Research On Magnetic Resonance Image Reconstruction Method Based On Compressed Sensing

Magnetic resonance imaging(MRI)is one of the important medical aids for its nonionizing radiation,excellent depiction of soft tissues and the arbitrary imaging.However,the scan time of MRI is relatively long,and it is hard to balance the relationship between the quality of reconstructed images and the scan time.Increasing the scan time means more data for reconstruction and higher quality images,but it may cause a number of problems including increasing the susceptibility to physiological motion artifacts and adding discomfort of the patients.Nevertheless,reducing the amount of data will lead to the decline of image quality.Therefore,the reconstruction of clear MRI images with less data is of great clinical significance.Based on compressed sensing,the sparse reconstruction algorithms of static MRI and dynamic MRI are studied separately,details are as follows:In order to solve the problem of long scan time,a static MRI reconstruction algorithm based on Shearlet and nonlocal structural similarity is proposed.In the algorithm,Shearlet transform is used to decompose images in multiple scales and directions,with which the features of images can be extracted more fully and the images can be represented more sparsely.In addition,combining the nonlocal structural similarity,the blocks of the image are weighted and summed in the iteration to ensure the detail information.Experimental results on the public static MRI dataset show that the proposed algorithm can reconstruct static MRI images with 10%data as well as ensure details,and the PSNR and SSIM of the reconstructed image are improved by 3dB and 0.2 respectively.In dynamic magnetic resonance imaging,a dynamic MRI sparse reconstruction algorithm based on temporal and spatial sparsity is proposed to shorten the scan time of a single frame image so as to obtain the motion states of organs.In the algorithm,the dynamic total variation is applied to represent the image sequences sparsely,which utilizes the temporal redundancy of dynamic MRI images.In addition,the spatial sparsity of images in the double tree complex wavelet transform domain is applied to better represent the features of images.Experimental results on the public dynamic MRI dataset show that the proposed algorithm can reconstruct images with 14%data and reduce the time by 0.4 seconds with the same reconstruction quality as other methods.