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Composite MR image reconstruction and unaliasing for general trajectories using neural networks

Sinha, Neelam and Ramakrishnan, AG and Saranathan, Manojkumar (2010) Composite MR image reconstruction and unaliasing for general trajectories using neural networks. In: Magnetic Resonance Imaging, 28 (10). pp. 1468-1484.

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Official URL: http://dx.doi.org/10.1016/j.mri.2010.06.021


In rapid parallel magnetic resonance imaging, the problem of image reconstruction is challenging. Here, a novel image reconstruction technique for data acquired along any general trajectory in neural network framework, called ``Composite Reconstruction And Unaliasing using Neural Networks'' (CRAUNN), is proposed. CRAUNN is based on the observation that the nature of aliasing remains unchanged whether the undersampled acquisition contains only low frequencies or includes high frequencies too. Here, the transformation needed to reconstruct the alias-free image from the aliased coil images is learnt, using acquisitions consisting of densely sampled low frequencies. Neural networks are made use of as machine learning tools to learn the transformation, in order to obtain the desired alias-free image for actual acquisitions containing sparsely sampled low as well as high frequencies. CRAUNN operates in the image domain and does not require explicit coil sensitivity estimation. It is also independent of the sampling trajectory used, and could be applied to arbitrary trajectories as well. As a pilot trial, the technique is first applied to Cartesian trajectory-sampled data. Experiments performed using radial and spiral trajectories on real and synthetic data, illustrate the performance of the method. The reconstruction errors depend on the acceleration factor as well as the sampling trajectory. It is found that higher acceleration factors can be obtained when radial trajectories are used. Comparisons against existing techniques are presented. CRAUNN has been found to perform on par with the state-of-the-art techniques. Acceleration factors of up to 4, 6 and 4 are achieved in Cartesian, radial and spiral cases, respectively. (C) 2010 Elsevier Inc. All rights reserved.

Item Type: Journal Article
Publication: Magnetic Resonance Imaging
Publisher: Elsevier Science
Additional Information: Copyright of this article belongs to Elsevier Science.
Keywords: Parallel magnetic resonance imaging; Undersampling; Non-Cartesian sampling; Unaliasing; Neural networks
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 21 Dec 2010 08:25
Last Modified: 21 Dec 2010 08:25
URI: http://eprints.iisc.ac.in/id/eprint/34556

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