MRI resolution enhancement using total variation regularization

Authors:
Shantanu H. Joshi;Antonio Marquina;Stanley J. Osher;Ivo Dinov;John D. Van Horn;Arthur W. Toga
Affiliations:
Laboratory of Neuroimaging, University of California, Los Angeles, CA;Departamento de Matematica Aplicada, Universidad de Valencia, Burjassot, Spain;Department of Mathematics, University of California, Los Angeles, CA;Laboratory of Neuroimaging, University of California, Los Angeles, CA;Laboratory of Neuroimaging, University of California, Los Angeles, CA;Laboratory of Neuroimaging, University of California, Los Angeles, CA
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Abstract

We propose a novel method for resolution enhancement for volumetric images based on a variational-based reconstruction approach. The reconstruction problem is posed using a deconvolution model that seeks to minimize the total variation norm of the image. Additionally, we propose a new edge-preserving operator that emphasizes and even enhances edges during the up-sampling and decimation of the image. The edge enhanced reconstruction is shown to yield significant improvement in resolution, especially preserving important edges containing anatomical information. This method is demonstrated as an enhancement tool for low-resolution, anisotropic, 3D brain MRI images, as well as a pre-processing step to improve skull-stripping segmentation of brain images.