An Adaptive Algorithm for Compressively Sampled MR Image Reconstruction Using Projections onto \(l_{p}\)-Ball
- Авторлар: Kaleem M.1, Qureshi M.1, Omer H.1
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Мекемелер:
- COMSATS Institute of Information Technology
- Шығарылым: Том 47, № 4 (2016)
- Беттер: 415-428
- Бөлім: Article
- URL: https://journals.rcsi.science/0937-9347/article/view/247452
- DOI: https://doi.org/10.1007/s00723-016-0761-0
- ID: 247452
Дәйексөз келтіру
Аннотация
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.
Негізгі сөздер
Авторлар туралы
Muhammad Kaleem
COMSATS Institute of Information Technology
Хат алмасуға жауапты Автор.
Email: kaleem.arfeen@gmail.com
Пәкістан, Islamabad
Mahmood Qureshi
COMSATS Institute of Information Technology
Email: kaleem.arfeen@gmail.com
Пәкістан, Islamabad
Hammad Omer
COMSATS Institute of Information Technology
Email: kaleem.arfeen@gmail.com
Пәкістан, Islamabad