Abstract

© 2016 IEEE. In this paper, a new single-image super-resolution method is presented to increase the spatial resolution of metabolite maps computed from magnetic resonance spectroscopic imaging. The proposed method is based on a non-local patch-based strategy that uses a high resolution T1-weighted image to regularise the super-resolution process. The method is implemented in a multi-scale fashion. The accuracy of the method is validated on both phantom and in vivo images. Both qualitative and quantitative validation suggest that the method has potential for clinically relevant neuroimaging applications.

Keywords:
Image resolution Imaging phantom Resolution (logic) Magnetic resonance imaging Computer science Artificial intelligence High resolution Computer vision Sub-pixel resolution Image (mathematics) Nuclear magnetic resonance Image processing Optics Physics Digital image processing Remote sensing Radiology Geology Medicine

Metrics

5
Cited By
0.28
FWCI (Field Weighted Citation Impact)
17
Refs
0.66
Citation Normalized Percentile
Is in top 1%
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Citation History

Topics

Advanced MRI Techniques and Applications
Health Sciences →  Medicine →  Radiology, Nuclear Medicine and Imaging
Advanced Image Processing Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Image and Signal Denoising Methods
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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