磁共振成像PROPELLER数据网格化中的采样密度补偿新算法

PROPELLER磁共振数据的重建是一个典型的K空间非笛卡尔采样数据的重建问题.由于现有网格化重建算法中的密度补偿需要计算每个采样点的密度补偿系数,须对非笛卡尔分布的数据进行卷积运算,给定N采样点,该卷积运算需要N×N/2次距离运算,由于PROPELLER采集的数据量N很大,计算耗时非常长.本文提出PROPELLER数据网格化重建中的密度补偿新算法,通过基于网格化分量全为1的向量来计算在均匀网格点上的采样密度分布值进而加以补偿,使得算法复杂度大大下降.实验表明,本文算法比现有算法的运行时间缩短400多倍,而重建质量与原有算法基本相同.

A New Algorithm for Sampling Density Compensation in the Gridding of PROPELLER MRI Data

The reconstruction of PROPELLER MRI data is a typical reconstruction of non-Cartesian data.The sampling den-sity compensation in the current gridding reconstruction is accomplished through the convolution of non-Cartesian data,which re-quires calculating a large number of distances between each other non-Cartesian positions when the number of data is large such as PROPELLER.Therefore the density compensation is highly computationally intensive.In this paper,a new algorithm was proposed for density estimation based on gridding a unity data vector.Since no more computations of distances were needed,the time com-plexity of the algorithm was decreased greatly.In the experiments,the proposed algorithm is shown to be able to perform the density compensation much more rapidly than the traditional algorithm while achieving equivalent quality of reconstruction.