基于迭代的PROPELLER MRI重建算法

Iterative reconstruction method for PROPELLER MRI

PROPELLER是磁共振成像中能有效消除运动伪影的一种新的采集技术。对于PROPELLER的重建,传统的卷积网格方法由于需要优化大量参数和采样密度补偿过程,重建图像的质量很难得到保证。本文提出使用迭代重建的方法进行PROPLLER的重建,通过加权预条件共轭梯度算法,迭代最小化代价函数,从而得到重建图像。为了提高速度,在每步迭代中,使用NUFFT计算矩阵-向量乘法。通过仿真数据和实际扫描数据比较验证,迭代算法相比卷积网格化方法提高了重建图像信噪比,消除了振铃伪影,并提高了图像的均匀性。

PROPELLER（ periodically rotated overlapping parallel lines with enhanced reconstruction） is a new acquisition technique which can efficiently reduce motion artifacts in MRI imaging. Convolution gridding method usually necessitates lots of parameters optimization and a sampling density compensation step, so the quality of the reconstructed image cannot be ensured. In the paper, an iterative method is applied to reconstruct images for PROPELLER MRI. In the method, a cost function is iteratively minimized by using weighted pre-conditioned conjugate gradient algorithm. In order to improve computation, NUFFT （nonuniform fast Fourier transformation ） is used to computing matrix-vector multiplication. Experimental comparison was made by using both digital phantom data and experimental PROPELLER imaging data. The results showed that the iterative method can improve signal to noise ratio of images and reduce ring artifacts of images in comparison with convolution gridding method. The homogeneity of images can be improved as well.