RESUMO
The measurement of the pole axis deviation angle of superconducting magnetic levitation rotors plays a crucial role in the field of high-precision navigation. This article conducts theoretical analysis and experimental research on the diffuse reflection intensity modulation principle of optical fiber sensors and the ideal diffuse reflection modulation model under ideal conditions. First, the structural distribution of optical fiber sensors is presented, and the principle of measuring the polar deviation angle of superconducting magnetic levitation rotors is analyzed. Then, based on the diffuse reflection intensity modulation principle, an optical fiber intensity modulation model for ideal superconducting magnetic levitation rotors is established. The correctness of this model was verified through simulation and calibration experiments, and the error between the simulation and experimental results was less than 8%.
RESUMO
PURPOSE: To develop and validate a fast dynamic MR imaging scheme. A novel approach termed K-T ARTificial Sparsity enhanced GROWL (K-T ARTS-GROWL) is proposed that integrates dynamic artificial sparsity and GROWL-based parallel imaging (PI). METHODS: Golden-angle radial k-space data are acquired with the free-breathing sampling scheme and then sorted into a time series by grouping consecutive spokes into temporal frames. The reconstruction framework sequentially applies PI and dynamic artificial sparsity. In the implementation, GROWL is taken as a special PI instance for its high computational efficiency and the K-T sparse is exploited to improve the PI reconstruction performance, because the dynamic MR images are often sparse in the x-f domain. In the final reconstruction procedure, artificial sparsity is constructed and fed back to the previous reconstruction. RESULTS: The K-T ARTS-GROWL results in high spatial and temporal resolution reconstructions. By exploiting dynamic artificial sparsity, the acceleration capability is further improved compared to the PI alone. The experimental results demonstrate that K-T ARTS-GROWL leads to significantly better image quality (P < 0.05) than the frame-by-frame GROWL and frame-by-frame ARTS-GROWL for in vivo liver imaging. Compared with the tested K-T reconstruction algorithms, the K-T ARTS-GROWL results in a better or comparable image quality and temporal resolution with greatly decreased computational costs. CONCLUSION: The proposed technique enables sparse, fast imaging of high spatial, high temporal resolutions for dynamic MRI.
