Optimizing Filter-Probe Diffusion Weighting in the Rat Spinal Cord for Human Translation

Diffusion tensor imaging (DTI) is a promising biomarker of spinal cord injury (SCI). In the acute aftermath, DTI in SCI animal models consistently demonstrates high sensitivity and prognostic performance, yet translation of DTI to acute human SCI has been limited. In addition to technical challenges, interpretation of the resulting metrics is ambiguous, with contributions in the acute setting from both axonal injury and edema. Novel diffusion MRI acquisition strategies such as double diffusion encoding (DDE) have recently enabled detection of features not available with DTI or similar methods. In this work, we perform a systematic optimization of DDE using simulations and an in vivo rat model of SCI and subsequently implement the protocol to the healthy human spinal cord. First, two complementary DDE approaches were evaluated using an orientationally invariant or a filter-probe diffusion encoding approach. While the two methods were similar in their ability to detect acute SCI, the filter-probe DDE approach had greater predictive power for functional outcomes. Next, the filter-probe DDE was compared to an analogous single diffusion encoding (SDE) approach, with the results indicating that in the spinal cord, SDE provides similar contrast with improved signal to noise. In the SCI rat model, the filter-probe SDE scheme was coupled with a reduced field of view (rFOV) excitation, and the results demonstrate high quality maps of the spinal cord without contamination from edema and cerebrospinal fluid, thereby providing high sensitivity to injury severity. The optimized protocol was demonstrated in the healthy human spinal cord using the commercially-available diffusion MRI sequence with modifications only to the diffusion encoding directions. Maps of axial diffusivity devoid of CSF partial volume effects were obtained in a clinically feasible imaging time with a straightforward analysis and variability comparable to axial diffusivity derived from DTI. Overall, the results and optimizations describe a protocol that mitigates several difficulties with DTI of the spinal cord. Detection of acute axonal damage in the injured or diseased spinal cord will benefit the optimized filter-probe diffusion MRI protocol outlined here.

[1]  O N Hausmann,et al.  Post-traumatic inflammation following spinal cord injury , 2003, Spinal Cord.

[2]  X. Papademetris,et al.  Diffusion tensor imaging as a predictor of locomotor function after experimental spinal cord injury and recovery. , 2014, Journal of neurotrauma.

[3]  F. Wang,et al.  Timing of diffusion tensor imaging in the acute spinal cord injury of rats , 2015, Scientific Reports.

[4]  Jens Frahm,et al.  Identification of the Upward Movement of Human CSF In Vivo and its Relation to the Brain Venous System , 2017, The Journal of Neuroscience.

[5]  S. Kurpad,et al.  Clinical correlates of high cervical fractional anisotropy in acute cervical spinal cord injury. , 2015, World neurosurgery.

[6]  B. Ellingson,et al.  Ex vivo diffusion tensor imaging and quantitative tractography of the rat spinal cord during long‐term recovery from moderate spinal contusion , 2008, Journal of magnetic resonance imaging : JMRI.

[7]  S. Kurpad,et al.  Rapid in vivo detection of rat spinal cord injury with double‐diffusion‐encoded magnetic resonance spectroscopy , 2017, Magnetic resonance in medicine.

[8]  C. Sønderby,et al.  Orientationally invariant metrics of apparent compartment eccentricity from double pulsed field gradient diffusion experiments , 2013, NMR in biomedicine.

[9]  M. Stuber,et al.  Respiratory motion artifact suppression in diffusion-weighted MR imaging of the spine , 2003, European Radiology.

[10]  P. Mitra,et al.  Conventions and nomenclature for double diffusion encoding NMR and MRI , 2016, Magnetic resonance in medicine.

[11]  Yoram Cohen,et al.  The effect of experimental parameters on the signal decay in double-PGSE experiments: negative diffractions and enhancement of structural information. , 2008, Journal of magnetic resonance.

[12]  Mitra,et al.  Multiple wave-vector extensions of the NMR pulsed-field-gradient spin-echo diffusion measurement. , 1995, Physical review. B, Condensed matter.

[13]  Joong Hee Kim,et al.  Diffusion tensor imaging predicts hyperacute spinal cord injury severity. , 2007, Journal of neurotrauma.

[14]  C. Fisher,et al.  Predicting Injury Severity and Neurological Recovery after Acute Cervical Spinal Cord Injury: A Comparison of Cerebrospinal Fluid and Magnetic Resonance Imaging Biomarkers. , 2017, Journal of neurotrauma.

