Standardization, Phantoms
Kathryn Keenan1

1NIST, United States

Synopsis

This talk will review how to design a phantom for MSK applications, how to develop standardization across platforms, and considerations for reproducibility and reliability studies.

Purpose

This talk will review how to design a phantom for MSK applications, how to develop standardization across platforms, and considerations for reproducibility and reliability studies.

Methods

We will review available phantoms and consider designs for a novel MSK phantom using methods ranging from 3D printing to paramagnetically-doped agarose gels. The talk will also highlight best practices for standardization of protocols [1].

Discussion

To guide the MSK community towards consensus protocols and documentation such as that for a Quantitative Imaging Biomarker Alliance Profile [2-4], phantoms and standardized protocols need to be developed. In addition, reproducibility and reliability studies are required. To conclude, we will review the attributes of high quality reproducibility and reliability studies [5-9] and present the measurement certainty that results. If time allows, we will also consider quantification of measurement uncertainty [10-11].

Acknowledgements

The NIST Magnetic Imaging Group and attendees at the 2014 and 2017 NIST workshops.

References

[1] Keenan KE, Ainslie M, Barker AJ, Boss MA, Cecil KM, Charles C, Chenevert TL, Clarke L, Evelhoch JL, Finn P, Gembris D, Gunter JL, Hill DLG, Jack CR Jr, Jackson EF, Liu G, Russek SE, Sharma SD, Steckner M, Stupic KF, Trzasko JD, Yuan C, Zheng J. Quantitative magnetic resonance imaging phantoms: A review and the need for a system phantom. Magn Reson Med 20018;79(1):48-61.

[2] Kessler LG, Barnhart HX, Buckler AJ, Choudhury KR, Kondratovich MV, Toledano A, Guimaraes AR, Filice R, Zhang Z, Sullivan DC, QIBA Terminology Working Group. The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions. Statistical methods in medical research. 2014. doi: 10.1177/0962280214537333. PubMed PMID: 24919826

[3] Raunig DL, McShane LM, Pennello G, Gatsonis C, Carson PL, Voyvodic JT, Wahl RL, Kurland BF, Schwarz AJ, Gonen M, Zahlmann G, Kondratovich M, O’Donnell K, Petrick N, Cole PE, Garra B, Sullivan DC, QIBA Technical Performance Working Group. Quantitative imaging biomarkers: A review of statistical methods for technical performance assessment. Statistical methods in medical research. 2014. doi: 10.1177/0962280214537344. PubMed PMID: 24919831.

[4] RSNA-QIBA Profiles http://qibawiki.rsna.org/index.php/Profiles

[5] Shire NJ, Yin M, Chen J, Railkar RA, Fox-Bosetti S, Johnson SM, Beals CR, Dardzinski BJ, Sanderson SO, Talwalker JA, Ehman RL. Test-retest repeatability of MR elastography for noninvasive liver fibrosis assessment in hepatitis C. JMRI. 2011;34(4):947-955.

[6] Hines CDG, Bley TA, Lindstrom MJ, Reeder SB. Repeatability of magnetic resonance elastography for quantification of hepatic stiffness. JMRI. 2010;31(3):725-731.

[7] Wang QB, Zhu H, Liu HL, Zhang B. Performance of magnetic resonance elastography and diffusion-weighted imaging for the staging of hepatic fibrosis: A meta-analysis. Hepatology. 2012. Doi: 10.1002/hep.25610

[8] Zivadinov R, Grop A, Sharma J, et. al. Reproducibility and accuracy of quantitative magnetic resonance imaging techniques of whole-brain atrophy measurement in multiple sclerosis. Journal of Neuroimaging. 5: 27—36. doi:10.1111/j.1552-6569.2005.tb00282.x.

[9] Stikov N, Boudreau M, Levesque I,Tardif C, Barral JK, Pike GB. On the accuracy of T1 mapping: Searching for common ground. Magnetic Resonance in Medicine. 2014; 73:514—522. doi: 10.1002/mrm.25135.

[10] Joint Committee for Guides in Metrology (JCGM). Evaluation of measurement data---Supplement 1 to the “Guide to the expression of uncertainty in measurement”---Propagation of distributions using a Monte Carlo method. 2008. https://www.bipm.org/utils/common/documents/jcgm/JCGM_101_2008_E.pdf

[11] Bretthorst GL. How accurately can parameters from exponential models be estimated? A Bayesian view. 27A: 73–83. doi:10.1002/cmr.a.20044.

Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)