An Adaptive Algorithm for Compressively Sampled MR Image Reconstruction Using Projections onto \(l_{p}\)-Ball
- 作者: Kaleem M.1, Qureshi M.1, Omer H.1
-
隶属关系:
- COMSATS Institute of Information Technology
- 期: 卷 47, 编号 4 (2016)
- 页面: 415-428
- 栏目: Article
- URL: https://journals.rcsi.science/0937-9347/article/view/247452
- DOI: https://doi.org/10.1007/s00723-016-0761-0
- ID: 247452
如何引用文章
详细
Compressed sensing (CS) is an emerging technique for magnetic resonance imaging (MRI) reconstruction from randomly under-sampled k-space data. CS utilizes the reconstruction of MR images in the transform domain using any non-linear recovery algorithm. The missing data in the \(k\)-space are conventionally estimated based on the minimization of the objective function using \(l_{1} - l_{2}\) norms. In this paper, we propose a new CS-MRI approach called tangent-vector-based gradient algorithm for the reconstruction of compressively under-sampled MR images. The proposed method utilizes a unit-norm constraint adaptive algorithm for compressively sampled data. This algorithm has a simple design and has shown good convergence behavior. A comparison between the proposed algorithm and conjugate gradient (CG) is discussed. Quantitative analyses in terms of artifact power, normalized mean square error and peak signal-to-noise ratio are provided to illustrate the effectiveness of the proposed algorithm. In essence, the proposed algorithm improves the minimization of the quadratic cost function by imposing a sparsity inducing \(l_{p}\)-norm constraint. The results show that the proposed algorithm exploits the sparsity in the acquired under-sampled MRI data effectively and exhibits improved reconstruction results both qualitatively and quantitatively as compared to CG.
作者简介
Muhammad Kaleem
COMSATS Institute of Information Technology
编辑信件的主要联系方式.
Email: kaleem.arfeen@gmail.com
巴基斯坦, Islamabad
Mahmood Qureshi
COMSATS Institute of Information Technology
Email: kaleem.arfeen@gmail.com
巴基斯坦, Islamabad
Hammad Omer
COMSATS Institute of Information Technology
Email: kaleem.arfeen@gmail.com
巴基斯坦, Islamabad