Super‐resolution T1estimation: Quantitative high resolution T1mapping from a set of low resolution T1‐weighted images with different slice orientations
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Purpose QuantitativeT1mapping is a magnetic resonance imaging technique that estimates the spin‐lattice relaxation time of tissues. Even thoughT1mapping has a broad range of potential applications, it is not routinely used in clinical practice as accurate and precise high resolutionT1mapping requires infeasibly long acquisition times. Method To improve the trade‐off between the acquisition time, signal‐to‐noise ratio and spatial resolution, we acquire a set of low resolutionT1‐weighted images and directly estimate a high resolutionT1map by means of super‐resolution reconstruction. Results Simulation and in vivo experiments show an increased spatial resolution of theT1map, while preserving a high signal‐to‐noise ratio and short scan time. Moreover, the proposed method outperforms conventional estimation in terms of root‐mean‐square error. Conclusion Super resolutionT1estimation enables resolution enhancement inT1mapping with the use of standard (inversion recovery)T1acquisition sequences. Magn Reson Med 77:1818–1830, 2017. © 2016 International Society for Magnetic Resonance in Medicine
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Super-resolution for MRI can be used to improve the trade-off between resolution, SNR, and acquisition time, generate 7T-like MR images on 3T MRI scanners, or obtain super-resolution T1 maps from a set of low resolution T1 weighted images.
“Super-resolution for MRI have been around for almost 10 years [192, 193] and can be used to improve the trade-off between resolution, SNR, and acquisition time [194], generate 7T-like MR images on 3T MRI scanners [195], or obtain super-resolution T 1 maps from a set of low resolution T 1 weighted images [196].”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [196]
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An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058 · see citing works
- Authors
- Van Steenkiste, G., Poot, D. H. J., Jeurissen, B., den Dekker, A. J., Vanhevel, F., Parizel, P. M., & Sijbers, J.
- Affiliations
- iMinds‐Vision Lab Department of Physics, University of Antwerp Antwerp Belgium
- Erasmus MC (NL)
- University of Antwerp (BE)
- Venue
- Magnetic Resonance in Medicine
- Publisher
- Wiley
- Published
- 2016-07
- Volume
- 77
- Issue
- 5
- Pages
- 1818-1830
- ISSN
- 0740-3194 (ISSN-L)
- 1522-2594
- OpenAlex
- W2462044004
- Cited by
- 20
- References
- 59
- Citations by year
- 2026: 1 · 2024: 3 · 2022: 6 · 2021: 1 · 2020: 2 · 2019: 3 · 2018: 3 · 2016: 1
- FWCI
- 2.02
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Magnetic Resonance in Medicine (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Funders
- Fonds Wetenschappelijk Onderzoek
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- quantitative T1 mapping, super-resolution reconstruction, T1 relaxation time, slice orientation, spatial resolution enhancement, acquisition time reduction, signal-to-noise ratio, inversion recovery
- MeSH terms
- Adult, Algorithms, Brain, Brain Mapping, Computer Simulation, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Motion, Reproducibility of Results, Models, Statistical, Anisotropy, Phantoms, Imaging, Imaging, Three-Dimensional, Signal-To-Noise Ratio
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