[15]  E. Roldán-Valadez,et al.  Feasibility of In Vivo Quantitative Magnetic Resonance Imaging With Diffusion Weighted Imaging, T2-Weighted Relaxometry, and Diffusion Tensor Imaging in a Clinical 3 Tesla Magnetic Resonance Scanner for the Acute Traumatic Spinal Cord Injury of Rats: Technical Note , 2013, Spine.

[16]  Julien Cohen-Adad,et al.  SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data , 2017, NeuroImage.

[17]  Brian D. Schmit,et al.  Detection of acute nervous system injury with advanced diffusion‐weighted MRI: a simulation and sensitivity analysis , 2015, NMR in biomedicine.

[18]  Jin Hyung Lee,et al.  DWI of the spinal cord with reduced FOV single‐shot EPI , 2008, Magnetic resonance in medicine.

[19]  O. Griesbeck,et al.  A recoverable state of axon injury persists for hours after spinal cord contusion in vivo , 2014, Nature Communications.

[20]  P. Narayana,et al.  Histological correlation of diffusion tensor imaging metrics in experimental spinal cord injury , 2008, Journal of neuroscience research.

[21]  Bennett A Landman,et al.  q‐space and conventional diffusion imaging of axon and myelin damage in the rat spinal cord after axotomy , 2010, Magnetic resonance in medicine.

[22]  Derek K. Jones,et al.  “Squashing peanuts and smashing pumpkins”: How noise distorts diffusion‐weighted MR data , 2004, Magnetic resonance in medicine.

[23]  Jens Frahm,et al.  Inspiration Is the Major Regulator of Human CSF Flow , 2015, The Journal of Neuroscience.

[24]  Nikolaus Weiskopf,et al.  The impact of post-processing on spinal cord diffusion tensor imaging , 2013, NeuroImage.

[25]  J. Veraart,et al.  Degeneracy in model parameter estimation for multi‐compartmental diffusion in neuronal tissue , 2016, NMR in biomedicine.

[26]  Joong Hee Kim,et al.  Full tensor diffusion imaging is not required to assess the white-matter integrity in mouse contusion spinal cord injury. , 2010, Journal of neurotrauma.

[27]  M. Esiri,et al.  Axonal damage: a key predictor of outcome in human CNS diseases. , 2003, Brain : a journal of neurology.

[28]  Jürgen Finsterbusch,et al.  Microscopic diffusion anisotropy in the human brain: Age-related changes , 2016, NeuroImage.

[29]  C. Fisher,et al.  Predicting Injury Severity and Neurologic Recovery after Acute Cervical Spinal Cord Injury – A Comparison of Cerebrospinal Fluid and Magnetic Resonance Imaging Biomarkers . Abbreviated Title : CSF and MRI Biomarkers in Acute Cervical Spinal Cord Injury , 2017 .

[30]  S. Kurpad,et al.  Ex vivo diffusion tensor imaging of spinal cord injury in rats of varying degrees of severity. , 2013, Journal of neurotrauma.

[31]  R. E. Schmidt,et al.  Noninvasive diffusion tensor imaging of evolving white matter pathology in a mouse model of acute spinal cord injury , 2007, Magnetic resonance in medicine.

[32]  Stephan E. Maier,et al.  Examination of spinal cord tissue architecture with magnetic resonance diffusion tensor imaging , 2007, Neurotherapeutics.

[33]  Aleksandra Pizurica,et al.  The effect of Gibbs ringing artifacts on measures derived from diffusion MRI , 2015, NeuroImage.

[34]  A. Flanders,et al.  The Early Evolution of Spinal Cord Lesions on MR Imaging following Traumatic Spinal Cord Injury , 2008, American Journal of Neuroradiology.

[35]  R. E. Schmidt,et al.  Toward accurate diagnosis of white matter pathology using diffusion tensor imaging , 2007, Magnetic resonance in medicine.

[36]  Jelle Veraart,et al.  One diffusion acquisition and different white matter models: How does microstructure change in human early development based on WMTI and NODDI? , 2015, NeuroImage.

[37]  J. Bonny,et al.  Time course of acute phase in mouse spinal cord injury monitored by ex vivo quantitative MRI , 2006, Neurobiology of Disease.

[38]  Mark W. Woolrich,et al.  FSL , 2012, NeuroImage.

[39]  M. Mallar Chakravarty,et al.  Neurite density from magnetic resonance diffusion measurements at ultrahigh field: Comparison with light microscopy and electron microscopy , 2010, NeuroImage.

[40]  Melvin T. Alexander,et al.  Correlation of MR diffusion tensor imaging parameters with ASIA motor scores in hemorrhagic and nonhemorrhagic acute spinal cord injury. , 2011, Journal of neurotrauma.

