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Abstract #2696

MRI TGV Based Super-Resolution

Adrian Martin1, 2, Antonio Marquina3, Juan Antonio Hernandez-Tamames1, 2, Pablo Garcia-Polo1, 2, Emanuele Schiavi, 24

1Electronics, Rey Juan Carlos University, Mostoles, Madrid, Spain; 2Alzheimer's Project, Queen Sofia Foundation - CIBERNED, Madrid, Spain; 3Applied Mathematics, Valencia University, Burjassot, Comunidad Valenciana, Spain; 4Applied Mathematics, Rey Juan Carlos University, Mostoles, Madrid, Spain

In some MRI applications, in particular when co-registration between modalities is needed, such as fMRI and 3DT1-IR, the acquired image needs to be upsampled to a higher resolution so common interpolation methods have been typically applied to increase this new apparent spatial resolution. Here we propose a new Super Resolution (SR) technique which outperforms these interpolation methods. It is based in a variational SR model proposed and validated in MRI by Joshi et al. in which we introduced the concept of the Total Generalized Variation. Using this operator the solutions obtained by the proposed method present a better image quality. A comparison between methods is presented with phantom and real brain MR images.