Literature DB >> 10619255

Automatic quality assessment protocol for MRI equipment.

P Bourel1, D Gibon, E Coste, V Daanen, J Rousseau.   

Abstract

The authors have developed a protocol and software for the quality assessment of MRI equipment with a commercial test object. Automatic image analysis consists of detecting surfaces and objects, defining regions of interest, acquiring reference point coordinates and establishing gray level profiles. Signal-to-noise ratio, image uniformity, geometrical distortion, slice thickness, slice profile, and spatial resolution are checked. The results are periodically analyzed to evaluate possible drifts with time. The measurements are performed weekly on three MRI scanners made by the Siemens Company (VISION 1.5T, EXPERT 1.0T, and OPEN 0.2T). The results obtained for the three scanners over approximately 3.5 years are presented, analyzed, and compared.

Mesh:

Year:  1999        PMID: 10619255     DOI: 10.1118/1.598809

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  10 in total

1.  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

2.  Quality assurance of clinical MRI scanners using ACR MRI phantom: preliminary results.

Authors:  Chien-Chuan Chen; Yung-Liang Wan; Yau-Yau Wai; Ho-Ling Liu
Journal:  J Digit Imaging       Date:  2004-12       Impact factor: 4.056

3.  Automated quality assurance routines for fMRI data applied to a multicenter study.

Authors:  Tony Stöcker; Frank Schneider; Martina Klein; Ute Habel; Thilo Kellermann; Karl Zilles; N Jon Shah
Journal:  Hum Brain Mapp       Date:  2005-06       Impact factor: 5.038

4.  Setting up MR compatibility of a commercial stereo-localization system for low-field open MR interventional procedures.

Authors:  Romain Viard; Maximilien Vermandel; Jean Rousseau
Journal:  Int J Comput Assist Radiol Surg       Date:  2008-10-28       Impact factor: 2.924

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

Authors:  Juha I Peltonen; Teemu Mäkelä; Alexey Sofiev; Eero Salli
Journal:  J Digit Imaging       Date:  2017-04       Impact factor: 4.056

6.  Automated quality assessment of structural magnetic resonance images in children: Comparison with visual inspection and surface-based reconstruction.

Authors:  Tonya White; Philip R Jansen; Ryan L Muetzel; Gustavo Sudre; Hanan El Marroun; Henning Tiemeier; Anqi Qiu; Philip Shaw; Andrew M Michael; Frank C Verhulst
Journal:  Hum Brain Mapp       Date:  2017-12-05       Impact factor: 5.038

7.  Effect of a prescan patient-radiologist encounter on functional MR image quality.

Authors:  S Y Mahmoud; M Ahmed; T M Emch; P Masood; D Moon; M D Phillips; P M Ruggieri; A S Smith; T W Stultz; A L Tievsky; S E Jones
Journal:  AJNR Am J Neuroradiol       Date:  2010-08-12       Impact factor: 3.825

8.  Automatic quality assessment in structural brain magnetic resonance imaging.

Authors:  Bénédicte Mortamet; Matt A Bernstein; Clifford R Jack; Jeffrey L Gunter; Chadwick Ward; Paula J Britson; Reto Meuli; Jean-Philippe Thiran; Gunnar Krueger
Journal:  Magn Reson Med       Date:  2009-08       Impact factor: 4.668

9.  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

10.  Histogram-based normalization technique on human brain magnetic resonance images from different acquisitions.

Authors:  Xiaofei Sun; Lin Shi; Yishan Luo; Wei Yang; Hongpeng Li; Peipeng Liang; Kuncheng Li; Vincent C T Mok; Winnie C W Chu; Defeng Wang
Journal:  Biomed Eng Online       Date:  2015-07-28       Impact factor: 2.819

  10 in total

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