Purpose: Universal Pulses (UPs) are excitation pulses that reduce the flip angle inhomogeneity in high field MRI systems without subject-specific optimization, originally developed for parallel transmit (PTX) systems at 7 T. We investigated the potential benefits of UPs for single channel (SC) transmit systems at 3 T, which are widely used for clinical and research imaging, and for which flip angle inhomogeneity can still be problematic.
Methods: SC-UPs were designed using a spiral nonselective k-space trajectory for brain imaging at 3 T using transmit field maps (B) and off-resonance maps (B) acquired on two different scanner types: a 'standard' single channel transmit system and a system with a PTX body coil. The effect of training group size was investigated using data (200 subjects) from the standard system. The PTX system was used to compare SC-UPs to PTX-UPs (15 subjects). In two additional subjects, prospective imaging using SC-UP was studied.
Results: Average flip angle homogeneity error fell from 9.5 ± 0.5 % for 'default' excitation to 3.0 ± 0.6 % using SC-UPs trained over 50 subjects. Performance of the UPs was found to steadily improve as training group size increased, but stabilized after ~15 subjects. On the PTX-enabled system, SC-UPs again outperformed default excitation in simulations (4.8 ± 0.6 % error versus 10.6 ± 0.8 % respectively) though greater homogenization could be achieved with PTX-UPs (3.9 ± 0.6 %) and personalized pulses (SC-PP 3.6 ± 1.0 %, PTX-PP 2.9 ± 0.6 %). MP-RAGE imaging using SC-UP resulted in greater separation between grey and white matter signal intensities than default excitation.
Conclusions: SC-UPs can improve excitation homogeneity in standard 3 T systems without further calibration and could be used instead of a default excitation pulse for nonselective neuroimaging at 3 T.
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http://dx.doi.org/10.1016/j.mri.2022.07.002 | DOI Listing |
Med Phys
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School of Physics and Optoelectronic Engineering, Foshan University, Foshan, China.
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View Article and Find Full Text PDFBiosensors (Basel)
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School of Materials Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China.
Surface electromyography (sEMG) signals reflect the local electrical activity of muscle fibers and the synergistic action of the overall muscle group, making them useful for gesture control of myoelectric manipulators. In recent years, deep learning methods have increasingly been applied to sEMG gesture recognition due to their powerful automatic feature extraction capabilities. sEMG signals contain rich local details and global patterns, but single-scale convolutional networks are limited in their ability to capture both comprehensively, which restricts model performance.
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