Quantitative double echo steady state T2 mapping of upper extremity peripheral nerves and muscles - Campbell, Gracyn J.; Sneag, Darryl B.; Queler, Sophie C.; Lin, Yenpo; Li, Qian; Tan, Ek T. (Frontiers in Neurology, 2024)
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. Analysis of images from 25 subjects found that DLR decreased DESS-T2 values in muscles (for 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 than normal (abnormal = 37.71±9.11 msec, normal = 27.18±6.34 msec, p<0.001).Higher A higher DESS-T2 in muscle was associated with electromyography findings motor unit recruitment (p<0.001). 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.