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.

Keywords: Charcot-Marie-Tooth Disease; Magnetic Resonance Imaging; Peripheral Nerves; Peripheral Nervous System; Peripheral Neuropathy; Sciatic Nerve.

Figure 1.. Peripheral nerve cross-sectional anatomy.

( A) Illustration of peripheral nerves’ cross-sectional anatomy with the presence of intraneural blood vessels: (1) epineurium, (2) lipid-equivalent connection tissues, (3) individual nerve fascicle, (4) perineurium, (5) artery, and (6) vein. ( B) Representative myelinated nerve fibers under light microscopy from a control sciatic nerve , stained with Toluidine blue: (7) myelin sheath, (8) axon, and (9) endoneurium. The picture in B was tailored from Figure 1A of Li et al. .

Figure 2.. In vivo ultra-high-resolution magnetic resonance imaging of sciatic nerve in a patient with Charcot–Marie–Tooth disease (CMT) and healthy control.

Images of a patient with CMT type 4J ( A– C, 35 years old, male) and those of a healthy control ( D– F, 35 years old, male) were acquired at distal 30% of femur length by using a three-dimensional (3D) high-resolution gradient-recalled echo scan with a voxel size of 0.15 × 0.2 × 3 mm ³; 3D fascicular nerve reconstructions ( A and D) were rendered (VolView 3.4, Clifton Park, NY, USA) from the overlay of the manually segmented tibial and peroneal portions of the nerve fascicles onto the original magnitude images ( B and E). The rightmost images ( C and F) were enlarged from B and E, respectively. CMT4J is a rare subtype of the inherited neuropathy caused by recessive genetic mutations with the loss of FIG4 protein which results in demyelination in peripheral nerves . Even though the significantly enlarged sciatic nerve cross-sectional area is a change in a number of peripheral neuropathies, it is not possible to differentiate demyelination versus axonal degeneration using the magnitude images (or other forms of conventional imaging such as proton density–weighted, T1-weighted, or T2-weighted imaging). However, susceptibility-based techniques such as T2* mapping, susceptibility-weighted imaging, and quantitative susceptibility mapping may be used to probe the integrity of the myelin. Imaging parameters were those listed in the fourth scan in Table 2.

Figure 3.. Differentiating intraneural blood vessels from nerve fascicles.

High-resolution water-excited three-dimensional (3D) gradient-recalled echo scans of a healthy volunteer (38 years old, male) without ( A) and with ( B) spatial saturation bands placed on the proximal side of the imaging slab, which suppresses the signal of major arteries flowing into the imaging slab. However, spatial saturation pulses on the 3D acquisition slab work for fast flow (dotted circles on A and B) but not slow flow (arrows on B and C) that presents in the artery inside the epineurium. This interpretation of slow blood flow suppression is confirmed by using a thin slice acquisition ( C) which was from a 2D proton density–weighted scan with fat suppression using a turbo spin echo sequence.

Figure 4.. Interleaved two-point Dixon water/fat separation.

Images were from the same patient with Charcot–Marie–Tooth disease type 4J (35 years old, male) using an adapted gradient-recalled echo sequence . Two echoes with echo times of 7.6 ms (in-phase) and 8.5 ms (out-of-phase) were acquired in an interleaved manner so that both images are naturally co-registered to each other. Phase ambiguities were resolved by using a projected power method in the two-point Dixon water/fat separation to get water (W, A) and fat (F, B) images . The fat fraction (FF) ( C) was computed to be FF = F / (W + F) after shifting the fat image to its real position by n pixels in the readout direction, where n = imaging frequency × 3.5 / bandwidth. Muscle atrophy with increased FF can be observed on the right-most muscle in this case.

Figure 5.. Semi-automated sciatic nerve fascicle segmentation and three-dimensional (3D) reconstruction.

This representative data was acquired using a high-resolution 3D gradient-recalled echo with water excitation on a healthy volunteer (35 years old, male). The sciatic nerve areas ( B) were manually drawn by an experienced neuroradiologist on the original magnitude image ( A). Then the nerve fascicles ( C) were extracted by using a histogram-based region growing approach (SPIN software, MR Innovation, Bingham Farms, MI, USA). The binary masks ( D) of nerve fascicles were generated with pixel erosion. The nerve fascicular 3D reconstruction ( E) (Pn, peroneal nerve; Tn, tibial nerve) was then generated by using 3D rendering (VolView 3.4, Clifton Park, NY, USA).