About this Research Topic
Ideally, optimal management of peripheral neuropathies would require a proper assessment of the nerve’s structural integrity, comprising the exact position, extent, and distribution of structural nerve lesions, the relation of lesions to neighboring and functionally connected organs such as muscles, fibrous tissue strands, and blood vessels, and an assessment of the effect of clinical risk factors and therapeutic strategies on the development of nerve lesions. Most current diagnostic approaches for the assessment of peripheral neuropathies in clinical practice focus on functional and clinical exams such as electrophysiological testing and clinical scores. However, these approaches alone only allow for an indirect assessment of structural nerve changes; electrophysiological testing is only sensitive to the function of the largest, most myelinated nerve fibers, providing little insight into small fibers; they also decrease in diagnostic accuracy for deep-lying neural structures (e.g. nerve plexus), and are dependent on the examiners’ experience and patients’ compliance.
To facilitate and monitor treatment strategies, a reliable diagnostic evaluation of peripheral nerve disease, therefore, needs to provide examiner- and intersubject-independent quantifiable information.
Recent progress in peripheral nerve imaging is based on high-frequency ultrasound and high-field magnetic resonance imaging. While ultrasound exams allow a real-time correlation of peripheral nerve images with clinical symptoms, magnetic resonance imaging, specifically magnetic resonance neurography (MRN), provides strong tissue contrasts and images with a resolution up to 200 µm in vivo, and 30 µm ex vivo in experimental settings, both sufficient to analyze structural changes at a fascicular level, and this will only increase with the availability of high field strength (e.g. 7 Tesla) systems. Their application in animal models of peripheral neuropathies relate these structural nerve changes to histological correlates to obtain validated measures or biomarkers of specific neuropathies, which can be translated and tested in clinical routine.
In addition, recent progress in advanced image analysis methodologies saw machine learning-based peripheral nerve segmentation with good accuracy and three-dimensional reconstruction of nerve fascicles and lesions for automated quantification. Limitations of these approaches are their dependence on image quality based on MR scanner field strength, used MR sequence and coil, amount of nerve lesions, as well as patient positioning in the MR scanner. Future work should strive to overcome these technical limitations by using isotropic MR sequences, higher field strengths, and improved computational segmentation and reconstruction methods. Such techniques will allow describing and cataloging nerve pathologies, providing new insight into the development of peripheral neuropathies and their change during therapy.
This Research Topic is looking for contributions from scientists who investigate the development and implementation of peripheral nerve imaging methodologies with high accuracy levels, providing grounds to improve the application in animal models and in clinical routine.
We welcome manuscript focusing on, but not limited to, the following:
• Testing and applications of MRI sequences to describe and study peripheral nerves and their structural changes in pathology
• Testing of ultrasound techniques to describe and study peripheral nerves and their structural changes in pathology
• Evidence from the combination of MRN and diffusion-weighted images to allow for a better discrimination of peripheral nerves from surrounding tissues
• Computational methods to segment, quantify and analyze peripheral nerve structure
To facilitate and monitor treatment strategies, a reliable diagnostic evaluation of peripheral nerve disease, therefore, needs to provide examiner- and intersubject-independent quantifiable information.
Recent progress in peripheral nerve imaging is based on high-frequency ultrasound and high-field magnetic resonance imaging. While ultrasound exams allow a real-time correlation of peripheral nerve images with clinical symptoms, magnetic resonance imaging, specifically magnetic resonance neurography (MRN), provides strong tissue contrasts and images with a resolution up to 200 µm in vivo, and 30 µm ex vivo in experimental settings, both sufficient to analyze structural changes at a fascicular level, and this will only increase with the availability of high field strength (e.g. 7 Tesla) systems. Their application in animal models of peripheral neuropathies relate these structural nerve changes to histological correlates to obtain validated measures or biomarkers of specific neuropathies, which can be translated and tested in clinical routine.
In addition, recent progress in advanced image analysis methodologies saw machine learning-based peripheral nerve segmentation with good accuracy and three-dimensional reconstruction of nerve fascicles and lesions for automated quantification. Limitations of these approaches are their dependence on image quality based on MR scanner field strength, used MR sequence and coil, amount of nerve lesions, as well as patient positioning in the MR scanner. Future work should strive to overcome these technical limitations by using isotropic MR sequences, higher field strengths, and improved computational segmentation and reconstruction methods. Such techniques will allow describing and cataloging nerve pathologies, providing new insight into the development of peripheral neuropathies and their change during therapy.
This Research Topic is looking for contributions from scientists who investigate the development and implementation of peripheral nerve imaging methodologies with high accuracy levels, providing grounds to improve the application in animal models and in clinical routine.
We welcome manuscript focusing on, but not limited to, the following:
• Testing and applications of MRI sequences to describe and study peripheral nerves and their structural changes in pathology
• Testing of ultrasound techniques to describe and study peripheral nerves and their structural changes in pathology
• Evidence from the combination of MRN and diffusion-weighted images to allow for a better discrimination of peripheral nerves from surrounding tissues
• Computational methods to segment, quantify and analyze peripheral nerve structure
Keywords: peripheral nerves, ultrasound techniques, MRN, machine learning-based peripheral nerve segmentation
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