At present, compressed sensing theo

At present, compressed sensing theory has been widely used in the improvement of medical image reconstruction algorithm, and has made significant progress. Compared with the traditional medical image reconstruction algorithm, the image reconstruction algorithm based on compression perception makes use of the sparsity of image signal, and can accomplish data acquisition and data compression reconstruction at the same time, and can use relatively few sampling data to reconstruct high-quality image, the number of images reduces the number of data sampling, reduces the subsequent data transmission, processing and storage, can well speed up the imaging speed of Mr, the invention solves the fundamental problems of slow imaging speed and large radiation dose of Mr. Therefore, applying compressed sensing theory to the magnetic resonance Imagin, which uses a small amount of k space scan data to reconstruct the MRI images accurately, can effectively shorten the magnetic resonance imagin time, to solve a series of related problems caused by the excessive magnetic resonance imagin. In compressed sensing theory, the sparsity of the original data in the transform domain is taken as a prior knowledge, and the sparse original data in the transform domain is sampled by using the measurement matrix. Finally, the original signal is reconstructed by solving the CONVEX optimization problem through an optimization algorithm. In this paper, the theory of compression perception is introduced from three aspects: Sparse Representation of signal, design of measurement matrix and nonlinear reconstruction algorithm.