CAJ | 학술논문

针对磁共振成像技术采样过程过慢的问题，给出一种新的基于压缩感知的图像重建方法。通过分析一种特殊的基于奇异值分解(SVD)的信号稀疏表示方法，提出一种结合稀疏信号位置和大小信息的支撑集混合检测方法，并根据该方法改进稀疏信号重建算法 FCSA。实验结果证明，在相同的欠采样率下，改进 FCSA 算法重建图像的峰值信噪比(PSNR)比传统的基于小波稀疏基的FCSA算法重建图像的PSNR高2.21 dB~12.72 dB，比基于SVD稀疏基的FCSA算法重建图像的PSNR高0.87 dB~2.05 dB，且重建时间从基于小波稀疏基的FCSA算法的103.21 s下降至改进FCSA算法的36.91 s。
침대자공진성상기술채양과정과만적문제，급출일충신적기우압축감지적도상중건방법。통과분석일충특수적기우기이치분해(SVD)적신호희소표시방법，제출일충결합희소신호위치화대소신식적지탱집혼합검측방법，병근거해방법개진희소신호중건산법 FCSA。실험결과증명，재상동적흠채양솔하，개진 FCSA 산법중건도상적봉치신조비(PSNR)비전통적기우소파희소기적FCSA산법중건도상적PSNR고2.21 dB~12.72 dB，비기우SVD희소기적FCSA산법중건도상적PSNR고0.87 dB~2.05 dB，차중건시간종기우소파희소기적FCSA산법적103.21 s하강지개진FCSA산법적36.91 s。
Aiming at the problem of slow sampling time in Magnetic Resonance Imaging(MRI), a new Compressed Sensing(CS) method is proposed. Singular Value Decomposition(SVD)-based sparse representation is an effective but not widely studied method in the CS-MRI field. This sparse representation is improved using the partially known signal support method. A hybrid support detection method is proposed to make use both the position and magnitude knowledge of the sparse signals. This hybrid support detection method is further applied in Fast Composite Splitting Algorithm(FCSA), which is an effective reconstruction algorithm for CS-MRI problem. Experimental results show that the proposed FCSA algorithm outperforms the FCSA with Wavelet method and the FCSA with SVD method in the reconstructed image qualities, its PSNR is 2.21 dB~12.72 dB higher than the FCSA with Wavelet method, 0.87 dB~2.05 dB higher than the FCSA with SVD method, and the reconstruction time is 36.91 s compared with 103.21 s of the FCSA with Wavelet method.