Quantitative MR Susceptibility Mapping Using Compressive Sensing From The Magnetic Field

Quantitative susceptibility imaging (QSM) is a new magnetic resonance imaging technology, is one of the hot issues in the field of magnetic resonance imaging currently. Compared to the classic MRI technology, QSM due to the magnetic properties of the local tissue can be measured quantitatively, so that it has a very attractive prospect in areas such as medical research and clinical medicine. For example, neurological disorders such as Alzheimer’s early to predict; bone lesions in pre-diagnosis; effective monitoring of radioactive therapy.However, due to the conical surface of the main magnetic field angle of54.7°, the unit value of zero dipole nuclear imaging and other physical mechanism of reasons, the reconstruction value by direct inversion QSM cut image in noisy areas have more pseudo-shadow, which severely restricts the QSM application performance, but also lead to further promote the use of critical current QSM limited. To this end, the paper in the framework of the theory of compressed sensing reconstruction QSM-depth study of ways to improve the image reconstruction quality QSM to provide high-quality images QSM related applications, related research has important theoretical and practical value. The main contents and results of the paper are as follows:1. solution of partial differential susceptibility image based on improved wound to the background field and pretreatment methods.In this paper, the solution of partial differential equations based on the idea before unwrapping improved methods and to the background field method. At the same time to ensure a stable unwrapping, well removed from the brain tissue or the main external magnetic field inhomogeneity introduced the background field Simulation and actual experimental data show that the proposed algorithm can get much better than paper existing approaches to the solution of the background field winding and the effect of a magnetic field to provide quality data for subsequent QSM reconstruction. 2. In the framework of compressed sensing theory proposed reconstruction method based ontotal variation norm and amplitude constraints.Since the data reconstruction QSM image is essentially a pathological anti-problem solving, critical ill inverse problem solution is to use a variety of prior information. Papers in the framework of the theory of compressed sensing, and go by the data unwrapping background field after pretreatment, the use of the e1total variation norm and amplitude weighted sparse sparse two a priori, can effectively suppress artifacts constructed objective functions and uses alternating optimization algorithm is effective for solving the objective function. Simulation and actual experimental data show that the proposed method in the paper on the original information contained in the image restoration with respect to suppression of artifacts significantly better than currently available methods.