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Background: Magnetic resonance neurography (MRN) is increasingly used as a diagnostic tool for peripheral neuropathies. Quantitative measures enhance MRN interpretation but require nerve segmentation which is time-consuming and error-prone and has not become clinical routine. In this study, we applied neural networks for the automated segmentation of peripheral nerves. Methods: A neural segmentation network was trained to segment the sciatic nerve and its proximal branches on the MRN scans of the right and left upper leg of 35 healthy individuals, resulting in 70 training examples, via 5-fold cross-validation (CV). The model performance was evaluated on an independent test set of one-sided MRN scans of 60 healthy individuals. Results: Mean Dice similarity coefficient (DSC) in CV was 0.892 (95% confidence interval [CI]: 0.888–0.897) with a mean Jaccard index (JI) of 0.806 (95% CI: 0.799–0.814) and mean Hausdorff distance (HD) of 2.146 (95% CI: 2.184–2.208). For the independent test set, DSC and JI were lower while HD was higher, with a mean DSC of 0.789 (95% CI: 0.760–0.815), mean JI of 0.672 (95% CI: 0.642–0.699), and mean HD of 2.118 (95% CI: 2.047–2.190). Conclusion: The deep learning-based segmentation model showed a good performance for the task of nerve segmentation. Future work will focus on extending training data and including individuals with peripheral neuropathies in training to enable advanced peripheral nerve disease characterization. Relevance statement: The results will serve as a baseline to build upon while developing an automated quantitative MRN feature analysis framework for application in routine reading of MRN examinations. Key Points: Quantitative measures enhance MRN interpretation, requiring complex and challenging nerve segmentation. We present a deep learning-based segmentation model with good performance. Our results may serve as a baseline for clinical automated quantitative MRN segmentation. [ABSTRACT FROM AUTHOR]

Background Current state-of-the-art to diagnose peripheral neuropathy are neurological examination and electrodiagnostic studies. However, deeply situated nerves and plexus remain difficult to assess using these techniques, hence magnetic resonance neurography (MRN) emerged as a complementary method. MRN remains a qualitative approach and quantification, in terms of extraction of imaging biomarkers is needed to facilitate diagnosis and for follow-up examinations. Towards a multi-parametric quantitative imaging approach, accurate nerve segmentation needs to be performed first. Hampered by time-consuming manual annotation, we developed a semi-automatic, time-effective, hence clinically feasible segmentation approach based on machine-learning algorithms. In this study, we compared its performance on low- and high-resolution MRN. Materials and methods We acquired images of 9 healthy volunteers at the upper leg using 3 MRN sequences: lower resolution turbo inversion recovery magnitude (TIRM, voxel size of 78 × 78 × 4.0 mm 3 ), lower resolution spin echo T2-weighted (T2, voxel size .52 × .52 × 4.0 mm 3 ), and high resolution, fat-suppressed spin echo T2-weighted (hrT2, voxel size .25 × .25 × 3.3 mm 3 ). An expert manually segmented the sciatic nerve on the T2 and hrT2 images (ground truth, GT). Our segmentation algorithm is based on a decision forest with context- and intensity-based descriptors. We split the images into two sets: a multi-modal low-resolution (TIRM and T2), and a mono-modal high-resolution (hrT2). We perform a leave-one-out cross-validation for both sets and calculate the Dice coefficient to the manually annotated GT. Cross-sectional areas (CSA) were calculated from the middle slice of the image stack. Results Dice coefficients of 0.723 ± 0.202 and 0.735 ± 0.080 are achieved for the low- and high-resolution MRN images ( p  = 0.858, CI 95 %). Segmentation results and 3-D renderings of the same nerve segmented in low- and high-resolution are shown in Fig. 1 . CSA for GT were 34.0 ± 5.5 mm 2 (T2), and 35.4 ± 8.5 mm 2 (hrT2), using semi-automatic segmentation 24.8 ± 7.6 mm 2 (T2), and 25.7 ± 10.6 mm 2 (hrT2). Discussion and conclusion Semi-automatic, hence fast peripheral nerve segmentation is feasible in both low- and high-resolution MRN images of the upper leg, providing image-based quantitative morphometric data. The equal performance of the low-resolution images might be due to the multi-modal setting, which possibly provides more discriminative descriptors for nerve. The lower CSA using semi-automatic segmentation is due to more rigorous segmentation constraints. Future work will include the application of our method to localize and quantify nerve lesions in images of patients diagnosed with peripheral neuropathy. [ABSTRACT FROM AUTHOR]

