MRI Superresolution Using Self-Similarity and Image Priors
In Magnetic Resonance Imaging typical clinical settings, both low- and high-resolution images of different types are routinarily acquired. In some cases, the acquired low-resolution images have to be upsampled to match with other high-resolution images for posterior analysis or postprocessing such a...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2010-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2010/425891 |
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| _version_ | 1849412656861020160 |
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| author | José V. Manjón Pierrick Coupé Antonio Buades D. Louis Collins Montserrat Robles |
| author_facet | José V. Manjón Pierrick Coupé Antonio Buades D. Louis Collins Montserrat Robles |
| author_sort | José V. Manjón |
| collection | DOAJ |
| description | In Magnetic Resonance Imaging typical clinical settings, both low- and high-resolution images of different types are routinarily acquired. In some cases, the acquired low-resolution images have to be upsampled to match with other high-resolution images for posterior analysis or postprocessing such as registration or multimodal segmentation. However, classical interpolation techniques are not able to recover the high-frequency information lost during the acquisition process. In the present paper, a new superresolution method is proposed to reconstruct high-resolution images from the low-resolution ones using information from coplanar high resolution images acquired of the same subject. Furthermore, the reconstruction process is constrained to be physically plausible with the MR acquisition model that allows a meaningful interpretation of the results. Experiments on synthetic and real data are supplied to show the effectiveness of the proposed approach. A comparison with classical state-of-the-art interpolation techniques is presented to demonstrate the improved performance of the proposed methodology. |
| format | Article |
| id | doaj-art-5ed593af566943e792cd6c1c84d7d3c7 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2010-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-5ed593af566943e792cd6c1c84d7d3c72025-08-20T03:34:22ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962010-01-01201010.1155/2010/425891425891MRI Superresolution Using Self-Similarity and Image PriorsJosé V. Manjón0Pierrick Coupé1Antonio Buades2D. Louis Collins3Montserrat Robles4Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, SpainMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, CanadaMathématiques et Informatique, Université Paris Descartes, 45 Rue des Saints Pères, 75270 Paris Cedex 06, FranceMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, CanadaInstituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, SpainIn Magnetic Resonance Imaging typical clinical settings, both low- and high-resolution images of different types are routinarily acquired. In some cases, the acquired low-resolution images have to be upsampled to match with other high-resolution images for posterior analysis or postprocessing such as registration or multimodal segmentation. However, classical interpolation techniques are not able to recover the high-frequency information lost during the acquisition process. In the present paper, a new superresolution method is proposed to reconstruct high-resolution images from the low-resolution ones using information from coplanar high resolution images acquired of the same subject. Furthermore, the reconstruction process is constrained to be physically plausible with the MR acquisition model that allows a meaningful interpretation of the results. Experiments on synthetic and real data are supplied to show the effectiveness of the proposed approach. A comparison with classical state-of-the-art interpolation techniques is presented to demonstrate the improved performance of the proposed methodology.http://dx.doi.org/10.1155/2010/425891 |
| spellingShingle | José V. Manjón Pierrick Coupé Antonio Buades D. Louis Collins Montserrat Robles MRI Superresolution Using Self-Similarity and Image Priors International Journal of Biomedical Imaging |
| title | MRI Superresolution Using Self-Similarity and Image Priors |
| title_full | MRI Superresolution Using Self-Similarity and Image Priors |
| title_fullStr | MRI Superresolution Using Self-Similarity and Image Priors |
| title_full_unstemmed | MRI Superresolution Using Self-Similarity and Image Priors |
| title_short | MRI Superresolution Using Self-Similarity and Image Priors |
| title_sort | mri superresolution using self similarity and image priors |
| url | http://dx.doi.org/10.1155/2010/425891 |
| work_keys_str_mv | AT josevmanjon mrisuperresolutionusingselfsimilarityandimagepriors AT pierrickcoupe mrisuperresolutionusingselfsimilarityandimagepriors AT antoniobuades mrisuperresolutionusingselfsimilarityandimagepriors AT dlouiscollins mrisuperresolutionusingselfsimilarityandimagepriors AT montserratrobles mrisuperresolutionusingselfsimilarityandimagepriors |