147. doi: 10.5812/iranjradiol.99147.


Iranian Journal of Radiology: 16 (Special Issue); e99147
Published Online: December 10, 2019
Article Type: Abstract
Received: October 26, 2019
Accepted: December 10, 2019


Background: Iranian brain mapping biobank (IBMB) has three major sources of data to be processed, categorized, and tagged for recurrent use by researchers, including research image acquisition, large scale cohort studies, and routine clinical samples from collaborating institutions. A major limitation of samples coming from routine clinical centers is the potential diversity of data parameters that may prevent to merge databases create biobanks.

Objectives: The study was performed to find out the reliability of a multi-institutional case collection.

Methods: Voluntary case collection was performed from four institutions that signed an agreement with the national brain-mapping laboratory. The centers operated machines from different vendors including Siemens and GE and the scanning protocols were diverse according to the operating technologists. An in-house developed application based on MATLAB 2018b was used to extra DICOM header information from the donated studies. All DICOM headers were imported to a unified database to be analyzed according to the modality, vendor type, and operator protocols.

Results: A total number of 1581 cases were entered into the project with 2414 procedures performed over a six-month period. This collection included 199509 series of images for which all tags were extracted. Except for the modality-specific tags (Table 1), all other tags were found to be uniform regardless of the machine and protocol. The most important tag diversity was seen in the MRI scanning parameters: “Protocol Name”, “Scan Options”, “Scanning Sequence”, “Sequence Name”, and “Sequence Variant”.

Conclusion: DICOM 3.0 standard has an invaluable role in the standardization of information incorporated into the image files as a header, which makes multi-institutional data collection feasible to a large extent. Practice-based data elements need to be unified at the time of acquisition or at the time of importing samples into a biobank to make the dataset homogeneous.

To see Table 1, please refer to the PDF file.

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