Passive shimming optimization method of MRI based on genetic algorithm-sequential quadratic programming
A genetic algorithm-sequential quadratic programming(GA-SQP)was proposed to improve the uniformity performance of main magnetic field(B0)in 7 T magnetic resonance imaging(MRI),in order to solve the inherent problem of uneven B0 field in MRI system.From the perspective of the mathematical model of passive shimming,a stable initial solution was obtained with the GA algorithm to achieve the first optimization of B0 field,and then the second optimization of the main magnetic field was realized in less time through the rapid solution of the SQP algorithm,and the uniformity of B0 of MRI was significantly improved.Additionally,L1-Norm regularization method was utilized to reduce the weight of the iron sheets and obtain a sparse iron distribution.Through simulation-based case studies,a bare magnetic field successfully shimmed with an uniformity of 462 × 10-6 to 4.5 × 10-6,using only 0.8 kg of iron pieces on shimming space.The magnetic field uniformity of the new solution was improved by 96.7％and the total iron sheet consumption weight was reduced by 85.7％,compared with those of the traditional GA optimization method.Experimental results show that the GA-SQP algorithm is more robust and competitive than other optimization algorithms.
magnetic resonance imagingpassive shimminggenetic algorithm-sequential quadratic program-ming(GA-SQP)regularization methodnonlinear programming
万方数据
维普
