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, DWI, and its directionality, DTI, the techniques used for analysis are limited to assuming an ideal Gaussian distribution for water displacement with no intermolecular interactions. This assumption reduces the amount of relevant information that can be interpreted in a clinical setting. By measuring the excess kurtosis, or peakedness, of the Gaussian distribution it is possible to get 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 provide additional information about the micromolecular environment of the pediatric spinal cord by more completely characterizing the probabilistic nature of random water displacement. A novel DKI imaging sequence based on a 2D spatially selective radio frequency pulse providing reduced FOV imaging with view angle tilting (VAT) was implemented, optimized on a 3Tesla MRI scanner, and tested on pediatric subjects (normal:15; patients with spinal cord injury:5). Software was developed and validated in-house for post processing of the DKI images and estimation of the tensor parameters. The results show statistically significant differences in kurtosis parameters (mean kurtosis, axial kurtosis) between normal and patients. DKI provides incremental and new information over conventional diffusion acquisitions that can be integrated into clinical protocols when coupled with higher order estimation algorithms.