Sciatic neurosteatosis: Relationship with age, gender, obesity and height

Imaging Biomarkers of Peripheral Nerves: Focus on Magnetic Resonance Neurography and Ultrasonography

Peripheral neuropathy is a prevalent and debilitating condition affecting millions of individuals globally. Magnetic resonance neurography (MRN) and ultrasonography (US) are noninvasive methods offering comprehensive visualization of peripheral nerves, using anatomical and functional imaging biomarkers to ensure accurate evaluation. For optimized MRN, superior and high-resolution two-dimensional and three-dimensional imaging protocols are essential. The anatomical MRN and US imaging markers include quantitative measures of nerve and fascicular size and signal, and qualitative markers of course and morphology. Among them, quantitative markers of T2-signal intensity ratio are sensitive to nerve edema-like signal changes, and the T1-mapping technique reveals nerve and muscle tissue fatty and fibrous compositional alterations.The functional markers are derived from physiologic properties of nerves, such as diffusion characteristics or blood flow. They include apparent diffusion coefficient from diffusion-weighted imaging and fractional anisotropy and tractography from diffusion tensor imaging to delve into peripheral nerve microstructure and integrity. Peripheral nerve perfusion using dynamic contrast-enhanced magnetic resonance imaging estimates perfusion parameters, offering insights into nerve health and neuropathies involving edema, inflammation, demyelination, and microvascular alterations in conditions like type 2 diabetes, linking nerve conduction pathophysiology to vascular permeability alterations.Imaging biomarkers thus play a pivotal role in the diagnosis, prognosis, and monitoring of nerve pathologies, thereby ensuring comprehensive assessment and elevating patient care. These biomarkers provide valuable insights into nerve structure, function, and pathophysiology, contributing to the accurate diagnosis and management planning for peripheral neuropathy.

Evidence of nerve hypertrophy in patients with inclusion body myositis on lower limb MRI

INTRODUCTION/AIMS

Inclusion body myositis (IBM) is a myopathic condition but in some patients has been associated with an axonal length-dependent polyneuropathy. In this study, we quantified the cross-sectional area of the sciatic and tibial nerves in patients with IBM comparing with Charcot-Marie-Tooth disease type 1A (CMT1A) and healthy controls using magnetic resonance neurography (MRN).

METHODS

MRN of the sciatic and tibial nerves was performed at 3T using MPRAGE and Dixon acquisitions. Nerve cross-sectional area (CSA) was measured at the mid-thigh and upper third calf regions by an observer blinded to the diagnosis. Correlations were performed between these measurements and clinical data.

RESULTS

A total of 20 patients with IBM, 20 CMT1A and 29 healthy controls (age- and sex-matched) were studied. Sciatic nerve CSA was significantly enlarged in patients with IBM and CMT1A compared to controls (sciatic nerve mean CSA 62.3 ± 22.9 mm² (IBM) vs. 35.5 ± 9.9 mm² (controls), p < 0.001; and 96.9 ± 35.5 mm² (CMT1A) vs. 35.5 ± 9.9 mm² (controls); p < 0.001). Tibial nerve CSA was also enlarged in IBM and CMT1 patients compared to controls.

DISCUSSION

MRN reveals significant hypertrophy of the sciatic and tibial nerves in patients with IBM and CMT1A compared to controls. Further studies are needed to correlate with neurophysiological measures and assess whether this finding is useful diagnostically.

Intraepineurial fat quantification and cross-sectional area analysis of the sciatic nerve using MRI in Charcot-Marie-Tooth disease type 1A patients

The objectives of this study were to assess the fat fraction (FF) and cross-sectional area (CSA) of the sciatic nerve in Charcot-Marie-Tooth disease type 1A (CMT1A) patients using Dixon-based proton density fat quantification MRI and to elucidate its potential association with clinical parameters. Thigh MRIs of 18 CMT1A patients and 18 age- and sex-matched volunteers enrolled for a previous study were reviewed. Analyses for FF and CSA of the sciatic nerve were performed at three levels (proximal to distal). CSA and FF were compared between the two groups and among the different levels within each group. The relationship between the MRI parameters and clinical data were assessed in the CMT1A patients. The CMT1A patients showed significantly higher FF at level 3 (p = 0.0217) and significantly larger CSA at all three levels compared with the control participants (p < 0.0001). Comparisons among levels showed significantly higher FF for levels 2 and 3 than for level 1 and significantly larger CSA for level 2 compared with level 1 in CMT1A patients. CSA at level 3 correlated positively with the CMT neuropathy score version 2 (CMTNSv2). In conclusion, the sciatic nerve FF of CMT1A patients was significantly higher on level 3 compared with both the controls and the measurements taken on more proximal levels, suggesting the possibility of increased intraepineurial fat within the sciatic nerves of CMT1A patients, with a possible distal tendency. Sciatic nerve CSA at level 3 correlated significantly and positively with CMTNSv2, suggesting its potential value as an imaging marker for clinical severity.

Segmentation of Peripheral Nerves From Magnetic Resonance Neurography: A Fully-Automatic, Deep Learning-Based Approach

Diagnosis of peripheral neuropathies relies on neurological examinations, electrodiagnostic studies, and since recently magnetic resonance neurography (MRN). The aim of this study was to develop and evaluate a fully-automatic segmentation method of peripheral nerves of the thigh. T2-weighted sequences without fat suppression acquired on a 3 T MR scanner were retrospectively analyzed in 10 healthy volunteers and 42 patients suffering from clinically and electrophysiologically diagnosed sciatic neuropathy. A fully-convolutional neural network was developed to segment the MRN images into peripheral nerve and background tissues. The performance of the method was compared to manual inter-rater segmentation variability. The proposed method yielded Dice coefficients of 0.859 ± 0.061 and 0.719 ± 0.128, Hausdorff distances of 13.9 ± 26.6 and 12.4 ± 12.1 mm, and volumetric similarities of 0.930 ± 0.054 and 0.897 ± 0.109, for the healthy volunteer and patient cohorts, respectively. The complete segmentation process requires less than one second, which is a significant decrease to manual segmentation with an average duration of 19 ± 8 min. Considering cross-sectional area or signal intensity of the segmented nerves, focal and extended lesions might be detected. Such analyses could be used as biomarker for lesion burden, or serve as volume of interest for further quantitative MRN techniques. We demonstrated that fully-automatic segmentation of healthy and neuropathic sciatic nerves can be performed from standard MRN images with good accuracy and in a clinically feasible time.