螺旋MRI的网格化数据重建算法比较

Comparison of Gridding Algorithms Used in Spiral Magnetic Resonance Imaging

螺旋MRI的原始数据是在不均匀的k-空间螺旋轨迹上采样得到的,需要通过网格化算法等手段将数据变成等间距的网格数据后,才能采用FFT进行重建,最后得到供临床使用的图像.本文对Jackson网格化算法和Claudia大矩阵算法的重建速度和图像结果进行了比较,并得出以下结论1)在获得相近图像质量的情况下,Claudia大矩阵重采样算法比Jack-son算法要快且更方便在仪器上实现.2)在Jackson双倍细网格算法的实现方式中,数据驱动插值比网格驱动插值更有效率.3)在Claudia大矩阵重采样算法中,对冗余比大于310∶1的数据进行图像重建的时候,网格点的幅值不平均化比平均化后的效果还要好.这几个结论都将有利于MRI图像重建技术的进一步提高.

Spiral magnetic resonance imaging （MRI） data are sampled in the k-space with non-uniform spiral trajectory so that gridding must first be performed to transform the raw data into uniform space before fast Fourier transform （FFT reconstruction） can be used to reconstruct images. In this paper, we compared two classes of gridding algorithms, the large matrix resampling algorithm proposed by Claudia et al and the doublesized gridding algorithm proposed by Jackson et al, with respect to the speed of computation and the quality of resultant images. First, it was shown that, while capable of getting images with similar quality relative to those obtained by the latter, the former algorithm is faster and easier to implement. Secondly, our results showed that datadriven interpolation is more efficient than grid-driven interpolation when the doublesized gridding algorithm is used. Lastly, in the large matrix resampling algorithm, when the efficient-versus-redundancy data ratio surpasses 310 ： 1, it is more efficient not to average the values of gridding points of the large matrix. The conclusions are helpful to improve the gridding and reconstruction techniques used in spiral MRI.