Single-image super-resolution of brain MR images using overcomplete dictionaries

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.

Figure optionsDownload high-quality image (141 K)Download as PowerPoint slideHighlights
► Technique that combines a multi-scale analysis with a dimensionality reduction scheme.
► A multi-scale edge analysis allows to estimate the missing high-frequency information.
► Dictionaries are constructed with prior information from similar brain MR images.
► Sparse representation framework allows to build particular patterns from dictionaries.
► Extensive validation demonstrates a substantial impact in brain tissue segmentation.