Compressively Sampled MRI Recovery Using Modified Iterative-Reweighted Least Square Method
- Autores: Haider H.1, Shah J.1, Qureshi I.2, Omer H.3, Kadir K.4
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Afiliações:
- Department of Electronic Engineering, Faculty of Engineering and Technology, International Islamic University
- Department of Electrical Engineering, Air University, Institute of Signals, Systems and Soft Computing
- Department of Electrical Engineering, COMSATS Institute of Information Technology
- British Malaysian Institute, Universiti Kuala Lumpur
- Edição: Volume 47, Nº 9 (2016)
- Páginas: 1033-1046
- Seção: Article
- URL: https://journals.rcsi.science/0937-9347/article/view/247529
- DOI: https://doi.org/10.1007/s00723-016-0810-8
- ID: 247529
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Resumo
Magnetic resonance imaging (MRI) is a medical imaging modality used for high-resolution soft-tissue imaging of human body. In traditional MRI acquisition methods, sampling is performed at Nyquist rate to store data in k-space. The MR image is recovered using inverse Fast Fourier Transform (FFT). This approach results in slow data acquisition process, which is uncomfortable for the patients. Compressed Sensing (CS) acquisition approach offers nearly perfect recovery of MR image using non-linear reconstruction algorithms even from partial k-space data. This study presents a novel method to reconstruct MR image from highly under-sampled data using modified Iterative-Reweighted Least Square (IRLS) method with additional data consistency constraints. IRLS is an effective numerical method used in convex optimization problems. The proposed algorithm was applied on original human brain and Shepp–Logan phantom image, and the data acquired from the MRI scanner at St. Mary’s Hospital, London. The experimental results show that the proposed algorithm outperforms Projection onto Convex Sets (POCS), Separable Surrogate Functional (SSF), Iterative-Reweighted Least Squares (IRLS), Zero Filling (ZF), and Low-Resolution (LR) methods based on the parameters, e.g. Peak Signal-to-Noise Ratio (PSNR), Improved Signal-to-Noise Ratio (ISNR), Fitness, Correlation, Structural SIMilarity (SSIM) index, and Artifact Power (AP).
Sobre autores
Hassaan Haider
Department of Electronic Engineering, Faculty of Engineering and Technology, International Islamic University
Autor responsável pela correspondência
Email: hassaan.haider@iiu.edu.pk
ORCID ID: 0000-0002-2662-604X
Paquistão, Islamabad
Jawad Shah
Department of Electronic Engineering, Faculty of Engineering and Technology, International Islamic University
Email: hassaan.haider@iiu.edu.pk
Paquistão, Islamabad
Ijaz Qureshi
Department of Electrical Engineering, Air University, Institute of Signals, Systems and Soft Computing
Email: hassaan.haider@iiu.edu.pk
Paquistão, Islamabad
Hammad Omer
Department of Electrical Engineering, COMSATS Institute of Information Technology
Email: hassaan.haider@iiu.edu.pk
Paquistão, Islamabad
Kushsairy Kadir
British Malaysian Institute, Universiti Kuala Lumpur
Email: hassaan.haider@iiu.edu.pk
Malásia, Gombak