title: Single-image super-resolution of brain MR images using overcomplete dictionaries.
abstract: Resolution in Magnetic Resonance (MR) is limited by diverse physical, technological and economical considerations. In conventional medical practice, resolution enhancement is usually performed with bicubic or B-spline interpolations, strongly affecting the accuracy of subsequent processing steps such as segmentation or registration. This paper presents a sparse-based super-resolution method, adapted for easily including prior knowledge, which couples up high and low frequency information so that a high-resolution version of a low-resolution brain MR image is generated. The proposed approach includes a whole-image multi-scale edge analysis and a dimensionality reduction scheme, which results in a remarkable improvement of the computational speed and accuracy, taking nearly 26 min to generate a complete 3D high-resolution reconstruction. The method was validated by comparing interpolated and reconstructed versions of 29 MR brain volumes with the original images, acquired in a 3T scanner, obtaining a reduction of 70% in the root mean squared error, an increment of 10.3 dB in the peak signal-to-noise ratio, and an agreement of 85% in the binary gray matter segmentations. The proposed method is shown to outperform a recent state-of-the-art algorithm, suggesting a substantial impact in voxel-based morphometry studies.
authors.affiliation: Computer Imaging and Medical Applications Laboratory-CIM&LAB, Universidad Nacional de Colombia, Bogotá, Colombia.
authors.forename: Andrea
authors.initials: A
authors.lastname: Rueda
authors.forename: Norberto
authors.initials: N
authors.lastname: Malpica
authors.forename: Eduardo
authors.initials: E
authors.lastname: Romero
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*