MRI Reconstruction From 2D Partial k-Space Using POCS Algorithm

被引：0
|
作者：
Chen, Jiaming [1 ]
Zhang, Lu [1 ]
Zhu, Yuemin [2 ,3 ]
Luo, Jianhua [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Coll Life Sci & Technol, Shanghai 200240, Peoples R China
[2] CNRS, CREATIS, UMR 5515, F-69100 Villeurbanne, France
[3] INSERM Unit, F-69621 Paris, France
来源：
2009 3RD INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL ENGINEERING, VOLS 1-11 | 2009年
关键词：
POCS; 2D partial k-space; MRI reconstruction;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
In the field of magnetic resonance imaging (MRI), reconstruction from partial k-space data is a common strategy. Projection onto convex set (POCS) method has been applied to dealing with one-dimensional (1D) partial k-space. This paper applied POCS method to MR imaging from two-dimensional (2D) partial k-space data. This method is evaluated with experiments using simulate data. Compared with the zero filling imaging (ZFI) method, reconstruction results of POCS method are much better, especially by selecting partial k-space data of an optimal location. The results show that POCS method however failed to restore some missing data that has no symmetrical part in the original k-space data, leading to inevitable reconstruction errors.
引用
收藏
页码：2662 / +
页数：3
相关论文
共 13 条
  • [1] MRI Reconstruction From Sparse K-Space Data Using Low Dimensional Manifold Model
    Abdullah, Saim
    Arif, Omar
    Arif, M. Bilal
    Mahmood, Tahir
    IEEE ACCESS, 2019, 7 : 88072 - 88081
  • [2] Multi-echo reconstruction from partial K-space scans via adaptively learnt basis
    Maggu, Jyoti
    Singh, Prerna
    Majumdar, Angshul
    MAGNETIC RESONANCE IMAGING, 2018, 45 : 105 - 112
  • [3] Compressed sensing using a deep adaptive perceptual generative adversarial network for MRI reconstruction from undersampled K-space data
    Wu, Kun
    Xia, Yan
    Ravikumar, Nishant
    Frangi, Alejandro F.
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2024, 96
  • [4] MRI Reconstruction by Completing Under-sampled K-space Data with Learnable Fourier Interpolation
    Ding, Qiaoqiao
    Zhang, Xiaoqun
    MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT VI, 2022, 13436 : 676 - 685
  • [5] Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction
    Xin, Bingyu
    Ye, Meng
    Axel, Leon
    Metaxas, Dimitris N.
    STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. REGULAR AND CMRXRECON CHALLENGE PAPERS, STACOM 2023, 2024, 14507 : 261 - 273
  • [6] Effectively Training MRI Reconstruction Network via Sequentially Using Undersampled k-Space Data with Very Low Frequency Gaps
    Xing, Tian-Yi
    Li, Xiao-Xin
    Chen, Zhi-Jie
    Zheng, Xi-Yu
    Zhang, Fan
    BIOINFORMATICS RESEARCH AND APPLICATIONS, ISBRA 2022, 2022, 13760 : 30 - 40
  • [7] A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects
    Gao, Chang
    Shih, Shu-Fu
    Finn, J. Paul
    Zhong, Xiaodong
    MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT VI, 2022, 13436 : 726 - 736
  • [8] Iterative next-neighbor regridding (INNG):: Improved reconstruction from nonuniformly sampled k-space data using rescaled matrices
    Moriguchi, H
    Duerk, JL
    MAGNETIC RESONANCE IN MEDICINE, 2004, 51 (02) : 343 - 352
  • [9] Two-stage neural network models for MR image reconstruction from sparsely sampled k-space
    Karras, DA
    Mertzios, BG
    Graveron-Demilly, D
    van Ormondt, D
    2004 IEEE INTERNATIONAL WORKSHOP ON IMAGING SYSTEMS AND TECHNIQUES (IST), 2004, : 123 - 128
  • [10] A new iterative reconstruction algorithm for 2D exterior fan-beam CT
    Zeng, Li
    Liu, Baodong
    Liu, Linghui
    Xiang, Caibing
    JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY, 2010, 18 (03) : 267 - 277
12