Research On The Reconstruction Algorithm In Magnetic Particle Imaging

Magnetic Particle Imaging (MPI) is a new type of tomographic Imaging technology.Compared with the predominant imaging technology-Magnetic Resonance Imagingtechnology, MPI reaches a high level of sensitivity and spatial resolution. A complete set ofMagnetic Particle Imaging system includes the excitation fields, the signal pickup assemblyand the image processing part in host computer. The image processing part, which is mainlyresponsible for obtaining the concentration distribution of the magnetic particle samplethrough the reconstruction algorithm by using the nonlinear response of Magneticnanoparticles(MNPs), is the core part of the imaging system.Based on the introduction of a study summarization on Magnetic Particle Imaging bothat home and abroad, this paper did deep research and analyzed the related problems of imagereconstruction in1D MPI. This paper firstly introduced the common attributes of nanometermaterials and the superparamagnetism of MNPs, as well as the basic principle of MPI indetails based on the Langevin theory of magnetism. Then, the mathematical model wasestablished in Matlab according to the imaging principle and two mainstream algorithm-SVD algorithm and LSQR algorithm were used to discuss the factors that influence theaccuracy of the solution concentration, such as acquisition frequency, size distribution ofMNPs, density of spatial coding points，ambient noise and so on. The condition number ofsystem matrix, which is a mathematical quantity that directly related to the solution precision,was found in the process of simulation. The simulation results showed that the conditionnumber of system matrix not only directly influenced the solution precision in the absence ofnoise, but also influenced the noise rejection capability of system with the existence ofambient noise. Finally the Tikhonov regularization method was proposed to improve the algorithm used in MPI, simulation results showed that the improved algorithm caneffectively improve the capability of system to reject ambient noise.