MRI Superresolution Using Self Similarity and Image Priors

Jose Vicente Manjon (1) , Pierrick Coupé (2) , Antoni Buades (3) , D. Louis Collins (2) , Montserrat Robles (1)

1 ITACA
2 Montreal Neurological Institute and Hospital
3 MAP5 - UMR 8145 - Mathématiques Appliquées Paris 5

In Magnetic Resonance Imaging low-resolution images are routinely interpolated to decrease voxel size and improve apparent resolution. 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 recover such information using coplanar high resolution images. The proposed methodology takes benefit from the fact that in typical clinical settings both high and low-resolution images of different types are taken from the same subject. These available high resolution images can be used to improve effectively the resolution of other coplanar lower resolution images. Experiments on synthetic and real data are supplied to show the effectiveness of the proposed approach. A comparison with classical interpolation techniques is presented to demonstrate the improved performance of the proposed methodology over previous State-of-the-art methods.

Domaines: Traitement des images [eess.IV]

Jose Vicente Manjon, Pierrick Coupé, Antoni Buades, D. Louis Collins, Montserrat Robles. MRI Superresolution Using Self Similarity and Image Priors. 2010. ⟨hal-00453259⟩