Partial Discreteness: A Novel Prior for Magnetic Resonance Image Reconstruction

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.

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“…6a) and 320 × 320 human knee image (Fig. 6b) from the dataset of [44]. The random undersampling of Fig.…”

Section: Analysis Of Undersampling Limit and Schemesmentioning

confidence: 99%

“…6 . a Pseudoradial mask [ 44 ], b noisy brain image, c reconstructed brain image, d Cartesian mask [ 44 ], e noisy knee image, f reconstructed knee image, g random mask [ 44 ], h noisy axial brain image, i denoised axial brain image …”

Section: Performance Evaluationmentioning

confidence: 99%

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

Manimala

Naidu²,

Prasad

2020

Wireless Pers Commun

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Comparative Analysis of Medical Imaging Techniques Used for the Detection of Thyroid Gland with an Emphasis on Thermogram

Drakshaveni¹,

Hamsavath²

2022

Emerging Research in Computing, Information, Communication and Applications

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A singular K-space model for fast reconstruction of magnetic resonance images from undersampled data

Luo

Mou²,

Qin

et al. 2017

Med Biol Eng Comput

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Reconstructing magnetic resonance images from undersampled k-space data is a challenging problem. This paper introduces a novel method of image reconstruction from undersampled k-space data based on the concept of singularizing operators and a novel singular k-space model. Exploring the sparsity of an image in the k-space, the singular k-space model (SKM) is proposed in terms of the k-space functions of a singularizing operator. The singularizing operator is constructed by combining basic difference operators. An algorithm is developed to reliably estimate the model parameters from undersampled k-space data. The estimated parameters are then used to recover the missing k-space data through the model, subsequently achieving high-quality reconstruction of the image using inverse Fourier transform. Experiments on physical phantom and real brain MR images have shown that the proposed SKM method constantly outperforms the popular total variation (TV) and the classical zero-filling (ZF) methods regardless of the undersampling rates, the noise levels, and the image structures. For the same objective quality of the reconstructed images, the proposed method requires much less k-space data than the TV method. The SKM method is an effective method for fast MRI reconstruction from the undersampled k-space data. Graphical abstract Two Real Images and their sparsified images by singularizing operator.

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Partial Discreteness: A Novel Prior for Magnetic Resonance Image Reconstruction

Cited by 7 publications

References 77 publications

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

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

Comparative Analysis of Medical Imaging Techniques Used for the Detection of Thyroid Gland with an Emphasis on Thermogram

A singular K-space model for fast reconstruction of magnetic resonance images from undersampled data

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