Super-Resolution
In 3D Magnetic Resonance Imaging (MRI), image resolution is limited by several factors such as hardware, time constraints or patient’s comfort. Therefore, the low resolution of MRI can limit accuracy of post-processing tasks such as lesion segmentation (e.g., brain tumor or multiple sclerosis). In order to efficiently reconstruct high resolution MRI from low resolution MRI a new patch-based method has been proposed to recover high frequency information by using a data-adaptive reconstruction in combination with a sub-sampling coherence constraint. The proposed method has been applied in mono and multi-modal context. Recently, we extended such method to collaborative diffusion-weighted MRI super-resolution (CLASR).
Monomodal MRI super-resolution
