Peripheral nerve magnetic resonance imaging

Abstract

Magnetic resonance imaging (MRI) has been used extensively in revealing pathological changes in the central nervous system. However, to date, MRI is very much underutilized in evaluating the peripheral nervous system (PNS). This underutilization is generally due to two perceived weaknesses in MRI: first, the need for very high resolution to image the small structures within the peripheral nerves to visualize morphological changes; second, the lack of normative data in MRI of the PNS and this makes reliable interpretation of the data difficult. This article reviews current state-of-the-art capabilities in in vivo MRI of human peripheral nerves. It aims to identify areas where progress has been made and those that still require further improvement. In particular, with many new therapies on the horizon, this review addresses how MRI can be used to provide non-invasive and objective biomarkers in the evaluation of peripheral neuropathies. Although a number of techniques are available in diagnosing and tracking pathologies in the PNS, those techniques typically target the distal peripheral nerves, and distal nerves may be completely degenerated during the patient’s first clinic visit. These techniques may also not be able to access the proximal nerves deeply embedded in the tissue. Peripheral nerve MRI would be an alternative to circumvent these problems. In order to address the pressing clinical needs, this review closes with a clinical protocol at 3T that will allow high-resolution, high-contrast, quantitative MRI of the proximal peripheral nerves.

Demyelination in hereditary sensory neuropathy type‐1C

“…All subjects were scanned on a 3T system (Verio, Siemens Healthineers, Erlangen, Germany) with an 8‐channel knee coil positioned proximal to the knee. An established imaging protocol was performed in the axial plane 22 . It includes: (1) an interleaved two‐point Dixon three dimensional (3D) gradient recalled echo (GRE) scan for muscle fat fraction (FF) 23,24 ; (2) a high‐resolution 3D GRE scan for nerve fascicle cross‐sectional area (fCSA); (3) a dual‐echo two dimensional (2D) turbo spin echo (TSE) scan for nerve transverse relaxation rate (R2) measure; (4) the strategically acquired gradient echo (STAGE) imaging 25 for nerve longitudinal relaxation time (T1), proton density (PD) and effective transverse relaxation rate (R2*) measurements, which generated the radiofrequency (RF) field maps (B 1 + and B 1 − ) simultaneously for correcting RF variations in the T1, PD, and magnetization transfer ratio (MTR) maps; (5) two 3D GRE scans performed with and without MT pulses for nerve MTR; and (6) a diffusion tensor imaging (DTI) scan for nerve apparent diffusion coefficient (ADC) and fractional anisotropy (FA).…”

“…For muscle qMRI, a pixelwise fat fraction (FF) image was given by FF = F /(W + F )*100%, where F is the fat image and W is the water (muscle) image generated from the two‐point Dixon algorithm 24 . Whole‐muscle mean fat fraction (wmmFF) was measured on the FF image using an established semi‐automated segmentation to delineate muscular boundaries on the central slice of the water image 22 …”

Fatigue in patients with hereditary neuropathy with liability to pressure palsies

“…An established imaging protocol was performed in the axial plane. 11 It includes: i) an interleaved two-point Dixon three-dimensional (3D) gradient recalled echo (GRE) scan for muscle fat fraction (FF); 12,13 ii) a highresolution 3D GRE scan for nerve fascicular cross-section area (fCSA); iii) the strategically acquired gradient echo (STAGE) imaging 14 for nerve longitudinal relaxation time (T1) and proton density (PD) mappings, which also generates the transmitter and receiver radiofrequency field maps for correcting heterogeneities in the T1, PD and magnetization transfer ratio (MTR) maps; and iv) two 3D GRE scans performed with and without MT pulses for nerve MTR and effective transverse relaxation time (T2star).…”

“…MRI data were processed using an in-house developed program in MATLAB (R2019a, MathWorks, Natick, MA, USA) with image alignments via SPM (fil.ion.ucl.ac.uk/ spm/). This established processing pipeline 11 generates six qMRI metrics, including the mean of FF for muscle, as well as means of fCSA, T1, PD, MTR, and T2star for peripheral nerves (sciatic and tibial for this study). Previously described semi-automated segmentation methods 11 were used for extracting volumetric muscle FF and the other 5 indices for nerves at the central slice.…”

“…This established processing pipeline 11 generates six qMRI metrics, including the mean of FF for muscle, as well as means of fCSA, T1, PD, MTR, and T2star for peripheral nerves (sciatic and tibial for this study). Previously described semi-automated segmentation methods 11 were used for extracting volumetric muscle FF and the other 5 indices for nerves at the central slice. The nerve PD was normalized by the adjacent normal-appearing muscle, because the PD map depends on the reconstruction scale which is different for each MRI scan.…”