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.
Highlights
Diffusion tensor imaging (DTI) is a magnetic resonance (MR) imaging technique that provides contrasts uniquely sensitive to intra-voxel tissue microarchitecture on a scale of tens of microns [1]
To address the concerns of time delays and limited access to statistical methods for improved quality assurance (QA) of DTI data, we present an in situ quality analysis pipeline
3.5 Low Dimensional Analysis of QA Metrics We evaluate if stored quality metrics from the pipeline capture important characteristics of DTI data through qualitative analysis of study clustering (Figure 5)
Summary
Diffusion tensor imaging (DTI) is a magnetic resonance (MR) imaging technique that provides contrasts uniquely sensitive to intra-voxel tissue microarchitecture on a scale of tens of microns [1]. A DTI experiment can consist of up to 90 or more volumes, be aggressive on hardware gradients, and be susceptible to standard as well as unique artifacts [2]; high data quality is difficult to maintain. DTI data quality analysis is exceedingly difficult. The processing of DTI data involves mapping data to a logarithmic diffusion model which is well-known to induce bias in measured parameters [3,4]. Because of these challenges, DTI data quality analysis is usually reserved as a separate step post data collection, reducing the chances of an experimental response to poor data quality. The documented challenges of producing accurate and biophysically meaningful metrics from a DTI experiment suggest DTI is an important target for further QA development
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