TY - JOUR
T1 - Multisite concordance of apparent diffusion coefficient measurements across the NCI quantitative imaging network
AU - Newitt, David C.
AU - Malyarenko, Dariya
AU - Chenevert, Thomas L.
AU - Quarles, C. Chad
AU - Bell, Laura
AU - Fedorov, Andriy
AU - Fennessy, Fiona
AU - Jacobs, Michael A.
AU - Solaiyappan, Meiyappan
AU - Hectors, Stefanie
AU - Taouli, Bachir
AU - Muzi, Mark
AU - Kinahan, Paul E.
AU - Schmainda, Kathleen M.
AU - Prah, Melissa A.
AU - Taber, Erin N.
AU - Kroenke, Christopher
AU - Huang, Wei
AU - Arlinghaus, Lori R.
AU - Yankeelov, Thomas E.
AU - Cao, Yue
AU - Aryal, Madhava
AU - Yen, Yi Fen
AU - Kalpathy-Cramer, Jayashree
AU - Shukla-Dave, Amita
AU - Fung, Maggie
AU - Liang, Jiachao
AU - Boss, Michael
AU - Hylton, Nola
N1 - Publisher Copyright:
© The Authors. The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
PY - 2018
Y1 - 2018
N2 - Diffusion weighted MRI has become ubiquitous in many areas of medicine, including cancer diagnosis and treatment response monitoring. Reproducibility of diffusion metrics is essential for their acceptance as quantitative biomarkers in these areas. We examined the variability in the apparent diffusion coefficient (ADC) obtained from both postprocessing software implementations utilized by the NCI Quantitative Imaging Network and online scan time-generated ADC maps. Phantom and in vivo breast studies were evaluated for two (ADC2) and four (ADC4) b-value diffusion metrics. Concordance of the majority of implementations was excellent for both phantom ADC measures and in vivo ADC2, with relative biases <0.1% (ADC2) and <0.5% (phantom ADC4) but with higher deviations in ADC at the lowest phantom ADC values. In vivo ADC4 concordance was good, with typical biases of ±2% to 3% but higher for online maps. Multiple b-value ADC implementations were separated into two groups determined by the fitting algorithm. Intergroup mean ADC differences ranged from negligible for phantom data to 2.8% for ADC4 in vivo data. Some higher deviations were found for individual implementations and online parametric maps. Despite generally good concordance, implementation biases in ADC measures are sometimes significant and may be large enough to be of concern in multisite studies.
AB - Diffusion weighted MRI has become ubiquitous in many areas of medicine, including cancer diagnosis and treatment response monitoring. Reproducibility of diffusion metrics is essential for their acceptance as quantitative biomarkers in these areas. We examined the variability in the apparent diffusion coefficient (ADC) obtained from both postprocessing software implementations utilized by the NCI Quantitative Imaging Network and online scan time-generated ADC maps. Phantom and in vivo breast studies were evaluated for two (ADC2) and four (ADC4) b-value diffusion metrics. Concordance of the majority of implementations was excellent for both phantom ADC measures and in vivo ADC2, with relative biases <0.1% (ADC2) and <0.5% (phantom ADC4) but with higher deviations in ADC at the lowest phantom ADC values. In vivo ADC4 concordance was good, with typical biases of ±2% to 3% but higher for online maps. Multiple b-value ADC implementations were separated into two groups determined by the fitting algorithm. Intergroup mean ADC differences ranged from negligible for phantom data to 2.8% for ADC4 in vivo data. Some higher deviations were found for individual implementations and online parametric maps. Despite generally good concordance, implementation biases in ADC measures are sometimes significant and may be large enough to be of concern in multisite studies.
KW - Apparent diffusion coefficient
KW - Breast MRI.
KW - Reproducibility
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U2 - 10.1117/1.JMI.5.1.011003
DO - 10.1117/1.JMI.5.1.011003
M3 - Article
C2 - 29021993
AN - SCOPUS:85032566078
SN - 2329-4302
VL - 5
JO - Journal of Medical Imaging
JF - Journal of Medical Imaging
IS - 1
M1 - 011003
ER -