Poster No:
1842
Submission Type:
Abstract Submission
Authors:
Aaron Capon1,2, Robert Smith1,2, David Vaughan1,2,3, Donna Parker1, Graeme Jackson1,2,3, David Abbott1,2, for the Australian Epilepsy Project Investigators1
Institutions:
1The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia, 2The University of Melbourne, Victoria, Australia, 3Austin Health, Heidelberg, Victoria, Australia
First Author:
Aaron Capon, PhD
The Florey Institute of Neuroscience and Mental Health|The University of Melbourne
Melbourne, Australia|Victoria, Australia
Co-Author(s):
Robert Smith
The Florey Institute of Neuroscience and Mental Health|The University of Melbourne
Melbourne, Australia|Victoria, Australia
David Vaughan
The Florey Institute of Neuroscience and Mental Health|The University of Melbourne|Austin Health
Melbourne, Australia|Victoria, Australia|Heidelberg, Victoria, Australia
Donna Parker
The Florey Institute of Neuroscience and Mental Health
Melbourne, Australia
Graeme Jackson
The Florey Institute of Neuroscience and Mental Health|The University of Melbourne|Austin Health
Melbourne, Australia|Victoria, Australia|Heidelberg, Victoria, Australia
David Abbott, PhD
The Florey Institute of Neuroscience and Mental Health|The University of Melbourne
Melbourne, Australia|Victoria, Australia
Introduction:
Critical to confidence in acquiring high-quality and consistent imaging data is an assessment of the fidelity of the acquisition protocol. Detecting discordance between imaging data and a planned acquisition protocol should ideally be both immediate (to maximise chance of recovery of missing data and/or rectify protocol in preparation for future imaging sessions) and automated (for scalability to complex protocols and large studies). We have designed and developed a software tool that automates Protocol Quality Control (QC), checking for data completeness and conformity of image metadata. The Protocol QC software is designed to be very flexible and expandable, capable of assessing a wide variety of imaging protocols.
Methods:
Written in Python, the Protocol QC Software by Florey validates a DICOM dataset against one or more user-defined template file(s) containing desired protocol parameters. Standard and Enhanced DICOM datasets are supported. An overall matching score is calculated for each protocol template, along with both summary and verbose log files (and optionally JSON data) with information relating to data and metadata (mis)matches.
Each protocol template is written in JSON by the researcher. In addition to a GENERAL dictionary specifying protocol-wide parameters, there is one dictionary per acquired sequence. Within these dictionaries one defines a set of DICOM header fields to be checked; this includes the comparison operator to be used when checking that field, which can be one of: exact, regex, in_range, in_set.
There are several options that control how an input dataset is compared to the template during validation:
• Check for data completeness (e.g. number of expected DICOM files per series, frames per series, number of images in mosaic format)
• Check if the acquired sequences adhere to the ordering specified in the template
• Check order of field-mapping sequences relative to the corresponding acquisition
• Exclude an individual acquisition from checking of acquisition ordering conformity
• Raise an error if unexpected duplicates are present for a given acquisition
• Raise an error if the number of expected duplicates does not match that specified
• Allow for known optional acquisitions and/or unknown acquisitions
• A date range can be specified for the validity of a template
We containerised and integrated the Protocol QC Software by Florey into the image ingestion pipeline for the Australian Epilepsy Project (AEP). The AEP is an active large-scale project involving collection of advanced multi-modal MRI in thousands of participants.
Results:
The software has validated that the correct protocol has been used across hundreds of imaging sessions from multiple scanning sites, which would otherwise be difficult to validate. Several issues were also discovered, including some incomplete data transfers and minor protocol violations. Our proactive solution facilitated data recovery and process remediation.
Conclusions:
The Protocol QC Software by Florey is a flexible, open-source software that can check that DICOM acquisitions conform to a user's acquisition protocol template. The software is licenced under the GNU General Public License. See https://florey.edu.au/imaging-software
Neuroinformatics and Data Sharing:
Workflows 1
Informatics Other 2
Keywords:
MRI
Workflows
Other - Quality Control; Quality Assurance; Automated
1|2Indicates the priority used for review
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