Magnetic resonance imaging assessment of degenerative cervical myelopathy: a review of structural changes and measurement techniques

Abstract

<jats:p>Degenerative cervical myelopathy encompasses a spectrum of age-related structural changes of the cervical spine that result in static and dynamic injury to the spinal cord and collectively represent the most common cause of myelopathy in adults. Although cervical myelopathy is determined clinically, the diagnosis requires confirmation via imaging, and MRI is the preferred modality. Because of the heterogeneity of the condition and evolution of MRI technology, multiple techniques have been developed over the years in an attempt to quantify the degree of baseline severity and potential for neurological recovery. In this review, these techniques are categorized anatomically into those that focus on bone, ligaments, discs, and the spinal cord. In addition, measurements for the cervical spine canal size and sagittal alignment are also described briefly. These tools have resulted collectively in the identification of numerous useful parameters. However, the development of multiple techniques for assessing the same feature, such as cord compression, has also resulted in a number of challenges, including introducing ambiguity in terms of which methods to use and hindering effective comparisons of analysis in the literature. In addition, newer techniques that use advanced MRI are emerging and providing exciting new tools for assessing the spinal cord in patients with degenerative cervical myelopathy.</jats:p>

Predictors of symptomatic myelopathy in degenerative cervical spinal cord compression

“…Although previously used diagnostic criteria for DCM were neither standardized nor consistent across published studies, recent and current studies have defined DCM by the presence of at least one neurological sign and at least one neurological symptom in addition to a positive MRI for compression of the cord(Amenta et al, 2014;Kalsi-Ryan, Karamidas, & Fehlings, 2013).Definition of MRI criteria for degenerative cervical cord compression is essential for reliable and reproducible diagnosis of DCM. In general, spinal cord compression can be described based on the appearance or by measuring a ratio between the anteroposterior diameter at the compressed site and that of a noncompressed site, a ratio between the anteroposterior diameter and the transverse diameter (i.e., CR), or CSA at the region of compression(Nouri, Martin, Mikulis, & Fehlings, 2016). MRI T1/T2 signal changes, although frequently detected in DCM, are neither sensitive nor specific for degenerative cervical cord compression and are invaluable to the diagnosis of DCM (Kalsi-Ryan et al, 2013;Wilson et al, 2013).…”

A Novel MRI Biomarker of Spinal Cord White Matter Injury: T2*-Weighted White Matter to Gray Matter Signal Intensity Ratio

“…11,[21][22][23][24][25] These encouraging findings prompted the current study in degenerative cervical myelopathy (DCM), a common condition involving degeneration of the discs, ligaments, and vertebrae, resulting in cervical spinal cord compression and functional impairment (Fig 1). 26,27 We aimed to determine how well T2*WI WM/GM differs between patients with DCM and healthy subjects and correlates with global disability and focal neurologic deficits when extracted from corresponding regions of WM, in comparison with FA, MTR, and CSA of the SC.…”

Considerations for Mean Upper Cervical Cord Area Implementation in a Longitudinal MRI Setting: Methods, Interrater Reliability, and MRI Quality Control

“…[6][7][8] MUCCA has been found to be robust at the C1-C2 and C2-C3 intervertebral levels. 9,10 However, there is little consensus on how reliable it is longitudinally or whether SC atrophy measures are ready for use as a clinical trial end point and/or for patient monitoring in neuroinflammatory diseases, 11,12 SC injury, 13,14 or degenerative cervical myelopathy, 15 among other diseases affecting the SC. Most studies of MR imaging-based SC atrophy comparisons of diseased with healthy SC measures have relied on interindividual variability calculated from crosssectional data.…”