NDLI: High Throughput MR Image Reconstruction Using Compressed Sensing

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High Throughput MR Image Reconstruction Using Compressed Sensing

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High Throughput MR Image Reconstruction Using Compressed Sensing

Content Provider	ACM Digital Library
Author	Deka, Bhabesh Datta, Sumit
Abstract	Magnetic resonance imaging (MRI) is an essential soft tissue imaging technique. Major limitation of this imaging technique is due to its slow acquisition. MR image reconstruction using the compressed sensing (CS) has mainly two research areas, one, how efficiently MRI data can be acquired and the other is how fast the reconstruction can be done without degrading the quality of the reconstructed images. From the recent study, it is observed that the TV-l1 – l2 model for MR image reconstruction from random under-sampled data gives the best result. In this paper, we propose a novel high throughput MR image reconstruction algorithm without compromising the quality. The experimental results show that the proposed method is quite efficient compared to the state-of-the-art MR image reconstruction techniques based on compressed sensing in terms of the cpu time and the quality of the reconstructed images.
Starting Page	1
Ending Page	6
Page Count	6
File Format	PDF
ISBN	9781450330619
DOI	10.1145/2683483.2683573
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-12-14
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Wavelets K-space Partial fourier Mri Total variation Compressed sensing Image reconstruction
Content Type	Text
Resource Type	Article

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