In MRI, the relatively thick slices of multi-slice acquisitions often hamper visualization and analysis of the underlying anatomy. A group of post-processing techniques referred to as super-resolution reconstruction (SRR) have been developed to address this issue. In this study, we present a novel approach to SRR in MRI, which exploits the high-resolution content usually available in the 2D slices of MRI slice stacks to reconstruct isotropic high-resolution 3D images. Relying on the assumption of local self-similarity of anatomical structures, the method can be applied both to a single slice stack and to the combination of multiple slice stacks that differ in the orientation of their field of view. We evaluate the method quantitatively on synthetic brain MRI and qualitatively on MRI of the lungs. The results show that the method outperforms state-of-the-art MRI super-resolution methods.

brain, cross-scale, dictionaries, inverse problems, lung, MRI, reconstruction, regularization, self-similarity, Super-resolution
dx.doi.org/10.1007/978-3-642-40760-4_16, hdl.handle.net/1765/58391
Department of Radiology

Plenge, E, Poot, D.H.J, Niessen, W.J, & Meijering, H.W. (2013). Super-resolution reconstruction using cross-scale self-similarity in multi-slice MRI. doi:10.1007/978-3-642-40760-4_16