Introduction: T2 mapping can characterize peripheral neuropathy and muscle denervation due to axonal damage. Three-dimensional double echo steady-state (DESS) can simultaneously provide 3D qualitative information and T2 maps with equivalent spatial resolution. However, insufficient signal-to-noise ratiomay bias DESS-T2 values. Deep learning reconstruction (DLR) techniques can reduce noise, and hence may improve quantitation of high-resolution DESS-T2. This study aims to (i) evaluate the efect of DLR methods on DESS-T2 values, and (ii) to evaluate the feasibility of using DESS-T2 maps to differentiate abnormal from normal nerves and muscles in the upper extremities, with abnormality as determined by electromyography. Methods and results: Analysis of images from 25 subjects found that DLR decreased DESS-T2 values in abnormal muscles (DLR = 37.71 ± 9.11 msec, standard reconstruction = 38.56 ± 9.44 msec, p = 0.005) and normal muscles (DLR: 27.18 ± 6.34msec, standard reconstruction: 27.58 ± 6.34msec, p < 0.001) consistent with a noise reduction bias. Mean DESS-T2, both with and without DLR, was higher in abnormal nerves (abnormal = 75.99 ± 38.21 msec, normal = 35.10 ± 9.78 msec, p < 0.001) and muscles (abnormal = 37.71 ± 9.11 msec, normal = 27.18 ± 6.34 msec, p < 0.001). A higher DESS-T2 in muscle was associated with electromyography motor unit recruitment (p < 0.001). Discussion: These results suggest that quantitative DESS-T2 is improved by DLR and can differentiate the nerves andmuscles involved in peripheral neuropathies from those uninvolved. [ABSTRACT FROM AUTHOR]

Purpose: To perform a review of the physical basis of DTI and DCE-MRI applied to Peripheral Nerves (PNs) evaluation with the aim of providing readers the main concepts and tools to acquire these types of sequences for PNs assessment. The potential added value of these advanced techniques for pre-and post-surgical PN assessment is also reviewed in diverse clinical scenarios. Finally, a brief introduction to the promising applications of Artificial Intelligence (AI) for PNs evaluation is presented. Methods: We review the existing literature and analyze the latest evidence regarding DTI, DCE-MRI and AI for PNs assessment. This review is focused on a practical approach to these advanced sequences providing tips and tricks for implementing them into real clinical practice focused on imaging postprocessing and their current clinical applicability. A summary of the potential applications of AI algorithms for PNs assessment is also included. Results: DTI, successfully used in central nervous system, can also be applied for PNs assessment. DCE-MRI can help evaluate PN's vascularization and integrity of Blood Nerve Barrier beyond the conventional gadolinium-enhanced MRI sequences approach. Both approaches have been tested for PN assessment including pre- and post-surgical evaluation of PNs and tumoral conditions. AI algorithms may help radiologists for PN detection, segmentation and characterization with promising initial results. Conclusion: DTI, DCE-MRI are feasible tools for the assessment of PN lesions. This manuscript emphasizes the technical adjustments necessary to acquire and post-process these images. AI algorithms can also be considered as an alternative and promising choice for PN evaluation with promising results. [ABSTRACT FROM AUTHOR]

