Abstract
MR image acquisition of moving organs remains challenging despite the advances in ultra-fast 2D MRI sequences. Post-acquisition techniques have been proposed to increase spatial resolution a posteriori by combining acquired orthogonal stacks into a single, high-resolution (HR) volume. Current super-resolution techniques classically rely on a two-step procedure. The volumetric reconstruction step leverages a physical slice acquisition model. However, the motion correction step typically neglects the point spread function (PSF) information. In this paper, we propose a PSF-aware slice-to-volume registration approach and, for the first time, demonstrate the potential benefit of Super-Resolution for upper abdominal imaging. Our novel reconstruction pipeline takes advantage of different MR acquisitions clinically used in routine MR cholangio-pancreatography studies to guide the registration. On evaluation of clinically relevant image information, our approach outperforms state-of-the-art reconstruction toolkits in terms of visual clarity and preservation of raw data information. Overall, we achieve promising results towards replacing currently required CT scans.
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Notes
- 1.
The BTK-results used in here do not include the SRR step. Using the standard parametrization of BTK, the SRR outcome was less satisfying and of poorer quality than the reconstruction obtained via local neighbourhood oriented Gaussian interpolation [10].
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Acknowledgements
This work is supported by the EPSRC-funded UCL Centre for Doctoral Training in Medical Imaging (EP/L016478/1), the Department of Healths NIHR-funded Biomedical Research Centre at University College London Hospitals and Innovative Engineering for Health award by the Wellcome Trust [WT101957] and Engineering and Physical Sciences Research Council (EPSRC) [NS/A000027/1]. Furthermore, this work was funded by NIHR Clinical Lectureship and NIHR Senior Investigator grant.
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Ebner, M. et al. (2017). Point-Spread-Function-Aware Slice-to-Volume Registration: Application to Upper Abdominal MRI Super-Resolution. In: Zuluaga, M., Bhatia, K., Kainz, B., Moghari, M., Pace, D. (eds) Reconstruction, Segmentation, and Analysis of Medical Images. RAMBO HVSMR 2016 2016. Lecture Notes in Computer Science(), vol 10129. Springer, Cham. https://doi.org/10.1007/978-3-319-52280-7_1
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