MR Micro-Neurography and a Segmentation Protocol Applied to Diabetic Neuropathy

The aim of this study was to assess with MRI morphometric ultrastructural changes in nerves affected by diabetic peripheral neuropathy (DPN). We used an MR micro-neurography imaging protocol and a semiautomated technique of tissue segmentation to visualize and measure the volume of internal nerve components, such as the epineurium and nerve fascicles. The tibial nerves of 16 patients affected by DPN and of 15 healthy volunteers were imaged. Nerves volume (NV), fascicles volume (FV), fascicles to nerve ratio (FNR), and nerves cross-sectional areas (CSA) were obtained. In patients with DPN the NV was increased and the FNR was decreased, as a result of an increase of the epineurium (FNR in diabetic neuropathy 0,665; in controls 0,699, p = 0,040). CSA was increased in subjects with DPN (12,84 mm(2) versus 10,22 mm(2), p = 0,003). The FV was increased in patients with moderate to severe DPN. We have demonstrated structural changes occurring in nerves affected by DPN, which otherwise are assessable only with an invasive biopsy. MR micro-neurography appears to be suitable for the study of microscopic changes in tibial nerves of diabetic patients

MR microneurography and quantitative T2 and DP measurements of the distal tibial nerve in CIDP

Objective: In this study we investigated the potential of magnetic resonance (MR) micro-neurography to detect morphological and relaxometric changes in distal tibial nerves in patients affected with chronic inflammatory demyelinating polyneuropathy (CIDP), and their associations with clinical and electrophysiological features. Materials and methods: 10 subjects affected with CIDP and 10 healthy subjects were examined. Multiple MR parameters, including the number of fascicles (N), fascicles diameter (FD), total fascicles area (FA), epineurium area (EA), total nerve area (NA), fascicles to nerve ratio (FNR) and quantitative T2 and proton density (PD) were investigated on high resolution MR images of the distal tibial nerve. Those parameters were correlated with clinical scores, age of onset, disease duration and electrophysiologic data. Results: Median NA and FA were significantly increased in the CIDP population (median values for NA in cm2 in CIDP: 0.185; controls: 0.135; p: 0.028; for FA in CIDP 0.136; controls 0.094; p: 0.021). There was no correlation between the parameters investigated and clinical or electrophysiologic features. Conclusion: MR microneurography can detect increased total nerve and fascicle area in distal tibial nerves in CIDP and may be useful for diagnosing CIDP

Corrigendum to “MR microneurography and quantitative T2 and DP measurements of the distal tibial nerve in CIDP” [Journal of the Neurological Sciences Volume 400, 15 May 2019, Pages 15–20](S0022510X19301145)(10.1016/j.jns.2019.03.001)

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Variable echo time imaging for detecting the short T2* components of the sciatic nerve: a validation study

Objective: The aim of this study was to develop and validate an MRI protocol based on a variable echo time (vTE) sensitive to the short T2* components of the sciatic nerve. Materials and methods: 15 healthy subjects (M/F: 9/6; age: 21–62) were scanned at 3T targeting the sciatic nerve at the thigh bilaterally, using a dual echo variable echo time (vTE) sequence (based on a spoiled gradient echo acquisition) with echo times of 0.98/5.37 ms. Apparent T2* (aT2*) values of the sciatic nerves were calculated with a mono-exponential fit and used for data comparison. Results: There were no significant differences in aT2* related to side, sex, age, and BMI, even though small differences for side were reported. Good-to-excellent repeatability and reproducibility were found for geometry of ROIs (Dice indices: intra-rater 0.68–0.7; inter-rater 0.70–0.72) and the related aT2* measures (intra-inter reader ICC 0.95–0.97; 0.66–0.85) from two different operators. Side-related signal-to-noise-ratio non-significant differences were reported, while contrast-to-noise-ratio measures were excellent both for side and echo. Discussion: Our study introduces a novel MR sequence sensitive to the short T2* components of the sciatic nerve and may be used for the study of peripheral nerve disorders

Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy

Purpose: Quantitative MRI (qMRI) plays a crucial role for assessing disease progression and treatment response in neuromuscular disorders, but the required MRI sequences are not routinely available in every center. The aim of this study was to predict qMRI values of water T2 (wT2) and fat fraction (FF) from conventional MRI, using texture analysis and machine learning. Method: Fourteen patients affected by Facioscapulohumeral muscular dystrophy were imaged at both thighs using conventional and quantitative MR sequences. Muscle FF and wT2 were calculated for each muscle of the thighs. Forty-seven texture features were extracted for each muscle on the images obtained with conventional MRI. Multiple machine learning regressors were trained to predict qMRI values from the texture analysis dataset. Results: Eight machine learning methods (linear, ridge and lasso regression, tree, random forest (RF), generalized additive model (GAM), k-nearest-neighbor (kNN) and support vector machine (SVM) provided mean absolute errors ranging from 0.110 to 0.133 for FF and 0.068 to 0.115 for wT2. The most accurate methods were RF, SVM and kNN to predict FF, and tree, RF and kNN to predict wT2. Conclusion: This study demonstrates that it is possible to estimate with good accuracy qMRI parameters starting from texture analysis of conventional MRI