Background: Quantitative MR-neurography (MRN) is increasingly applied, however, the impact of the MR-scanner on the derived parameters is unknown. Here, we used different 3.0T MR scanners and applied comparable MR-sequences in order to quantify the inter-scanner reproducibility of various MRN parameters of the sciatic nerve. Methods: Ten healthy volunteers were prospectively examined at three different 3.0T MR scanners and underwent MRN of their sciatic nerve using comparable imaging protocols including diffusion tensor imaging (DTI) and T2 relaxometry. Subsequently, inter-scanner agreement was assessed for seven different parameters by calculating the intraclass correlation coefficients (ICCs) and the standard error of measurement (SEM). Results: Assessment of inter-scanner reliability revealed good to excellent agreement for T2 (ICC: 0.846) and the quantitative DTI parameters, such as fractional anisotropy (FA) (ICC: 0.876), whereas moderate agreement was observed for proton spin density (PD) (ICC: 0.51). Analysis of variance identified significant inter-scanner differences for several parameters, such as FA (p < 0.001; p = 0.02), T2 (p < 0.01) and PD (p = 0.02; p < 0.01; p = 0.02). Calculated SEM values were mostly within the range of one standard deviation of the absolute mean values, for example 0.033 for FA, 4.12 ms for T2 and 27.8 for PD. Conclusion: This study quantifies the measurement imprecision for peripheral nerve DTI and T2 relaxometry, which is associated with the use of different MR scanners. The here presented values may serve as an orientation of the possible scanner-associated fluctuations of MRN biomarkers, which can occur under similar conditions. [ABSTRACT FROM AUTHOR]

Purpose: Diffusion tensor imaging (DTI) is increasingly being used in magnetic resonance neurography (MRN). The purpose of this study was to determine the interreader and test-retest reliability of peripheral nerve DTI in MRN with focus on the sciatic nerve. Methods: In this prospective study 27 healthy volunteers each underwent 3 scans of a short DTI protocol on separate days consisting of a T2-weighted turbo spin-echo and single-shot DTI sequence of the sciatic nerve of the dominant leg. The DTI parameters fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were obtained after manual nerve segmentation by two independent readers. Intraclass correlation coefficients (ICC), standard error of measurement (SEM), and Bland-Altman plots were calculated as measures for both interreader and test-retest agreement for all readout parameters. Results: The mean ± standard deviation was 0.507 ± 0.05 for FA, 1308.5 ± 162.4 × 10−6 mm2/s for MD, 905.6 ± 145.4 ×10−6 mm2/s for RD and 2114.1 ± 219.2 × 10−6 mm2/s for AD. The SEM for FA was 0.02 for interreader and test-retest agreement, the SEM for MD, RD, and AD ranged between 46.2 × 10−6 mm2/s (RD) and 70.1 × 10−6 mm2/s (AD) for interreader reliability and between 45.9 × 10−6 mm2/s (RD) and 70.1 × 10−6 mm2/s (AD) for test-retest reliability. The ICC for interreader reliability of DTI parameters ranged between 0.81 and 0.92 and ICC for test-retest reliability between 0.76 and 0.91. Conclusion: Peripheral nerve DTI of the sciatic nerve is reliable and reproducible. The measures presented here may serve as first orientation values of measurement accuracy when interpreting parameters of sciatic nerve DTI. [ABSTRACT FROM AUTHOR]

Tinel sign is a sensitive clinical indicator for the location of nerve lesions, while magnetic resonance imaging is an efficient tool for lesion diagnosis. Several imaging studies of the Tinel sign with a short distance median nerve have been reported. However, few studies have examined the Tinel sign provoked by a distant lesion or its findings in magnetic resonance neurography. Herein, we report our peripheral nerve conduction and magnetic resonance neurography findings for two women with Tinel signs in the median nerve with a long distance caused by a proximal tumor. Case 1 showed a mass at the proximal superior trunk of the brachial plexus, originating from the fifth cervical spinal root. Case 2 showed a mass at the proximal median nerve at the axilla. There were no changes in symptoms or Tinel signs in either case, and no abnormalities in nerve conduction studies including F waves, for >2 years. [ABSTRACT FROM AUTHOR]