Siemens Healthcare Gmbh

King's College London

Aurelien Bustin

Radhouene Neji

Rene Botnar

Claudia Prieto

A method of reconstructing magnetic resonance (MR) image data from k-space data. The method includes obtaining k-space data of an image region of a subject; and reconstructing, using a sparse image coding procedure, the MR image data from the k-space data by performing an iterative optimization method. The optimization method includes a data consistency iteration step and a denoising iteration step applied to MR image data generated by the data consistency iteration step. The denoising iteration step incorporates a sparsifying operation to provide a sparse representation of the MR image data for the imaged region as an input to the data consistency iteration step.

Method of reconstructing magnetic resonance image data

A plurality of sets of k-space data each of the same image region of a subject but having different contrasts are obtained. A sparse image coding procedure is performed to reconstruct a plurality of MR images each corresponding to one of the sets of k-space data. This involves solving an optimization problem comprising a data consistency iteration step used to generate the reconstructed MR images; and a denoising iteration step applied to the reconstructed MR images generated during the data consistency iteration step. The denoising iteration step includes performing a 2D/3D block matching operation to identify similar patches across the reconstructed MR images, and using the similar patches across the reconstructed MR images in a sparsifying operation to provide sparse representations of the reconstructed MR images. The sparse representations are used as an input to the data consistency iteration step.

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London, United Kingdom

Aurelien Bustin