Abstract:

The sensitivity encoding ( SENSE) technique utilizes sensitivity information from multiple receiving coils to reduce scan time. The existing SENSE-based parallel MRI reconstruction methods have problems of artifacts and missing details, which is not conducive to clinical diagnosis. By introducing data-driven adaptive sparse transform learning (TL) into the SENSE algorithm, TL-SENSE algorithm is proposed, that reduce the artifacts and improve the quality of parallel MRI reconstruction. The proposed algorithm employs the alternating direction method of multipliers (ADMM) to solve the target optimization problem. And The proposed algorithm comprises three steps: transform updating, hard threshold denoising and image updating. The simulation results show that the proposed algorithm performs well in image denoising and restoration and preserves the texture details and edge information. It also achieves higher consistency between the reconstructed image and the original image. For the selected 48 sets of data, the average signal noise ratio of TL-SENSE increased by 4.62 dB, 1.91 dB, 1.30 dB and 0.89 dB compared with that of SENSE, L1-SENSE, TV-SENSE, and LpTV-SENSE, respectively.

Key words: parallel magnetic resonance imaging, transform learning, sensitivity encoding, alternating direction method of multipliers