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dc.contributor.authorKiragu, Henry
dc.contributor.authorMwangi, Elijah
dc.contributor.authorKamucha, George
dc.date.accessioned2018-07-23T06:59:15Z
dc.date.available2018-07-23T06:59:15Z
dc.date.issued2018
dc.identifier.citationKiragu, Henry, Elijah Mwangi, and George Kamucha. "A rapid MRI reconstruction method based on compressive sampling and concomitant artifacts suppression." 2018 19th IEEE Mediterranean Electrotechnical Conference (MELECON). IEEE, 2018.en_US
dc.identifier.urihttps://ieeexplore.ieee.org/abstract/document/8379093/
dc.identifier.urihttp://hdl.handle.net/11295/103482
dc.description.abstractIn this paper, a proposed rapid Magnetic Resonance Imaging (MRI) reconstruction method is presented. The method is based on the compressibility of the images in the wavelet domain and suppression of the resulting artifacts. The proposed method commences with under-sampling of the k-space data of an image using a few selected phase encoding gradient steps. The under-sampled k-space is then converted into the image which exhibits both aliasing and Gibb's ringing artifacts. The corrupted image is then randomly sampled and reconstructed using Compressive Sampling (CS). The concomitant artifacts that result in loss of high frequency image details are then suppressed using a proposed filter function. The performance of the proposed method is compared to those of other reported CS methods using the Structural SIMilarity (SSIM) and the Peak Signal to Noise Ratio (PSNR) measures. Experimental results reveal that, the proposed method requires 20 % fewer measurements than the other methods for a given image reconstruction quality. This translates to a corresponding reduction in the MRI scan time.en_US
dc.language.isoenen_US
dc.publisherUniversity of Nairobien_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.titleA rapid MRI reconstruction method based on compressive sampling and concomitant artifacts suppressionen_US
dc.typeArticleen_US


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