| Citation: | DUAN J Z，WANG C J. Sensitivity encoding reconstruction algorithm based on multi-category dictionary learning[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（7）：2123-2132 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0571
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The sensitivity encoding (SENSE) method explicitly utilizes sensitivity information from multiple receiving coils to reduce scan time. The images reconstructed using the SENSE model have a portion of blurring artifacts that are not conducive to medical diagnosis. We propose a sensitivity coding reconstruction approach based on multi-classification dictionary learning to minimize overlap artifacts and enhance the quality of parallel magnetic resonance imaging by integrating fast dictionary learning on classed patches into the SENSE model. In order to obtain picture reconstructions using alternating direction method of multipliers, the algorithm first classifies the image blocks and then trains multiple dictionaries of various classes in each category. The results on the human brain and knee data show that the algorithm improves the average signal-to-noise ratio by 1.53 dB, 1.22 dB and 1.05 dB over the TV-SENSE, TV-LORAKS-SENSE and LpTV-SENSE algorithms, respectively. The reconstructed image is in high agreement with the reference image, and the image detail part and edge contour information are kept intact.
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