BACKGROUND AND PURPOSE:Advanced MR imaging techniques are critical to understanding the pathophysiology of conditions involving the spinal cord. We provide a novel, quantitative solution to map vertebral and spinal cord levels accounting for anatomic variability within the human spinal cord. For the first time, we report a population distribution of the segmental anatomy of the cervical spinal cord that has direct implications for the interpretation of advanced imaging studies most often conducted across groups of subjects.

Optical coherence tomography (OCT) has the combined advantage of high temporal (µsec) and spatial (<10µm) resolution. These features make it an attractive tool to study the dynamic relationship between neural activity and the surrounding blood vessels in the spinal cord, a topic that is poorly understood. Here we present work that aims to optimize an in vivo OCT imaging model of the rodent spinal cord. In this study we image the microvascular networks of both rats and mice using speckle variance OCT. This is the first report of depth resolved imaging of the in vivo spinal cord using an entirely endogenous contrast mechanism.

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Spinal cord segmentation is a developing area of research intended to aid the processing and interpretation of advanced magnetic resonance imaging (MRI). For example, high resolution three-dimensional volumes can be segmented to provide a measurement of spinal cord atrophy. Spinal cord segmentation is difficult due to the variety of MRI contrasts and the variation in human anatomy. In this study we propose a new method of spinal cord segmentation based on one-dimensional template matching and provide several metrics that can be used to compare with other segmentation methods. A set of ground-truth data from 10 subjects was manually-segmented by two different raters. These ground truth data formed the basis of the segmentation algorithm. A user was required to manually initialize the spinal cord center-line on new images, taking less than one minute. Template matching was used to segment the new cord and a refined center line was calculated based on multiple centroids within the segmentation. Arc distances down the spinal cord and cross-sectional areas were calculated. Inter-rater validation was performed by comparing two manual raters (n = 10). Semi-automatic validation was performed by comparing the two manual raters to the semi-automatic method (n = 10). Comparing the semi-automatic method to one of the raters yielded a Dice coefficient of 0.91 +/- 0.02 for ten subjects, a mean distance between spinal cord center lines of 0.32 +/- 0.08 mm, and a Hausdorff distance of 1.82 +/- 0.33 mm. The absolute variation in cross-sectional area was comparable for the semi-automatic method versus manual segmentation when compared to inter-rater manual segmentation. The results demonstrate that this novel segmentation method performs as well as a manual rater for most segmentation metrics. It offers a new approach to study spinal cord disease and to quantitatively track changes within the spinal cord in an individual case and across cohorts of subjects.

Context: Audit and Feedback (A&F), the summary and provision of clinical performance, is a popular quality improvement strategy. We are developing a web-based dashboard that uses data from the electronic medical record to help physicians identify gaps in care and act. However, A&F tools can only be effective if the targeted health professionals actively review their data and take action. In order to maximise the impact of A&F, the design should consider the needs and goals of clinicians.