MRI quality assurance based on 3D FLAIR brain images

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Peltonen , J I , Mäkelä , T & Salli , E 2018 , ' MRI quality assurance based on 3D FLAIR brain images ' , Magnetic Resonance Materials in Physics, Biology and Medicine. , vol. 31 , no. 6 , pp. 689–699 .

Title: MRI quality assurance based on 3D FLAIR brain images
Author: Peltonen, Juha I; Mäkelä, Teemu; Salli, Eero
Contributor organization: Clinicum
Department of Diagnostics and Therapeutics
University of Helsinki
Department of Physics
HUS Medical Imaging Center
Date: 2018-12
Language: eng
Number of pages: 11
Belongs to series: Magnetic Resonance Materials in Physics, Biology and Medicine.
ISSN: 0968-5243
Abstract: Objective Quality assurance (QA) of magnetic resonance imaging (MRI) often relies on imaging phantoms with suitable structures and uniform regions. However, the connection between phantom measurements and actual clinical image quality is ambiguous. Thus, it is desirable to measure objective image quality directly from clinical images. Materials and methods In this work, four measurements suitable for clinical image QA were presented: image resolution, contrast-to-noise ratio, quality index and bias index. The methods were applied to a large cohort of clinical 3D FLAIR volumes over a test period of 9.5 months. The results were compared with phantom QA. Additionally, the effect of patient movement on the presented measures was studied. Results A connection between the presented clinical QA methods and scanner performance was observed: the values reacted to MRI equipment breakdowns that occurred during the study period. No apparent correlation with phantom QA results was found. The patient movement was found to have a significant effect on the resolution and contrast-to-noise ratio values. Discussion QA based on clinical images provides a direct method for following MRI scanner performance. The methods could be used to detect problems, and potentially reduce scanner downtime. Furthermore, with the presented methodologies comparisons could be made between different sequences and imaging settings. In the future, an online QA system could recognize insufficient image quality and suggest an immediate re-scan.
Subject: 217 Medical engineering
3126 Surgery, anesthesiology, intensive care, radiology
Magnetic resonance imaging
Quality assurance
Quality control
Computer-assisted image analysis
Peer reviewed: Yes
Rights: unspecified
Usage restriction: openAccess
Self-archived version: acceptedVersion

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