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Medical-image Analysis and Statistical Interpretation Lab

Simultaneous Analysis and Quality Assurance for Diffusion Tensor Imaging.

Posted by on Monday, April 15, 2013 in Diffusion Weighted MRI, Machine Learning, Neuroimaging.

Carolyn B. Lauzon, Andrew J. Asman, Michael L. Esparza, Scott S. Burns, Qiuyun Fan, Yurui Gao, Adam W. Anderson, Nicole Davis, Laurie E. Cutting, Bennett A. Landman. “Simultaneous Analysis and Quality Assurance for Diffusion Tensor Imaging.” PLoS ONE. 2013 Apr 30;8(4) PMC23637895 †

Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3640065/

 

Abstract

Diffusion tensor imaging (DTI) enables non-invasive, cyto-architectural mapping of in vivo tissue microarchitecture through voxel-wise mathematical modeling of multiple magnetic resonance imaging (MRI) acquisitions, each differently sensitized to water diffusion. DTI computations are fundamentally estimation processes and are sensitive to noise and artifacts. Despite widespread adoption in the neuroimaging community, maintaining consistent DTI data quality remains challenging given the propensity for patient motion, artifacts associated with fast imaging techniques, and the possibility of hardware changes/failures. Furthermore, the quantity of data acquired per voxel, the non-linear estimation process, and numerous potential use cases complicate traditional visual data inspection approaches. Currently, quality inspection of DTI data has relied on visual inspection and individual processing in DTI analysis software programs (e.g. DTIPrep, DTI-studio). However, recent advances in applied statistical methods have yielded several different metrics to assess noise level, artifact propensity, quality of tensor fit, variance of estimated measures, and bias in estimated measures. To date, these metrics have been largely studied in isolation. Herein, we select complementary metrics for integration into an automatic DTI analysis and quality assurance pipeline. The pipeline completes in 24 hours, stores statistical outputs, and produces a graphical summary quality analysis (QA) report. We assess the utility of this streamlined approach for empirical quality assessment on 608 DTI datasets from pediatric neuroimaging studies. The efficiency and accuracy of quality analysis using the proposed pipeline is compared with quality analysis based on visual inspection. The unified pipeline is found to save a statistically significant amount of time (over 70%) while improving the consistency of QA between a DTI expert and a pool of research associates. Projection of QA metrics to a low dimensional manifold reveal qualitative, but clear, QA-study associations and suggest that automated outlier/anomaly detection would be feasible.

Each of the 567 DTI datasets was characterized by a 112 element vector of stored outputs from the pipeline. PCA analysis was performed on the resulting data. (A) DTI dataset locations in the first two dimensions of the PCA analysis. Data is symbolized by study Roman numeral (Table 1). Single arrow points to a data quality outlier from study I; subject 3 in Figure 4 and in (B). A double headed arrow points to a cluster representing an isolated protocol sub-group from study VI. (B) FA maps from similar sagittal slice locations in two subjects from Study I. Subject 4 is the indicated outlier in (A) and subject 5 was selected from the center of the study I cluster seen in (A).
Each of the 567 DTI datasets was characterized by a 112 element vector of stored outputs from the pipeline. PCA analysis was performed on the resulting data. (A) DTI dataset locations in the first two dimensions of the PCA analysis. Data is symbolized by study Roman numeral (Table 1). Single arrow points to a data quality outlier from study I; subject 3 in Figure 4 and in (B). A double headed arrow points to a cluster representing an isolated protocol sub-group from study VI. (B) FA maps from similar sagittal slice locations in two subjects from Study I. Subject 4 is the indicated outlier in (A) and subject 5 was selected from the center of the study I cluster seen in (A).