一种适用于真实人体数据能有效消除Gibbs伪影的MR重建新算法

A Novel Algorithm to Reduce the Gibbs Ringing Artifacts in Vivo MRI

Gibbs振铃是在磁共振成像中常见的主要存在于组织边缘处的一种伪影,它是在采用部分k空间数据进行图像重建时产生的.Gegenbauer重建方法能够有效消除Gibbs环状伪影并能保持图像高分辨率,但重建时间长且参数的选择对重建结果影响很大.文中引入逆多项式方法对Gegenbauer重建方法进行了改进,同时以Chebyshev多项式替代Gegenbauer多项式,免去了参数的人为选择,提高了重建精度并加快了速度.由于上述方法是针对连续区间讨论的,因此如何通过边缘检测准确地划分连续子区间显得尤为重要.文中提出的频域滤波边缘检测法能得到准确的边缘检测结果,有效地提高了文中方法对具有复杂组织结构的真实人体MR数据重建的精度,使其更具实用性.

Gibbs ringing in magnetic resonance imaging is a well know artifact which is prevalent particularly at the tissue boundaries, this phenomena results from the reconstruction procedure involving only part of the k-space data. The Gegenbauer reconstruction method has been shown to be able to eliminate Gibbs artifacts effectively while retaining high resolution. Its disadvantages include time-consuming and the reconstruction result depending on the selection of parameters greatly. In this paper, the authors improve the Gegenbauer method by introducing the Inverse Polynomial Reconstruction Method （IPRM） and replacing the Gegenbauer polynomial with Chebyshev polynomial. The new method reduces the construction error and computational cost effectively without any need to select the parameters. Because the method above is discussed in smooth interval, the edge detection becomes critical in determining the smooth intervals for high resolution reconstruction. This paper presents an edge detection method which can achieve precise edge effectively and make the new reconstruction method suitable both in vitro and vivo.