基于局部亚像素移位和隔行局部变差消除Gibbs伪影

Elimination of Gibbs artifact based on local subpixel shift and interlaced local variation

目的将Gibbs伪影消除方法局部亚像素移位(LSS)扩展到k空间零填充重建的磁共振图像。方法提出两种基于LSS的消除k空间零填充重建的磁共振图像中Gibbs伪影的方法。第1种:LSS+图像域插值,该方法首先在非零填充图像上执行原始的LSS方法,使图像上局部变差最小,然后执行图像域插值获得最终图像。第2种:是隔行局部变差法(iLV),该方法首先对k空间数据进行零填充,随后将零填充后的k空间数据通过二维傅里叶变换到图像域,然后使用iLV来搜索最小的隔行局部总变差,从而消除图像的Gibbs伪影。我们将本文提出的两种方法iLV,LSS+插值与原始LSS以及汉明窗滤波器做对比,分别在体模和活体数据中验证了本文提出的两种方法的可行性与鲁棒性。结果两种方法均较LSS与汉明窗滤波器优,在保留图像细节的基础上,极大的消除了Gibbs伪影。iLV和LSS+插值方法相比,iLV方法能够更好地保留图像的细节。结论 iLV与LSS+插值方法均实现了对传统LSS方法的扩展,能很好地消除零填充k空间数据重建图像中的Gibbs伪影,且iLV方法在保留图像细节信息方面更突出。

Objective To extend the application of Gibbs artifact reduction method that exploits local subvoxel-shifts(LSS)to zero-padded k-space magnetic resonance imaging(MRI)data.Methods We investigated two approaches to extending the application of LSS-based method to under-sampled data.The first approach,namely LSS+interpolation,utilized the original LSS-based method to minimize the local variation on nonzero-padding reconstructed images,followed by image interpolation to obtain the final images.The second approach,interlaced local variation,used zero-padded Fourier transformation followed by elimination of Gibbs artifacts by minimizing a novel interlaced local variations(iLV)term.We compared the two methods with the original LSS and Hamming window filter algorithms,and verified their feasibility and robustness in phantom and in vivo data.Results The two methods proposed showed better performance than the original LSS and Hamming window filters and effectively eliminated Gibbs artifacts while preserving the image details.Compared to LSS+interpolation method,iLV method better preserved the details of the images.Conclusion The iLV and LSS+interpolation methods proposed herein both extend the application of the original LSS method and can eliminate Gibbs artifacts in zero-filled k-space data reconstruction images,and iLV method shows a more prominent advantage in retaining the image details.