Toward Precision and Reproducibility of Diffusion Tensor Imaging: A Multicenter Diffusion Phantom and Traveling Volunteer Study

BACKGROUND AND PURPOSE: Precision medicine is an approach to disease diagnosis, treatment, and prevention that relies on quantitative biomarkers that minimize the variability of individual patient measurements. The aim of this study was to assess the intersite variability after harmonization of a high-angular-resolution 3T diffusion tensor imaging protocol across 13 scanners at the 11 academic medical centers participating in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury multisite study. MATERIALS AND METHODS: Diffusion MR imaging was acquired from a novel isotropic diffusion phantom developed at the National Institute of Standards and Technology and from the brain of a traveling volunteer on thirteen 3T MR imaging scanners representing 3 major vendors (GE Healthcare, Philips Healthcare, and Siemens). Means of the DTI parameters and their coefficients of variation across scanners were calculated for each DTI metric and white matter tract. RESULTS: For the National Institute of Standards and Technology diffusion phantom, the coefficients of variation of the apparent diffusion coefficient across the 13 scanners was <3.8% for a range of diffusivities from 0.4 to 1.1 × 10−6 mm2/s. For the volunteer, the coefficients of variations across scanners of the 4 primary DTI metrics, each averaged over the entire white matter skeleton, were all <5%. In individual white matter tracts, large central pathways showed good reproducibility with the coefficients of variation consistently below 5%. However, smaller tracts showed more variability, with the coefficients of variation of some DTI metrics reaching 10%. CONCLUSIONS: The results suggest the feasibility of standardizing DTI across 3T scanners from different MR imaging vendors in a large-scale neuroimaging research study.

[1]  Timothy Edward John Behrens,et al.  Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging , 2003, Nature Neuroscience.

[2]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[3]  Daniel Rueckert,et al.  Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.

[4]  M. Jenkinson Non-linear registration aka Spatial normalisation , 2007 .

[5]  Klaas Nicolay,et al.  Reproducibility of Quantitative Cerebral T2 Relaxometry, Diffusion Tensor Imaging, and 1H Magnetic Resonance Spectroscopy at 3.0 Tesla , 2007, Investigative radiology.

[6]  R. Henry,et al.  Diffusion Tensor MR Imaging and Fiber Tractography: Theoretic Underpinnings , 2008, American Journal of Neuroradiology.

[7]  Bruce D. McCandliss,et al.  Extent of Microstructural White Matter Injury in Postconcussive Syndrome Correlates with Impaired Cognitive Reaction Time: A 3T Diffusion Tensor Imaging Study of Mild Traumatic Brain Injury , 2008, American Journal of Neuroradiology.

[8]  R. Henry,et al.  Diffusion Tensor MR Imaging and Fiber Tractography: Technical Considerations , 2008, American Journal of Neuroradiology.

[9]  P. Basser,et al.  Polyvinylpyrrolidone (PVP) water solutions as isotropic phantoms for diffusion MRI studies , 2008 .

[10]  D. Bohning,et al.  Reproducibility, Interrater Agreement, and Age-Related Changes of Fractional Anisotropy Measures at 3T in Healthy Subjects: Effect of the Applied b-Value , 2008, American Journal of Neuroradiology.

[11]  John S. Duncan,et al.  Identical, but not the same: Intra-site and inter-site reproducibility of fractional anisotropy measures on two 3.0 T scanners , 2010, NeuroImage.

[12]  Marco Rovaris,et al.  Intercenter differences in diffusion tensor MRI acquisition , 2010, Journal of magnetic resonance imaging : JMRI.

[13]  T. A. Carpenter,et al.  Mapping Traumatic Axonal Injury Using Diffusion Tensor Imaging: Correlations with Functional Outcome , 2011, PloS one.

[14]  Daniel L. Polders,et al.  Signal to noise ratio and uncertainty in diffusion tensor imaging at 1.5, 3.0, and 7.0 Tesla , 2011, Journal of magnetic resonance imaging : JMRI.

[15]  Xing Qiu,et al.  Quantification of accuracy and precision of multi-center DTI measurements: A diffusion phantom and human brain study , 2011, NeuroImage.

[16]  Xue Wang,et al.  Reproducibility of Structural, Resting-State BOLD and DTI Data between Identical Scanners , 2012, PloS one.

[17]  Steven Laureys,et al.  Assessment of White Matter Injury and Outcome in Severe Brain Trauma: A Prospective Multicenter Cohort , 2012, Anesthesiology.

[18]  Jessica A. Turner,et al.  MultiCenter Reliability of Diffusion Tensor Imaging , 2012, Brain Connect..

[19]  Naoto Hayashi,et al.  Effect of scanner in longitudinal diffusion tensor imaging studies , 2012, Human brain mapping.

[20]  N. Bargalló,et al.  White matter integrity related to functional working memory networks in traumatic brain injury , 2012, Neurology.

[21]  J. Debbins,et al.  A Validation Study of Multicenter Diffusion Tensor Imaging: Reliability of Fractional Anisotropy and Diffusivity Values , 2012, American Journal of Neuroradiology.

[22]  Steven Laureys,et al.  Assessment of White Matter Injury and Outcome in Severe Brain Trauma. A Prospective Multicenter Cohort , 2013 .

[23]  Guy B. Williams,et al.  Inter Subject Variability and Reproducibility of Diffusion Tensor Imaging within and between Different Imaging Sessions , 2013, PloS one.

[24]  May D. Wang,et al.  Biomedical imaging informatics in the era of precision medicine: progress, challenges, and opportunities , 2013, J. Am. Medical Informatics Assoc..

[25]  Hester F. Lingsma,et al.  Transforming research and clinical knowledge in traumatic brain injury pilot: multicenter implementation of the common data elements for traumatic brain injury. , 2013, Journal of neurotrauma.

[26]  Carlo Pierpaoli,et al.  A framework for the analysis of phantom data in multicenter diffusion tensor imaging studies , 2013, Human brain mapping.

[27]  D. Galanaud,et al.  Long-Term White Matter Changes after Severe Traumatic Brain Injury: A 5-Year Prospective Cohort , 2014, American Journal of Neuroradiology.

[28]  Hester F. Lingsma,et al.  Diffusion tensor imaging for outcome prediction in mild traumatic brain injury: a TRACK-TBI study. , 2014, Journal of neurotrauma.

[29]  Masaaki Hori,et al.  Intersite Reliability of Diffusion Tensor Imaging on Two 3T Scanners. , 2015, Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine.

[30]  Vijay K. Venkatraman,et al.  Region of interest correction factors improve reliability of diffusion imaging measures within and across scanners and field strengths , 2015, NeuroImage.

[31]  Peter Savadjiev,et al.  Harmonizing Diffusion MRI Data Across Multiple Sites and Scanners , 2015, MICCAI.

[32]  D. Auer,et al.  Multi-centre reproducibility of diffusion MRI parameters for clinical sequences in the brain , 2015, NMR in biomedicine.

[33]  F. Collins,et al.  A new initiative on precision medicine. , 2015, The New England journal of medicine.