Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
567-05-006 ISMRM Abstract

Equation-based k-space boosting increases arterial-phase CNR in gadoxetate-enhanced liver MRI: a preclinical evaluation.

Accepted
Felix Kreis 1, Gregor Jost1, Gunnar Schuetz1, Sebastian Gerz1, Andreas Bolz1, Hubertus Pietsch1
1Radiology, Bayer AG, Berlin, Germany
Presenting Author: Felix Kreis

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. J. Endrikat, B. Bogosavljev, A. Bhatti, S. Forgia, M. S. Fuksbrumer, und S. Kim, „Clinical Safety of Gadoxetate Disodium: Insights From 20 Years of Use and More Than 12 Million Administrations“, Invest. Radiol., S. 10.1097/RLI.0000000000001224, Dez. 2024, doi: 10.1097/RLI.0000000000001224. [doi]
2. R. Golfieri, M. Renzulli, V. Lucidi, B. Corcioni, F. Trevisani, und L. Bolondi, „Contribution of the hepatobiliary phase of Gd-EOB-DTPA-enhanced MRI to Dynamic MRI in the detection of hypovascular small (≤2 cm) HCC in cirrhosis“, Eur. Radiol., Bd. 21, Nr. 6, S. 1233–1242, Juni 2011, doi: 10.1007/s00330-010-2030-1. [doi]
3. J. Huh u. a., „Troubleshooting Arterial-Phase MR Images of Gadoxetate Disodium-Enhanced Liver“, Korean J. Radiol., Bd. 16, Nr. 6, S. 1207–1215, 2015, doi: 10.3348/kjr.2015.16.6.1207. [doi]
4. B. Avants, N. