The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort.

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TitleThe impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort.
Publication TypeJournal Article
Year of Publication2016
AuthorsRoalf, DR, Quarmley, M, Elliott, MA, Satterthwaite, TD, Vandekar, SN, Ruparel, K, Gennatas, ED, Calkins, ME, Moore, TM, Hopson, R, Prabhakaran, K, Jackson, CT, Verma, R, Hakonarson, H, Gur, RC, Gur, RE
JournalNeuroimage
Volume125
Pagination903-919
Date Published2016 Jan 15
ISSN1095-9572
KeywordsAdolescent, Area Under Curve, Child, Cohort Studies, Diffusion Tensor Imaging, Female, Humans, Image Interpretation, Computer-Assisted, Male, Neuroimaging, Quality Assurance, Health Care, ROC Curve, Young Adult
Abstract

BACKGROUND: Diffusion tensor imaging (DTI) is applied in investigation of brain biomarkers for neurodevelopmental and neurodegenerative disorders. However, the quality of DTI measurements, like other neuroimaging techniques, is susceptible to several confounding factors (e.g., motion, eddy currents), which have only recently come under scrutiny. These confounds are especially relevant in adolescent samples where data quality may be compromised in ways that confound interpretation of maturation parameters. The current study aims to leverage DTI data from the Philadelphia Neurodevelopmental Cohort (PNC), a sample of 1601 youths with ages of 8-21 who underwent neuroimaging, to: 1) establish quality assurance (QA) metrics for the automatic identification of poor DTI image quality; 2) examine the performance of these QA measures in an external validation sample; 3) document the influence of data quality on developmental patterns of typical DTI metrics.METHODS: All diffusion-weighted images were acquired on the same scanner. Visual QA was performed on all subjects completing DTI; images were manually categorized as Poor, Good, or Excellent. Four image quality metrics were automatically computed and used to predict manual QA status: Mean voxel intensity outlier count (MEANVOX), Maximum voxel intensity outlier count (MAXVOX), mean relative motion (MOTION) and temporal signal-to-noise ratio (TSNR). Classification accuracy for each metric was calculated as the area under the receiver-operating characteristic curve (AUC). A threshold was generated for each measure that best differentiated visual QA status and applied in a validation sample. The effects of data quality on sensitivity to expected age effects in this developmental sample were then investigated using the traditional MRI diffusion metrics: fractional anisotropy (FA) and mean diffusivity (MD). Finally, our method of QA is compared with DTIPrep.RESULTS: TSNR (AUC=0.94) best differentiated Poor data from Good and Excellent data. MAXVOX (AUC=0.88) best differentiated Good from Excellent DTI data. At the optimal threshold, 88% of Poor data and 91% Good/Excellent data were correctly identified. Use of these thresholds on a validation dataset (n=374) indicated high accuracy. In the validation sample 83% of Poor data and 94% of Excellent data was identified using thresholds derived from the training sample. Both FA and MD were affected by the inclusion of poor data in an analysis of an age, sex and race matched comparison sample. In addition, we show that the inclusion of poor data results in significant attenuation of the correlation between diffusion metrics (FA and MD) and age during a critical neurodevelopmental period. We find higher correspondence between our QA method and DTIPrep for Poor data, but we find our method to be more robust for apparently high-quality images.CONCLUSION: Automated QA of DTI can facilitate large-scale, high-throughput quality assurance by reliably identifying both scanner and subject induced imaging artifacts. The results present a practical example of the confounding effects of artifacts on DTI analysis in a large population-based sample, and suggest that estimates of data quality should not only be reported but also accounted for in data analysis, especially in studies of development.

DOI10.1016/j.neuroimage.2015.10.068
Alternate JournalNeuroimage
PubMed ID26520775
PubMed Central IDPMC4753778
Grant ListP50 MH096891 / MH / NIMH NIH HHS / United States
T32 MH065218 / MH / NIMH NIH HHS / United States
P50MH096891 / MH / NIMH NIH HHS / United States
MH089924 / MH / NIMH NIH HHS / United States
R01 MH107235 / MH / NIMH NIH HHS / United States
K23 MH098130 / MH / NIMH NIH HHS / United States
RC2 MH089924 / MH / NIMH NIH HHS / United States
K23MH098130 / MH / NIMH NIH HHS / United States
K01 MH102609 / MH / NIMH NIH HHS / United States
T32MH065218-11 / MH / NIMH NIH HHS / United States
MH089983 / MH / NIMH NIH HHS / United States
R01MH107703 / MH / NIMH NIH HHS / United States
R01 MH107703 / MH / NIMH NIH HHS / United States
RC2 MH089983 / MH / NIMH NIH HHS / United States
K01MH102609 / MH / NIMH NIH HHS / United States
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