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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Research Article

Automatic Analysis of ACR Phantom Images in MRI

Author(s): Ines Ben Alaya* and Mokhtar Mars

Volume 16, Issue 7, 2020

Page: [892 - 901] Pages: 10

DOI: 10.2174/1573405615666190903145343

Price: $65

Abstract

Background: Quality Assurance (QA) of Magnetic Resonance Imaging (MRI) system is an essential step to avoid problems in diagnosis when image quality is low. It is considered a patient safety issue. The accreditation program of the American College of Radiology (ACR) includes a standardized image quality measurement protocol. However, it has been shown that human testing by visual inspection is not objective and not reproducible.

Methods: The overall goal of the present paper was to develop and implement a fully automated method for accurate image analysis to increase its objectivity. It can positively impact the QA process by decreasing the reaction time, improving repeatability, and by reducing operator dependency. The proposed QA procedures were applied to ten clinical MRI scanners. The performance of the automated procedure was assessed by comparing the test results with the decisions made by trained MRI technologists according to ACR guidelines. The p-value, correlation coefficient of the manual and automatic measurements were also computed using the Pearson test.

Results and Conclusion: Compared to the manual process, the use of the proposed approach can significantly reduce the time requirements while maintaining consistency with manual measurements and furthermore, decrease the subjectivity of the results. Accordingly, a strong correlation was found and the corresponding p-value was much lower than the significance level of 0.05 indicating a good agreement between the two measurements.

Keywords: Magnetic resonance imaging, quality control, ACR MRI phantom, MRI image quality, image processing, medical imaging.

Graphical Abstract

[1]
Haacke EM, Brown RW, Thompson MR, Venkatesan R. Magnetic resonance imaging: physical principles and sequence design. New York: Wiley-Liss 1999.
[2]
Price RR, Axel L, Morgan T, et al. Quality assurance methods and phantoms for magnetic resonance imaging: report of AAPM nuclear magnetic resonance task group no. 1. Med Phys 1990; 17(2): 287-95.
[http://dx.doi.org/10.1118/1.596566] [PMID: 2333055]
[3]
Sobol W. NessAiver M, Orton C. Point / counter point the physics components of the ACR MRI accreditation program are overly tedious and beyond what is needed to ensure good patient care. Med Phys 2008; 35(8): 3419-21.
[4]
Kaljuste D, Nigul M. Evaluation of the ACR MRI phantom for quality assurance tests of 1.5 T MRI scanners in Estonian hospitals. Proc Estonian Acad Sci 2014; 63(3): 328-4.
[http://dx.doi.org/10.3176/proc.2014.3.06]
[5]
Nowik P, Bujila R, Poludniowski G, Fransson A. Quality control of CT systems by automated monitoring of key performance indicators: a two-year study. J Appl Clin Med Phys 2015; 16(4): 254-65.
[http://dx.doi.org/10.1120/jacmp.v16i4.5469] [PMID: 26219012]
[6]
DiFilippo FP. Technical note: automated quantitative analysis of planar scintigraphic resolution with the ACR SPECT phantom. Med Phys 2018; 45(3): 1118-22.
[http://dx.doi.org/10.1002/mp.12779] [PMID: 29385653]
[7]
Mortamet B, Bernstein MA, Jack CR Jr, et al. Automatic quality assessment in structural brain magnetic resonance imaging. Magn Reson Med 2009; 62(2): 365-72.
[http://dx.doi.org/10.1002/mrm.21992] [PMID: 19526493]
[8]
Gedamu EL, Collins DL, Arnold DL. Automated quality control of brain MR images. J Magn Reson Imaging 2008; 28(2): 308-19.
[http://dx.doi.org/10.1002/jmri.21434] [PMID: 18666143]
[9]
Fu L, Fonov V, Pike B, Evans AC, Collins DL. Automated analysis of multi site MRI phantom data for the NIHPD project 2006. Medical image computing and computer-assisted intervention; 2006 Oct 1-6; Copenhagen, Denmark. Berlin: Springer; pp 144-51.
[http://dx.doi.org/10.1007/11866763_18]
[10]
Huff SJ. Software development and automation of the American College of Radiology MR phantom testing accreditation criteria for the general electric magnetic resonance imaging whole body scanner. MSE dissertation. Milwaukee School of Engineering. 2003.
[11]
Gardner EA, Ellis JH, Hyde RJ, Aisen AM, Quint DJ, Carson PL. Detection of degradation of magnetic resonance (MR) images: comparison of an automated MR image-quality analysis system with trained human observers. Acad Radiol 1995; 2(4): 277-81.
[http://dx.doi.org/10.1016/S1076-6332(05)80184-9] [PMID: 9419562]
[12]
Peltonen JI, Mäkelä T, Sofiev A, Salli E. An automatic image processing workflow for daily magnetic resonance imaging quality assurance. J Digit Imaging 2017; 30(2): 163-71.
[http://dx.doi.org/10.1007/s10278-016-9919-4] [PMID: 27834027]
[13]
Fitzpatrick A. Automated quality assurance for magnetic resonance image with extensions to diffusion tensor imaging by automated quality assurance for magnetic resonance image with extensions to diffusion tensor imaging. Masters dissertation. Virginia Polytechnic Institute and State University 2005.
[14]
Panych LP, Chiou JY, Qin L, Kimbrell VL, Bussolari L, Mulkern RV. On replacing the manual measurement of ACR phantom images performed by MRI technologists with an automated measurement approach. J Magn Reson Imaging 2016; 43(4): 843-52.
[http://dx.doi.org/10.1002/jmri.25052] [PMID: 26395366]
[15]
Magnotta VA, Friedman L. Measurement of signal-to-noise and contrast-to-noise in the fBIRN multicenter imaging study. J Digit Imaging 2006; 19(2): 140-7.
[http://dx.doi.org/10.1007/s10278-006-0264-x] [PMID: 16598643]
[16]
Mulkern RV, Forbes P, Dewey K, et al. Establishment and results of a magnetic resonance quality assurance program for the pediatric brain tumor consortium. Acad Radiol 2008; 15(9): 1099-110.
[http://dx.doi.org/10.1016/j.acra.2008.04.004] [PMID: 18692750]
[17]
ACR guidelines help sites resume non-urgent care. American College of Radiology. Available from: www.acr.org
[18]
Weinreb J, Wilcox PA, Hayden J, Lewis R, Froelich J. ACR MRI accreditation: yesterday, today, and tomorrow. J Am Coll Radiol 2005; 2(6): 494-503.
[http://dx.doi.org/10.1016/j.jacr.2004.11.004] [PMID: 17411866]
[19]
Ihalainen TM, Lönnroth NT, Peltonen JI, et al. MRI quality assurance using the ACR phantom in a multi-unit imaging center. Acta Oncol 2011; 50(6): 966-72.
[http://dx.doi.org/10.3109/0284186X.2011.582515] [PMID: 21767198]
[20]
Och JG, Clarke GD, Sobol WT, Rosen CW, Mun SK. Acceptance testing of magnetic resonance imaging systems: report of AAPM nuclear magnetic resonance task group no. 6. Med Phys 1992; 19(1): 217-29.
[http://dx.doi.org/10.1118/1.596903] [PMID: 1620053]
[21]
Phantom Test Guidance for the ACR MRI Accreditation program, Reston. Virginia, US, Tech Rep 2006.
[22]
Khandelwal M, Shirsagar S, Rawat P. MRI image segmentation using thresholding with 3-class C-means clustering. Proceedings of the 2nd International Conference on Inventive Systems and Control (ICISC) 2018 Jan19-20; Coimbatore, India New York: IEEE 2018

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