[41]  Alexander Sasha Rabchevsky,et al.  Serial Diffusion Tensor Imaging In Vivo Predicts Long-Term Functional Recovery and Histopathology in Rats following Different Severities of Spinal Cord Injury. , 2016, Journal of neurotrauma.

[42]  S. Lalwani,et al.  Spinal cord injury. , 2011, Journal of neurosurgery. Spine.

[43]  P. W. Stroman,et al.  The current state-of-the-art of spinal cord imaging: Applications , 2014, NeuroImage.

[44]  Allan R. Martin,et al.  Translating state-of-the-art spinal cord MRI techniques to clinical use: A systematic review of clinical studies utilizing DTI, MT, MWF, MRS, and fMRI , 2015, NeuroImage: Clinical.

[45]  J. Steeves,et al.  Common data elements for spinal cord injury clinical research: a National Institute for Neurological Disorders and Stroke project , 2015, Spinal Cord.

[46]  Michal Neeman,et al.  A simple method for obtaining cross‐term‐free images for diffusion anisotropy studies in NMR microimaging , 1991, Magnetic resonance in medicine.

[47]  Kathryn Trinkaus,et al.  Diffusion tensor imaging at 3 hours after traumatic spinal cord injury predicts long-term locomotor recovery. , 2010, Journal of neurotrauma.

[48]  Julien Cohen-Adad,et al.  The current state-of-the-art of spinal cord imaging: Methods , 2014, NeuroImage.

[49]  Catriona Miller,et al.  Diffusion Tensor Imaging Parameter Obtained during Acute Blunt Cervical Spinal Cord Injury in Predicting Long-Term Outcome. , 2017, Journal of neurotrauma.

[50]  F. Wang,et al.  Determination of the ideal rat model for spinal cord injury by diffusion tensor imaging , 2014, Neuroreport.

[51]  J. Frank,et al.  Neurite beading is sufficient to decrease the apparent diffusion coefficient after ischemic stroke , 2010, Proceedings of the National Academy of Sciences.

[52]  Dariusz Adamek,et al.  Visualisation of the extent of damage in a rat spinal cord injury model using MR microsopy of the water diffusion tensor. , 2005, Acta neurobiologiae experimentalis.

[53]  P GullapalliRao,et al.  Diffusion Tensor Imaging Parameter Obtained during Acute Blunt Cervical Spinal Cord Injury in Predicting Long-Term Outcome. , 2017 .

[54]  L R Schad,et al.  Comparison of diffusion anisotropy measurements in combination with the flair-technique. , 1999, Magnetic resonance imaging.

[55]  Brian D. Schmit,et al.  Lesion growth and degeneration patterns measured using diffusion tensor 9.4-T magnetic resonance imaging in rat spinal cord injury. , 2010, Journal of neurosurgery. Spine.

[56]  K. Hasan,et al.  In vivo serial diffusion tensor imaging of experimental spinal cord injury , 2006, Journal of neuroscience research.

[57]  Adam R Ferguson,et al.  The Brain and Spinal Injury Center score: a novel, simple, and reproducible method for assessing the severity of acute cervical spinal cord injury with axial T2-weighted MRI findings. , 2015, Journal of neurosurgery. Spine.

[58]  D. Basso,et al.  A sensitive and reliable locomotor rating scale for open field testing in rats. , 1995, Journal of neurotrauma.

[59]  P. London Injury , 1969, Definitions.

[60]  J. H. Steiger Tests for comparing elements of a correlation matrix. , 1980 .

[61]  Daniel C. Alexander,et al.  Convergence and Parameter Choice for Monte-Carlo Simulations of Diffusion MRI , 2009, IEEE Transactions on Medical Imaging.

[62]  P. Bottomley Spatial Localization in NMR Spectroscopy in Vivo , 1987, Annals of the New York Academy of Sciences.

[63]  Hsiao-Fang Liang,et al.  Detecting axon damage in spinal cord from a mouse model of multiple sclerosis , 2006, Neurobiology of Disease.

[64]  P. Narayana,et al.  In vivo longitudinal MRI and behavioral studies in experimental spinal cord injury. , 2010, Journal of neurotrauma.

[65]  Lucio Frydman,et al.  Metabolic properties in stroked rats revealed by relaxation-enhanced magnetic resonance spectroscopy at ultrahigh fields , 2014, Nature Communications.

[66]  T. Tominaga,et al.  Prediction of Neurological Recovery Using Apparent Diffusion Coefficient in Cases of Incomplete Spinal Cord Injury , 2011, Neurosurgery.