id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-256756-8w5rtucg	Manimala, M. V. R.	Sparse MR Image Reconstruction Considering Rician Noise Models: A CNN Approach	2020-08-11		.txt	text/plain	6681	411	55	The proposed algorithm employs a convolutional neural network (CNN) to denoise MR images corrupted with Rician noise. Dictionary learning for MRI (DLMRI) provided an effective solution to recover MR images from sparse k-space data [2] , but had a drawback of high computational time. The proposed denoising algorithm reconstructs MR images with high visual quality, further; it can be directly employed without optimization and prediction of the Rician noise level. The proposed CNN based algorithm is capable of denoising the Rician noise corrupted sparse MR images and also reduces the computation time substantially. This section presents the proposed CNN-based formulation for denoising and reconstruction of MR images from the sparse k-space data. The proposed CNN based denoising algorithm has been compared with various state-ofthe-art-techniques namely (1) Dictionary learning magnetic resonance imaging (DLMRI) [2] (2) Non-local means (NLM) and its variants namely unbiased NLM (UNLM), Rician NLM (RNLM), enhanced NLM (ENLM) and enhanced NLM filter with preprocessing (PENLM) [5] .	./cache/cord-256756-8w5rtucg.txt	./txt/cord-256756-8w5rtucg.txt