Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

Abstract

same b-vector), this artifact is not visible (mountSinai03, right panel). Such an artifact could be problematic for image registration with regularization along the S-I axis, or for performing diffusion tractography. (e) b = 0 image from a DWI scan (perform02) acquired with poor shimming and resulting signal dropout. (f) Another example of poor shimming resulting in sub-efficient fat saturation, with the fat being aliased on top of the SC. Here we show the mean DWI scan of a participant from the single subject database (perform). (g) Effect of pulsatile movement on a non-cardiac gated acquisition (single subject, juntendoAchieva). Diffusion-weighted scans (sagittal view) acquired at three b-vecs fairly orthogonal to the SC (i.e., diffusion-specific signal attenuation should be minimum in the SC), showing abrupt signal drop at a few slices (red arrows), likely due to cardiac-related pulsatile effects.

Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study

“…(xi) We highlight the importance of considering the scanner-related effects present in SC imaging data. 24,25 (xii) We confirm significant relationships between body size, brain volumes/weight, and CSA-SC in line with previously reported results. 17,21,47…”

“…More detailed information about protocol settings and scanner subtypes can be found in the spine-generic protocol original papers. 24,25…”

“…The dataset allows for the separate assessment of cervical SC WM and GM morphology in a large cohort of healthy cosmopolitan volunteers with available demographic records and images of cerebral morphology. 24,25…”

“…Both DTI and MTR image contrasts are available within the spine-generic dataset. 24,25 Moreover, body weight and BMI are correlated with MTR of peripheral nerves and muscles. 35 Our second hypothesis was therefore that “SC microstructure, as measured using MTR and DTI, interacts with body size” .…”

Combining PropSeg and a convolutional neural network for automatic spinal cord segmentation in pediatric populations and patients with spinal cord injury

“…In our method, we use the OptiC algorithm 10,13,14 available in SCT 4 with the command line sct_get_centerline in order to automate the centerline detection. OptiC detects the spinal cord centerline using a 2D CNN 15 on each slice of the MR image. The resulting centerline is used as input for the next steps.…”