Spatially selective 2D RF inner field of view (iFOV) diffusion kurtosis imaging (DKI) of the pediatric spinal cord

Abstract

Magnetic resonance based diffusion imaging has been gaining more utility and clinical relevance over the past decade. Using conventional echo planar techniques, it is possible to acquire and characterize water diffusion within the central nervous system (CNS); namely in the form of Diffusion Weighted Imaging (DWI) and Diffusion Tensor Imaging (DTI). While each modality provides valuable clinical information in terms of the presence of diffusion and its directionality, both techniques are limited to assuming an ideal Gaussian distribution for water displacement with no intermolecular interactions. This assumption neglects pathological processes that are not Gaussian therefore reducing the amount of potentially clinically relevant information. Additions to the Gaussian distribution measured by the excess kurtosis, or peakedness, of the probabilistic model provide a better understanding of the underlying cellular structure. The objective of this work is to provide mathematical and experimental evidence that Diffusion Kurtosis Imaging (DKI) can offer additional information about the micromolecular environment of the pediatric spinal cord. This is accomplished by a more thorough characterization of the nature of random water displacement within the cord. A novel DKI imaging sequence based on a tilted 2D spatially selective radio frequency pulse providing reduced field of view (FOV) imaging was developed, implemented, and optimized on a 3 Tesla MRI scanner, and tested on pediatric subjects (healthy subjects: 15; patients with spinal cord injury (SCI):5). Software was developed and validated for post processing of the DKI images and estimation of the tensor parameters. The results show statistically significant differences in mean kurtosis (p < 0.01) and radial kurtosis (p < 0.01) between healthy subjects and subjects with SCI. DKI provides incremental and novel information over conventional diffusion acquisitions when coupled with higher order estimation algorithms.

Age related diffusion and tractography changes in typically developing pediatric cervical and thoracic spinal cord

“…Manual ROIs were drawn around the gray and white matter of the cord in the FA map. To avoid any partial volume artifacts which occur at the cord/CSF interface, the ROI borders were drawn approximately 1–2 voxels away from the perimeter of the spinal cord ( Conklin et al, 2016 ). The following levels were included: C1, mid-dens, base dens, mid-C2, C2–C3, mid-C3 (upper cervical cord); C3–C4, mid-C4, C4–C5, mid-C5 (middle cervical cord); C5–C6, mid-C6, C6–C7, mid-C7, C7–T1 (lower cervical cord); mid-T1, T1–T2, mid-T2, T2–T3, mid-T3, T3–T4, mid-T4, T4–T5 (upper thoracic cord); mid-T5, T5–T6, mid-T6, T6–T7, mid-T7, T7–T8, mid-T8, T8–T9 (middle thoracic cord); mid-T9, T9–T10, mid-T10, T10–T11, mid-T11, T11–T12, mid-T12, T12–L1, mid-L1 (lower thoracic cord).…”

Reduced Field of View Diffusion Tensor Imaging and Fiber Tractography of the Pediatric Cervical and Thoracic Spinal Cord Injury

“…Unfortunately, in pediatric imaging, these techniques increase scanning time, which may result in a lack of patience and cooperation by patients. 17,[27][28][29] Although the implementation of the 2D RF diffusion sequence helped to mitigate image contamination attributed to these effects, motion correction was performed to further enhance tensor estimation accuracy. 17 Diffusion-weighted images were aligned with the average reference image (b0) using a rigid body registration algorithm and a normalized mutual information cost function using an inhouse software developed in Matlab (The MathWorks, Inc., Natick, MA).…”

Identification of ghost artifact using texture analysis in pediatric spinal cord diffusion tensor images

“…Morphological processing relies on the ordering of the sets of pixel in an image through processes such as erosion, dilation, closing and opening [11,12]. The proposed multi-stage segmentation algorithm (figure 2 and 3) uses some basic information from the mean b0 image such as roundness, solidness and size of the cord [12–15]. The method works only on binary images.…”