Research On The Inverse Problem Of Magnetic-acoustic Magnetic Particle Imaging Based On The Method Of Moments

Magnetic-acoustic magnetic particle imaging(MAMPI)is a new tomography technique.It combines the advantages of high sensitivity of magnetic particle imaging and high resolution of ultrasonic imaging,and can reconstruct high-quality images of nano particle distribution,which makes it have broad application prospects in biomedical and molecular imaging.At present,the theory is still in the initial stage of development.The research on algorithm implementation,factors affecting the quality of reconstruction imaging and improvement of image reconstruction algorithm in inverse problem imaging need to be further studied.In view of the above problems,I have carried out the following research:(1)An image reconstruction algorithm for inverse problem of MAMPI based on method of moment was proposed.The method of moment was used to design the algorithm of discrete data for MAMPI inverse problem theory,according to the simulation data,the feasibility of the concentration image reconstruction algorithm of each model was studied,and the quality of the reconstructed image was evaluated quantitatively and the error was analyzed.The results showed that the proposed image reconstruction algorithm can reconstruct the concentration distribution of magnetic nanoparticles.(2)The influence of the radius of the tissue loaded with magnetic nanoparticles and the acoustic impedance of the imaging tissue on the quality of the reconstructed image was studied.The results showed that there is a negative gain relationship between the radius of the imaging model and the quality of the reconstructed image;with the increase of acoustic impedance,the reconstruction error concentration area increases in a certain range,and the image reconstruction quality decreases.(3)In order to reduce the reconstruction error of magnetic nanoparticles concentration image,a total variation regularization algorithm was proposed to optimize the image reconstruction.Firstly,the SART iterative algorithm and non negative constraint were used to get the intermediate results;then,the total variational regularization optimization algorithm is used to perform the total variational minimization constraint on the result;finally,by judging the criterion of iteration termination,the iteration cycle was jumped out.The results showed that the proposed total variation regularization optimization algorithm can produce better constraint effect on the reconstruction error and improve the quality of the reconstructed image.The paper has 32 figures,7 tables,and 74 references.