For the last 15 years, super‐resolution (SR) algorithms have successfully been applied to magnetic resonance imaging (MRI) data to increase the spatial resolution of scans after acquisition has been performed, thus facilitating the doctors' diagnosis. The variety of application and techniques has grown ever since, especially in the MRI modality, showing the interest of the community to such postacquisition processing. This article presents a review of the general principle of SR as well as how this principle has been adapted to MRI data. The main algorithms and the principal acquisition protocols are detailed for both static and moving subjects. The presented strategies are discussed and compared according to the data specificities. Later, different ways of measuring the resolution enhancement and quantify the benefit of SR are detailed. Finally, unexplored perspectives on the application of SR to MRI data are discussed. © 2012 Wiley Periodicals, Inc. Concepts Magn Reson Part A 40A: 306–325, 2012.

The results presented here are encouraging and suggest that super-resolution can be regarded as an efficient method to improve the resolution of 4D MRI sequences. It produces an isotropic 4D sequence that would be impossible to acquire in practice. Further investigations will be required to evaluate its reproducibility in various clinical applications.

Dynamic three-dimensional (3-D) (four-dimensional, 4-D) magnetic resonance (MR) imaging is gaining importance in the study of pulmonary motion for respiratory diseases and pulmonary tumor motion for radiotherapy. To perform quantitative analysis using 4-D MR images, segmentation of anatomical structures such as the lung and pulmonary tumor is required. Manual segmentation of entire thoracic 4-D MRI data that typically contains many 3-D volumes acquired over several breathing cycles is extremely tedious, time consuming, and suffers high user variability. This requires the development of new automated segmentation schemes for 4-D MRI data segmentation. Registration-based segmentation technique that uses automatic registration methods for segmentation has been shown to be an accurate method to segment structures for 4-D data series. However, directly applying registration-based segmentation to segment 4-D MRI series lacks efficiency. Here we propose an automated 4-D registration-based segmentation scheme that is based on spatiotemporal information for the segmentation of thoracic 4-D MR lung images. The proposed scheme saved up to 95% of computation amount while achieving comparable accurate segmentations compared to directly applying registration-based segmentation to 4-D dataset. The scheme facilitates rapid 3-D/4-D visualization of the lung and tumor motion and potentially the tracking of tumor during radiation delivery.

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