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BACKGROUND: Parsonage-Turner syndrome (PTS) is characterized by severe, acute upper extremity pain and subsequent paresis and most commonly involves the long thoracic nerve (LTN). While MR neurography (MRN) can detect LTN hourglass-like constrictions (HGCs), quantitative muscle MRI (qMRI) can quantify serratus anterior muscle (SAM) neurogenic changes. PURPOSE/HYPOTHESIS: 1) To characterize qMRI findings in LTN-involved PTS. 2) To investigate associations between qMRI and clinical assessments of HGCs/electromyography (EMG). STUDY TYPE: Prospective. POPULATION: 30 PTS subjects (25 M/5 F, mean/range age = 39/15-67 years) with LTN involvement who underwent bilateral chest wall qMRI and unilateral brachial plexus MRN. FIELD STRENGTH/SEQUENCES: 3.0 Tesla/multiecho spin-echo T2-mapping, diffusion-weighted echo-planar-imaging, multiecho gradient echo. ASSESSMENT: qMRI was performed to obtain T2, muscle diameter fat fraction (FF), and cross-sectional area of the SAM. Clinical reports of MRN and EMG were obtained; from MRN, the number of HGCs; from EMG, SAM measurements of motor unit recruitment levels, fibrillations, and positive sharp waves. qMRI/MRN were performed within 90 days of EMG. EMG was performed on average 185 days from symptom onset (all ≥2 weeks from symptom onset) and 5 days preceding MRI. STATISTICAL TESTS: Paired t-tests were used to compare qMRI measures in the affected SAM versus the contralateral, unaffected side (P < 0.05 deemed statistically significant). Kendall's tau was used to determine associations between qMRI against HGCs and EMG. RESULTS: Relative to the unaffected SAM, the affected SAM had increased T2 (50.42 ± 6.62 vs. 39.09 ± 4.23 msec) and FF (8.45 ± 9.69 vs. 4.03% ± 1.97%), and decreased muscle diameter (74.26 ± 21.54 vs. 88.73 ± 17.61 µm) and cross-sectional area (9.21 ± 3.75 vs. 16.77 ± 6.40 mm2 ). There were weak to negligible associations (tau = -0.229 to <0.001, P = 0.054-1.00) between individual qMRI biomarkers and clinical assessments of HGCs and EMG. DATA CONCLUSION: qMRI changes in the SAM were observed in subjects with PTS involving the LTN. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.
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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 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. 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 ± 6.34 msec, standard reconstruction: 27.58 ± 6.34 msec, 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 and muscles involved in peripheral neuropathies from those uninvolved.