Improved in vivo diffusion tensor imaging of human cervical spinal cord
Graphical abstract
Highlights
► A comprehensive tract-specific DTI protocol for human cervical spinal cord. ► Small radial diffusivity in WM highlighted the effect of optimization. ► Respiratory motion likely biased DTI parameters in spinal cord. ► Mixed-effects model accounted for systematic measurement bias. ► Outlier removal is indispensable for reproducible spinal cord diffusion imaging.
Introduction
Spinal cord damage is a major cause of clinical disability in multiple sclerosis (MS), neuromyelitis optica (NMO), amyotrophic lateral sclerosis (ALS) and trauma. The spinal cord contains white matter (WM) tracts that carry information to and from the brain. The lateral corticospinal tracts (CST), located in the lateral portion of the cord, convey motor commands from the brain to all spinal segments; the posterior columns (PC), located in the dorsal portion of the cord, relay sensory impulses upwards to nuclei in the brainstem. The ability to evaluate tissue integrity within these specific WM tracts would have immediate clinical impact.
Conventional spinal cord magnetic resonance imaging (MRI), using T2-weighted (T2W), T1-weighted (T1W), and short-TI inversion recovery (STIR) sequences, can reveal the location of lesions that are either hyper- or hypo-intense. However, such qualitative assessments are not specific with respect to the underlying disease process leading to white matter pathology. Diffusion tensor imaging (DTI, Basser and Pierpaoli, 1996) improves pathologic specificity through the quantitative directional diffusivities, which measure water diffusion parallel (i.e., axial or longitudinal diffusivity equals λ1 or λ||) and perpendicular (i.e., radial or perpendicular diffusivity equals λ⊥ = λ2 + λ3/2) to the WM tracts (Song et al., 2002, Song et al., 2003). These parameters have been shown to reflect axon and myelin damage in mouse models of spinal cord injury and disease (Budde et al., 2009, Kim et al., 2006).
However, quantitative in vivo DTI of the human spinal cord (Clark and Werring, 2002, Ellingson et al., 2008, Maier, 2007, Maier and Mamata, 2005) is challenging for several reasons (Barker, 2001): (i) The cord has a small cross-sectional area; (ii) magnetic field inhomogeneities from nearby vertebrae cause image distortions; and (iii) cerebrospinal fluid (CSF) pulsations, cardiac pulsation in surrounding vessels, and respiratory motion generate significant motion artifacts in the anterior-posterior and rostro-caudal directions. Thus, limited signal-to-noise ratio (SNR) resulting from physiological artifacts as well as thermal noise is a major concern in high in-plane resolution axial spinal cord DTI acquisition.
Reduced field-of-view (rFOV) sequences (Dowell et al., 2009, Finsterbusch, 2009, Jeong et al., 2005, Jeong et al., 2006, Kim et al., 2010, Saritas et al., 2008, Wheeler-Kingshott et al., 2002a, Wilm et al., 2007, Wilm et al., 2009, Xu et al., 2010) have shown promise in producing high quality, in vivo human spinal cord DTI. However, despite improvements in image quality and spatial resolution, tract-specific diffusion quantification within the individual spinal cord WM tracts has not been entirely satisfactory. Few studies have employed cardiac gating (Cohen-Adad et al., 2011a, Kharbanda et al., 2006, Summers et al., 2006, Wheeler-Kingshott et al., 2002a) to minimize the effect of CSF pulsations or have implemented outlier detection routines (Freund et al., 2010) as quality control measures. Consequently, overestimation of water diffusivities is likely prevalent in the human spinal cord DTI literature. In addition, motion correction, which depends on image registration, is challenging in rFOV spinal cord DTI because of limited tissue contrast and the small FOV.
Here we propose a slice-by-slice cardiac gated, rFOV cervical spinal cord DTI protocol. Features of this protocol include (i) iterative (Shimony et al., 2006) 2D image registration (Smith et al., 2010) optimized for rFOV spinal cord diffusion weighted (DW) images; (ii) outlier detection (Chang et al., 2005) to minimize the influence of physiological noise; (iii) a diffusion tensor estimation procedure incorporating positive eigenvalue priors, similar to (Cohen-Adad et al., 2008, Ducreux et al., 2007, Onu et al., 2010); and (iv) tract-specific region of interest (ROI) demarcation based on known anatomy (Klawiter et al., 2012, Xu et al., 2010). We offer this protocol as a comprehensive solution to the aforementioned challenges of tract-specific quantitative DTI of in vivo human spinal cord.
Section snippets
Subjects
Eighteen neurologically normal volunteers (median age 34 ± 13 years, range 19–70 years; 8 females and 10 males) participated in this study. Each participant provided a written informed consent. All aspects of the study were approved by the Washington University Human Research Protection Office. Eight of the volunteers (1 subject, 4 times; 1 subject, 3 times, and 6 subjects, 2 times) were re-scanned over a 2-year period. Subjects were instructed to breathe and swallow normally during the entire data
Results
The average number of outlier images per slice was 6 ± 5% (mean ± sd). The number of outlier images varied substantially between slices and subjects with a trend of more outlier images towards the caudal slices. The outliers identified as excessive translation (i.e., failed registration) before the initial DTI fitting accounted for less than 1% of the total number of outlier images. In DTI data processed without outlier rejection there were appreciable overestimations of λ⊥ (Fig. 5A), λ|| (Fig. 5
Discussion
Starting with the improved image quality and spatial resolution offered by rFOV diffusion imaging (Dowell et al., 2009, Finsterbusch, 2009, Jeong et al., 2005, Jeong et al., 2006, Kim et al., 2010, Saritas et al., 2008, Wheeler-Kingshott et al., 2002a, Wilm et al., 2007, Wilm et al., 2009), we employed slice-by-slice cardiac triggering and individually tiltable slices in spinal cord DTI acquisition (Klawiter et al., 2012, Xu et al., 2010). We further implemented several post-processing steps to
Acknowledgments
We thank Mark Jenkinson (fMRI group, Oxford, UK) for providing a 2D translation-only (DOF = 2) schedule file for FSL FLIRT registration. We are also grateful to Larry Bretthorst (Biomedical Magnetic Resonance Laboratory, Washington University School of Medicine) for instrumental discussions about the implementation of Bayesian positive priors in the non-linear DTI fitting method. We appreciate the technical and scanning assistance of Glenn J. Foster, Mark A. Nolte and Scott M. Love (Center for
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Dr. Xu is now affiliated with Mount Sinai School of Medicine.