Suppr超能文献

四种磁共振成像图像质量参数的扫描仪间和扫描仪内差异。

Inter- and intra-scanner variations in four magnetic resonance imaging image quality parameters.

作者信息

Peltonen Juha I, Mäkelä Teemu, Lehmonen Lauri, Sofiev Alexey, Salli Eero

机构信息

University of Helsinki and Helsinki University Hospital, HUS Medical Imaging Center, Radiology, Helsinki, Finland.

University of Helsinki, Department of Physics, Helsinki, Finland.

出版信息

J Med Imaging (Bellingham). 2020 Nov;7(6):065501. doi: 10.1117/1.JMI.7.6.065501. Epub 2020 Dec 4.

Abstract

In addition to less frequent and more comprehensive tests, quality assurance (QA) protocol for a magnetic resonance imaging (MRI) scanner may include cursory daily or weekly phantom checks to verify equipment constancy. With an automatic image analysis workflow, the daily QA images can be further used to study scanner baseline performance and both long- and short-term variations in image quality. With known baselines and variation profiles, automatic error detection can be employed. Four image quality parameters were followed for 17 MRI scanners over six months: signal-to-noise ratio (SNR), image intensity uniformity, ghosting artifact, and geometrical distortions. Baselines and normal variations were determined. An automatic detection of abnormal QA images was compared with image deviations visually detected by human observers. There were significant inter-scanner differences in the QA parameters. In some cases, the results exceeded commonly accepted tolerances. Scanner field strengths, or a unit being stationary versus mobile, did not have a clear relationship with the QA results. The variations and baseline levels of image QA parameters can differ significantly between MRI scanners. Scanner specific error thresholds based on parameter means and standard deviations are a viable option for detecting abnormal QA images.

摘要

除了进行次数较少但更全面的测试外,磁共振成像(MRI)扫描仪的质量保证(QA)协议可能包括每日或每周进行的粗略模体检查,以验证设备的稳定性。通过自动图像分析工作流程,每日QA图像可进一步用于研究扫描仪的基线性能以及图像质量的长期和短期变化。利用已知的基线和变化曲线,可以采用自动错误检测。在六个月的时间里,对17台MRI扫描仪的四个图像质量参数进行了跟踪:信噪比(SNR)、图像强度均匀性、鬼影伪影和几何畸变。确定了基线和正常变化。将自动检测异常QA图像的结果与人类观察者视觉检测到的图像偏差进行了比较。QA参数在不同扫描仪之间存在显著差异。在某些情况下,结果超出了普遍接受的公差范围。扫描仪的场强,或者设备是固定式还是移动式,与QA结果没有明确的关系。MRI扫描仪之间图像QA参数的变化和基线水平可能存在显著差异。基于参数均值和标准差的特定于扫描仪的错误阈值是检测异常QA图像的可行选择。

相似文献

1
Inter- and intra-scanner variations in four magnetic resonance imaging image quality parameters.
J Med Imaging (Bellingham). 2020 Nov;7(6):065501. doi: 10.1117/1.JMI.7.6.065501. Epub 2020 Dec 4.
2
An Automatic Image Processing Workflow for Daily Magnetic Resonance Imaging Quality Assurance.
J Digit Imaging. 2017 Apr;30(2):163-171. doi: 10.1007/s10278-016-9919-4.
3
Quality assurance for MRI: practical experience.
Br J Radiol. 2000 Apr;73(868):376-83. doi: 10.1259/bjr.73.868.10844863.
5
An open source automatic quality assurance (OSAQA) tool for the ACR MRI phantom.
Australas Phys Eng Sci Med. 2015 Mar;38(1):39-46. doi: 10.1007/s13246-014-0311-8. Epub 2014 Nov 21.
6
MRI quality assurance based on 3D FLAIR brain images.
MAGMA. 2018 Dec;31(6):689-699. doi: 10.1007/s10334-018-0699-3. Epub 2018 Aug 17.
7
Sensitivity analysis of different quality assurance methods for magnetic resonance imaging in radiotherapy.
Phys Imaging Radiat Oncol. 2020 Mar 13;13:21-27. doi: 10.1016/j.phro.2020.03.001. eCollection 2020 Jan.
8
Technical Note: Proof of concept for radiomics-based quality assurance for computed tomography.
J Appl Clin Med Phys. 2019 Nov;20(11):199-205. doi: 10.1002/acm2.12750. Epub 2019 Oct 14.
10
Daily QA of linear accelerators using only EPID and OBI.
Med Phys. 2015 Oct;42(10):5584-94. doi: 10.1118/1.4929550.

引用本文的文献

1
2
A Multidimensional Particle Swarm Optimization-Based Algorithm for Brain MRI Tumor Segmentation.
Sensors (Basel). 2025 Apr 29;25(9):2800. doi: 10.3390/s25092800.

本文引用的文献

1
An Automatic Image Processing Workflow for Daily Magnetic Resonance Imaging Quality Assurance.
J Digit Imaging. 2017 Apr;30(2):163-171. doi: 10.1007/s10278-016-9919-4.
2
On replacing the manual measurement of ACR phantom images performed by MRI technologists with an automated measurement approach.
J Magn Reson Imaging. 2016 Apr;43(4):843-52. doi: 10.1002/jmri.25052. Epub 2015 Sep 23.
3
An open source automatic quality assurance (OSAQA) tool for the ACR MRI phantom.
Australas Phys Eng Sci Med. 2015 Mar;38(1):39-46. doi: 10.1007/s13246-014-0311-8. Epub 2014 Nov 21.
4
Fully-automated quality assurance in multi-center studies using MRI phantom measurements.
Magn Reson Imaging. 2014 Jul;32(6):771-80. doi: 10.1016/j.mri.2014.01.017. Epub 2014 Feb 3.
5
MRI quality assurance using the ACR phantom in a multi-unit imaging center.
Acta Oncol. 2011 Aug;50(6):966-72. doi: 10.3109/0284186X.2011.582515.
8
Measurement of MRI scanner performance with the ADNI phantom.
Med Phys. 2009 Jun;36(6):2193-205. doi: 10.1118/1.3116776.
9
Automated analysis of multi site MRI phantom data for the NIHPD project.
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):144-51. doi: 10.1007/11866763_18.
10
A survey of MRI quality assurance programmes.
Br J Radiol. 2006 Jul;79(943):592-6. doi: 10.1259/bjr/67655734.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验