Gradient nonlinearity effects on upper cervical spinal cord area measurement from 3D T1‐weighted brain MRI acquisitions

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Quantifying spinal cord atrophy in neurologic conditions from various etiologies, including trauma, inflammation or neurodegeneration, has gained increasing attention in the neuroimaging field, particularly with the development of dedicated spinal cord imaging techniques 1. In multiple sclerosis (MS), upper cervical cord area (UCCA) is a surrogate of atrophy, and has been shown to be associated with physical disability, particularly in progressive stages of the disease 6. Spinal cord MRI remains technically challenging and adds substantial time to a scanning session; therefore, many clinical sites do not routinely acquire dedicated spinal cord MRI that can provide reliable measures of UCCA. However, standard brain high‐resolution 3D T1‐weighted acquisitions that include the upper cervical cord can be used to provide estimates of UCCA 12.
Gradient nonlinearity (GNL) distortions in MRI are a consequence of technical challenges in the design of gradient coils, and an important source of systematic error interfering with the integrity of imaging metrics in longitudinal and multisite studies. These challenges are particularly important in modern whole‐body MRI scanners designed with shorter bores for increased patient comfort and reduced footprint, and fast gradient rise times for faster imaging. Moreover, a recent head‐only, compact gradient design was reported to provide increased peripheral nerve stimulation thresholds 14 and very high slew rates (up to 700 T/m/s) that provide significantly improved echo‐planar imaging performance 15. However, such systems have significantly increased GNL effects over standard whole‐body scanners, including terms associated with asymmetric transverse gradients. Large, multisite initiatives have investigated the effect of GNL distortions on brain tissue–volume quantification 16; however, the effect of GNL on UCCA measurements extracted from standard brain T1‐weighted acquisitions has not yet been assessed. This is extremely important, as the spinal cord is located in the periphery of the field of view (FOV), where GNL effects are expected to introduce maximal errors.
In the present work, we explored (i) the variability of the UCCA measurements from brain 3D T1‐weighted scans related to GNL and subject positioning; (ii) the effect of correction algorithms implemented on commercial scanners; and 3 easily applicable methods that can be used to retrospectively correct data acquired without optimal distortion correction.

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