RESUMEN

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.

Asunto(s)

Imagen de Difusión Tensora/métodos , Algoritmos , Niño , Humanos , Análisis de Componente Principal , Control de Calidad , Programas Informáticos

RESUMEN

Precise image acquisition is an integral part of modern patient care and medical imaging research. Periodic quality control using standardized protocols and phantoms ensures that scanners are operating according to specifications, yet such procedures do not ensure that individual datasets are free from corruption-for example due to patient motion, transient interference, or physiological variability. If unacceptable artifacts are noticed during scanning, a technologist can repeat a procedure. Yet, substantial delays may be incurred if a problematic scan is not noticed until a radiologist reads the scans or an automated algorithm fails. Given scores of slices in typical three-dimensional scans and wide-variety of potential use cases, a technologist cannot practically be expected inspect all images. In large-scale research, automated pipeline systems have had great success in achieving high throughput. However, clinical and institutional workflows are largely based on DICOM and PACS technologies; these systems are not readily compatible with research systems due to security and privacy restrictions. Hence, quantitative quality control has been relegated to individual investigators and too often neglected. Herein, we propose a scalable system, the Vanderbilt Image Processing Enterprise Resource-VIPER, to integrate modular quality control and image analysis routines with a standard PACS configuration. This server unifies image processing routines across an institutional level and provides a simple interface so that investigators can collaborate to deploy new analysis technologies. VIPER integrates with high performance computing environments has successfully analyzed all standard scans from our institutional research center over the course of the last 18 months.