Teodoro Martín-Noguerol1, Rafael Barousse2, Antonio Luna3, Mariano Socolovsky4, Juan M Górriz5,6, Manuel Gómez-Río7,8. 1. MRI unit, Radiology Department, HT Medica, Carmelo Torres 2, 23007, Jaén, Spain. t.martin.f@htime.org. 2. Peripheral Nerve and Plexus Department, Centro Rossi, Sánchez de Loria 117, C1173 AAC, Buenos Aires, Argentina. 3. MRI unit, Radiology Department, HT Medica, Carmelo Torres 2, 23007, Jaén, Spain. 4. Nerve & Plexus Surgery Program, Division of Neurosurgery, Hospital de Clínicas, University of Buenos Aires School of Medicine, Paraguay 2155, C1121 ABG, Buenos Aires, Argentina. 5. Department of Signal Theory, Networking and Communications, University of Granada, Avenida de Fuente Nueva, s/n, 18071, Granada, Spain. 6. Department of Psychiatry, University of Cambridge, Cambridge, CB21TN, UK. 7. Department of Nuclear Medicine, Virgen de las Nieves University Hospital, Av. de las Fuerzas Armadas, 2, 18014, Granada, Spain. 8. IBS Granada Bio-Health Research Institute, Av. de Madrid, 15, 18012, Granada, Spain.
Abstract
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.
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.
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