Non-local MRI Library-Based Super-Resolution: Application to Hippocampus Subfield Segmentation
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Magnetic Resonance Imaging (MRI) has become one of the most used techniques in research and clinical settings. One of the limiting factors of the MRI is the relatively low resolution for some applications. Although new high resolution MR sequences have been proposed recently, usually these acquisitions require long scanning times which is not always possible neither desirable. Recently, super-resolution techniques have been proposed to alleviate this problem by inferring the underlying high resolution images from low resolution acquisitions. We present a new super-resolution technique that takes benefit from the self-similarity properties of the images and the use of a high resolution image library. The proposed method is compared with related state-of-the-art methods showing a significant reconstruction improvement. Finally, we show the advantage of the proposed framework compared to classic interpolation when used for segmentation of hippocampus subfields.
KeywordsHigh Resolution Image Segmentation Accuracy Montreal Neurological Institute Space Single Magnetic Resonance Imaging Correct Intensity Inhomogeneity
This research was partially supported by the Spanish grant TIN2013-43457-R from the Ministerio de Economia y competitividad. This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project “Défi imag’In”.
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