About: Sparse MR Image Reconstruction Considering Rician Noise Models: A CNN Approach

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Sparse MR Image Reconstruction Considering Rician Noise Models: A CNN Approach
Creator	Prasad, M Dhanunjaya Naidu, · Manimala, M Prasad, ·
source	Medline; PMC
abstract	Compressive sensing (CS) provides a potential platform for acquiring slow and sequential data, as in magnetic resonance (MR) imaging. However, CS requires high computational time for reconstructing MR images from sparse k-space data, which restricts its usage for high speed online reconstruction and wireless communications. Another major challenge is removal of Rician noise from magnitude MR images which changes the image characteristics, and thus affects the clinical usefulness. The work carried out so far predominantly models MRI noise as a Gaussian type. The use of advanced noise models primarily Rician type in CS paradigm is less explored. In this work, we develop a novel framework to reconstruct MR images with high speed and visual quality from noisy sparse k-space data. The proposed algorithm employs a convolutional neural network (CNN) to denoise MR images corrupted with Rician noise. To extract local features, the algorithm exploits signal similarities by processing similar patches as a group. An imperative reduction in the run time has been achieved as the CNN has been trained on a GPU with Convolutional Architecture for Fast Feature Embedding framework making it suitable for online reconstruction. The CNN based reconstruction also eliminates the necessity of optimization and prediction of noise level while denoising, which is the major advantage over existing state-of-the-art-techniques. Analytical experiments have been carried out with various undersampling schemes and the experimental results demonstrate high accuracy and consistent peak signal to noise ratio even at 20-fold undersampling. High undersampling rates provide scope for wireless transmission of k-space data and high speed reconstruction provides applicability of our algorithm for remote health monitoring.
has issue date	2020-08-11(xsd:dateTime)
bibo:doi	10.1007/s11277-020-07725-0
bibo:pmid	32836885
has license	no-cc
sha1sum (hex)	7e35c87541eeace12e17e0b831a4bba60d10581d
schema:url	https://doi.org/10.1007/s11277-020-07725-0
resource representing a document's title	Sparse MR Image Reconstruction Considering Rician Noise Models: A CNN Approach
has PubMed Central identifier	PMC7417787
has PubMed identifier	32836885
schema:publication	Wirel Pers Commun
resource representing a document's body	covid:7e35c87541eeace12e17e0b831a4bba60d10581d#body_text
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