Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
MRI Inter-slice Reconstruction Using Super-Resolution
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Fundamental Limits of Reconstruction-Based Superresolution Algorithms under Local Translation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deterministic edge-preserving regularization in computed imaging
IEEE Transactions on Image Processing
BTK: An open-source toolkit for fetal brain MR image processing
Computer Methods and Programs in Biomedicine
Comparison of super resolution reconstruction acquisition geometries for use in mouse phenotyping
Journal of Biomedical Imaging
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Super-resolution techniques provide a route to studying fine scale anatomical detail using multiple lower resolution acquisitions. In particular, techniques that do not depend on regular sampling can be used in medical imaging situations where imaging time and resolution are limited by subject motion. We investigate in this work the use of a super-resolution technique for anisotropic fetal brain MR data reconstruction without modifying the data acquisition protocol. The approach, which consists of iterative motion correction and high resolution image estimation, is compared with a previously used scattered data interpolation-based reconstruction method. To optimize acquisition time, an evaluation of the influence of the number of input images and image noise is also performed. Evaluation on simulated MR images and real data show significant improvements in performance provided by the super-resolution approach.