Literature DB >> 27834027

An Automatic Image Processing Workflow for Daily Magnetic Resonance Imaging Quality Assurance.

Juha I Peltonen1,2, Teemu Mäkelä3,4, Alexey Sofiev3,4, Eero Salli3.   

Abstract

The performance of magnetic resonance imaging (MRI) equipment is typically monitored with a quality assurance (QA) program. The QA program includes various tests performed at regular intervals. Users may execute specific tests, e.g., daily, weekly, or monthly. The exact interval of these measurements varies according to the department policies, machine setup and usage, manufacturer's recommendations, and available resources. In our experience, a single image acquired before the first patient of the day offers a low effort and effective system check. When this daily QA check is repeated with identical imaging parameters and phantom setup, the data can be used to derive various time series of the scanner performance. However, daily QA with manual processing can quickly become laborious in a multi-scanner environment. Fully automated image analysis and results output can positively impact the QA process by decreasing reaction time, improving repeatability, and by offering novel performance evaluation methods. In this study, we have developed a daily MRI QA workflow that can measure multiple scanner performance parameters with minimal manual labor required. The daily QA system is built around a phantom image taken by the radiographers at the beginning of day. The image is acquired with a consistent phantom setup and standardized imaging parameters. Recorded parameters are processed into graphs available to everyone involved in the MRI QA process via a web-based interface. The presented automatic MRI QA system provides an efficient tool for following the short- and long-term stability of MRI scanners.

Entities:  

Keywords:  Image processing; MR imaging; Quality assurance; Quality control

Mesh:

Year:  2017        PMID: 27834027      PMCID: PMC5359204          DOI: 10.1007/s10278-016-9919-4

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  17 in total

1.  Effectiveness and relevance of MR acceptance testing: results of an 8 year audit.

Authors:  D W McRobbie; R A Quest
Journal:  Br J Radiol       Date:  2002-06       Impact factor: 3.039

2.  MRI quality control: six imagers studied using eleven unified image quality parameters.

Authors:  T Ihalainen; O Sipilä; S Savolainen
Journal:  Eur Radiol       Date:  2004-03-03       Impact factor: 5.315

3.  [(A tentative number) JIS Z 4952-magnetic resonance equipment for medical imaging-part 1: determination of essential image quality parameters].

Authors:  Nobuo Sunohara
Journal:  Nihon Hoshasen Gijutsu Gakkai Zasshi       Date:  2010-07-20

4.  A survey of MRI quality assurance programmes.

Authors:  C J Koller; J P Eatough; P J Mountford; G Frain
Journal:  Br J Radiol       Date:  2006-07       Impact factor: 3.039

5.  Measurement of signal-to-noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters.

Authors:  Olaf Dietrich; José G Raya; Scott B Reeder; Maximilian F Reiser; Stefan O Schoenberg
Journal:  J Magn Reson Imaging       Date:  2007-08       Impact factor: 4.813

6.  Automating quality assurance for digital radiography.

Authors:  Bruce I Reiner
Journal:  J Am Coll Radiol       Date:  2009-07       Impact factor: 5.532

7.  MRI quality assurance using the ACR phantom in a multi-unit imaging center.

Authors:  Toni M Ihalainen; Nadja T Lönnroth; Juha I Peltonen; Jouni K Uusi-Simola; Marjut H Timonen; Linda J Kuusela; Sauli E Savolainen; Outi E Sipilä
Journal:  Acta Oncol       Date:  2011-08       Impact factor: 4.089

8.  Automating PACS Quality Control with the Vanderbilt Image Processing Enterprise Resource.

Authors:  Michael L Esparza; E Brian Welch; Bennett A Landman
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2012-02-12

9.  Measurement of MRI scanner performance with the ADNI phantom.

Authors:  Jeffrey L Gunter; Matt A Bernstein; Brett J Borowski; Chadwick P Ward; Paula J Britson; Joel P Felmlee; Norbert Schuff; Michael Weiner; Clifford R Jack
Journal:  Med Phys       Date:  2009-06       Impact factor: 4.071

10.  Quality control of CT systems by automated monitoring of key performance indicators: a two-year study.

Authors:  Patrik Nowik; Robert Bujila; Gavin Poludniowski; Annette Fransson
Journal:  J Appl Clin Med Phys       Date:  2015-07-08       Impact factor: 2.102

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  6 in total

1.  Wide slab is useful for routine quality control of MRI slice thickness.

Authors:  Yoshiyuki Ishimori; Masahiko Monma; Hiraku Kawamura
Journal:  Radiol Phys Technol       Date:  2018-06-19

2.  MRI quality assurance based on 3D FLAIR brain images.

Authors:  Juha I Peltonen; Teemu Mäkelä; Eero Salli
Journal:  MAGMA       Date:  2018-08-17       Impact factor: 2.310

3.  Improvement in MR quality control workflow and outcomes with a web-based database.

Authors:  Xiangyu Yang; Kevin Little; Xia Jiang; David Hintenlang
Journal:  J Appl Clin Med Phys       Date:  2020-04-19       Impact factor: 2.102

4.  Factors influencing daily quality assurance measurements of magnetic resonance imaging scanners.

Authors:  Nana Owusu; Vincent A Magnotta
Journal:  Radiol Phys Technol       Date:  2021-10-08

5.  Magnetic Resonance Imaging Image Segmentation under Edge Detection Intelligent Algorithm in Diagnosis of Surgical Wrist Joint Injuries.

Authors:  Zhongyi Li; Xi Ji
Journal:  Contrast Media Mol Imaging       Date:  2021-10-01       Impact factor: 3.161

6.  A consistency evaluation of signal-to-noise ratio in the quality assessment of human brain magnetic resonance images.

Authors:  Shaode Yu; Guangzhe Dai; Zhaoyang Wang; Leida Li; Xinhua Wei; Yaoqin Xie
Journal:  BMC Med Imaging       Date:  2018-05-16       Impact factor: 1.930

  6 in total

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