Zhengce Wang1,2, Kaixuan Zhao1,2, Zhongbiao Xu1,2, Yanqiu Feng1,2. 1. School of Biomedical Enginnering, Guangzhou 510515, China. 2. Guangdong Provincial Key Laboratory of Medical Imaging Processing, Southern Medical University, Guangzhou 510515, China.
Abstract
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. CONCLUSIONS: 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.
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. CONCLUSIONS: 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.
Keywords:
Gibbs artifacts; interlaced local variation; local subpixel shift; magnetic resonance imaging; zero padding
Authors: Daniele Perrone; Jan Aelterman; Aleksandra Pižurica; Ben Jeurissen; Wilfried Philips; Alexander Leemans Journal: Neuroimage Date: 2015-07-02 Impact factor: 6.556