{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["15"],"submitter":["Campbell GJ"],"pubmed_abstract":["<h4>Introduction</h4>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 ratio may 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 effect 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.<h4>Methods and results</h4>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, <i>p</i> = 0.005) and normal muscles (DLR: 27.18 ± 6.34 msec, standard reconstruction: 27.58 ± 6.34 msec, <i>p</i> < 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, <i>p</i> < 0.001) and muscles (abnormal = 37.71 ± 9.11 msec, normal = 27.18 ± 6.34 msec, <i>p</i> < 0.001). A higher DESS-T2 in muscle was associated with electromyography motor unit recruitment (<i>p</i> < 0.001).<h4>Discussion</h4>These results suggest that quantitative DESS-T2 is improved by DLR and can differentiate the nerves and muscles involved in peripheral neuropathies from those uninvolved."],"journal":["Frontiers in neurology"],"pagination":["1359033"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10902120"],"repository":["biostudies-literature"],"pubmed_title":["Quantitative double echo steady state T2 mapping of upper extremity peripheral nerves and muscles."],"pmcid":["PMC10902120"],"pubmed_authors":["Sneag DB","Campbell GJ","Lin Y","Li Q","Queler SC","Tan ET"],"additional_accession":[]},"is_claimable":false,"name":"Quantitative double echo steady state T2 mapping of upper extremity peripheral nerves and muscles.","description":"<h4>Introduction</h4>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 ratio may 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 effect 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.<h4>Methods and results</h4>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, <i>p</i> = 0.005) and normal muscles (DLR: 27.18 ± 6.34 msec, standard reconstruction: 27.58 ± 6.34 msec, <i>p</i> < 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, <i>p</i> < 0.001) and muscles (abnormal = 37.71 ± 9.11 msec, normal = 27.18 ± 6.34 msec, <i>p</i> < 0.001). A higher DESS-T2 in muscle was associated with electromyography motor unit recruitment (<i>p</i> < 0.001).<h4>Discussion</h4>These results suggest that quantitative DESS-T2 is improved by DLR and can differentiate the nerves and muscles involved in peripheral neuropathies from those uninvolved.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024","modification":"2025-04-04T20:39:27.119Z","creation":"2025-04-04T20:39:27.119Z"},"accession":"S-EPMC10902120","cross_references":{"pubmed":["38426170"],"doi":["10.3389/fneur.2024.1359033"]}}