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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

Subpages (3): CLASR Monomodal Multimodal