Sensory neuronopathy involves the spinal cord and brachial plexus: a quantitative study employing multiple-echo data image combination (MEDIC) and turbo inversion recovery magnitude (TIRM)
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Sensory neuronopathy (SNN) is a distinctive subtype of peripheral neuropathies, specifically targeting dorsal root ganglion (DRG). We utilized MRI to demonstrate the imaging characteristics of DRG, spinal cord (SC), and brachial plexus at C7 level in SNN.
We attempted multiple-echo data image combination (MEDIC) and turbo inversion recovery magnitude (TIRM) methods in nine patients with sensory neuronopathy and compared with those in 16 disease controls and 20 healthy volunteers. All participants underwent MRI for the measurement of DRG, posterior column (PC), lateral column, and spinal cord area (SCA) at C7 level. DRG diameters were obtained through its largest cross section, standardized by dividing sagittal diameter of mid-C7 vertebral canal. We also made comparisons of standardized anteroposterior diameter (APD) and left–right diameters of SC and PC in these groups. Signal intensity and diameter of C7 spinal nerve were assessed on TIRM.
Compared to control groups, signal intensities of DRG and PC were higher in SNN patients when using MEDIC, but the standardized diameters were shorter in either DRG or PC. Abnormal PC signal intensities were identified in eight out of nine SNN patients (89 %) with MEDIC and five out of nine (56 %) with T2-weighted images. SCA, assessed with MEDIC, was smaller in SNN patients than in the other groups, with significant reduction of its standardized APD. C7 nerve root diameters, assessed with TIRM, were decreased in SNN patients.
MEDIC and TIRM sequences demonstrate increased signal intensities and decreased area of DRG and PC, and decreased diameter of nerve roots in patients with SNN, which can play a significant role in early diagnosis.
KeywordsSensory neuronopathy Dorsal root ganglion Posterior column Multiple-echo data image combination Turbo inversion recovery magnitude
The authors thank Hui Zhang PhD., from the Department of Computer Science and Centre for Medical Image Computing, University College London, UK, for critical comments and helpful suggestions. We also would like to thank Huan Xu for her kind help in revising the manuscript.
Conflict of interest
We declare that we have no conflict of interest.
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