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An open source automatic quality assurance (OSAQA) tool for the ACR MRI phantom

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Abstract

Routine quality assurance (QA) is necessary and essential to ensure MR scanner performance. This includes geometric distortion, slice positioning and thickness accuracy, high contrast spatial resolution, intensity uniformity, ghosting artefact and low contrast object detectability. However, this manual process can be very time consuming. This paper describes the development and validation of an open source tool to automate the MR QA process, which aims to increase physicist efficiency, and improve the consistency of QA results by reducing human error. The OSAQA software was developed in Matlab and the source code is available for download from http://jidisun.wix.com/osaqa-project/. During program execution QA results are logged for immediate review and are also exported to a spreadsheet for long-term machine performance reporting. For the automatic contrast QA test, a user specific contrast evaluation was designed to improve accuracy for individuals on different display monitors. American College of Radiology QA images were acquired over a period of 2 months to compare manual QA and the results from the proposed OSAQA software. OSAQA was found to significantly reduce the QA time from approximately 45 to 2 min. Both the manual and OSAQA results were found to agree with regard to the recommended criteria and the differences were insignificant compared to the criteria. The intensity homogeneity filter is necessary to obtain an image with acceptable quality and at the same time keeps the high contrast spatial resolution within the recommended criterion. The OSAQA tool has been validated on scanners with different field strengths and manufacturers. A number of suggestions have been made to improve both the phantom design and QA protocol in the future.

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References

  1. Aisen AM, Martel W, Braunstein EM, McMillin KI, Phillips WA, Kling TF (1986) MRI and CT evaluation of primary bone and soft-tissue tumors. Am J Roentgenol 146:749–756

    Article  CAS  Google Scholar 

  2. Price RR, Axel L, Morgan T, Newman R, Perman W, Schneiders N et al (1990) Quality assurance methods and phantoms for magnetic resonance imaging: report of AAPM nuclear magnetic resonance task group no. 1. Med Phys 17:287

    Article  CAS  PubMed  Google Scholar 

  3. Lerski RA, De Certaines JD (1993) II. Performance assessment and quality control in MRI by Eurospin test objects and protocols. Magn Reson Imaging 11:817–833

    Article  CAS  PubMed  Google Scholar 

  4. Phantom test guidance for the ACR MRI accreditation program, 2005

  5. Acceptance testing and quality assurance procedures for magnetic resonance imaging facilities, Report of MR subcommittee task group 1, AAPM report No. 100, 2010

  6. Gunter JL, Bernstein MA, Borowski BJ, Ward CP, Britson PJ, Felmlee JP et al (2009) Measurement of MRI scanner performance with the ADNI phantom. Med Phys 36:2193–2205

    Article  PubMed Central  PubMed  Google Scholar 

  7. Ihalainen T, Sipilä O, Savolainen S (2004) MRI quality control: six imagers studied using eleven unified image quality parameters. Eur Radiol 14:1859–1865

    Article  CAS  PubMed  Google Scholar 

  8. Ihalainen TM, Lönnroth NT, Peltonen JI, Uusi-Simola JK, Timonen MH, Kuusela LJ et al (2011) MRI quality assurance using the ACR phantom in a multi-unit imaging center. Acta Oncol 50(6):966–972

    Article  PubMed  Google Scholar 

  9. Mulkern RV, Forbes P, Dewey K, Osganian S, Clark M, Wong S et al (2008) Establishment and results of a magnetic resonance quality assurance program for the pediatric brain tumor consortium. Acad Radiol 15:1099–1111

    Article  PubMed Central  PubMed  Google Scholar 

  10. Fu L, Fonov V, Pike B, Evans AC, Collins DL (2006) Automated analysis of multi site MRI phantom data for the NIHPD project. Med Image Comput Comput Interv 2006:144–151

    Google Scholar 

  11. Site scanning instructions for use of the MR phantom for the ACR MRI accreditation program, 2000

  12. Huff SJ (2003) 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

  13. Fitzpatrick A (2005) Automated quality assurance for magnetic resonance image with extensions to diffusion tensor imaging

  14. Panych LP, Bussolari L, Mulkern RV (2010) Automated analysis of ACR phantom data as an adjunct to a regular MR quality assurance program. Int Soc Magn Reson Med 5071

  15. Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675

    Article  CAS  PubMed  Google Scholar 

  16. Tofts P (1994) Standing waves in uniform water phantoms. J Magn Reson 104:143–147

    Article  CAS  Google Scholar 

  17. Müller-Gärtner H, Links J, Prince J, Bryan R, McVeigh E, Leal J et al (1992) Measurement of radiotracer concentration in brain gray matter using positron emission tomography: MRI-based correction for partial volume effects. J Cereb Blood Flow Metab 12:571–583

    Article  PubMed  Google Scholar 

  18. Shattuck D, Sandor-Leahy S, Schaper K, Rottenberg D, Leahy R (2001) Magnetic resonance image tissue classification using a partial volume model. Neuroimage 13:856–876

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

This work was supported by Cancer Council New South Wales Research Grant RG11-05.

Conflict of interest

Authors declare no conflict of interest.

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Authors and Affiliations

  1. University of Newcastle, Newcastle, NSW, Australia

    Jidi Sun, Fred Menk, Peter Stanwell & Peter B. Greer

  2. Calvary Mater Newcastle, Newcastle, NSW, Australia

    Michael Barnes & Peter B. Greer

  3. CSIRO Australian E-Health Research Centre, Brisbane, QLD, Australia

    Jason Dowling

Authors
  1. Jidi Sun
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  2. Michael Barnes
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  3. Jason Dowling
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  4. Fred Menk
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  5. Peter Stanwell
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  6. Peter B. Greer
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Correspondence to Jidi Sun.

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Sun, J., Barnes, M., Dowling, J. et al. An open source automatic quality assurance (OSAQA) tool for the ACR MRI phantom. Australas Phys Eng Sci Med 38, 39–46 (2015). https://doi.org/10.1007/s13246-014-0311-8

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  • DOI: https://doi.org/10.1007/s13246-014-0311-8

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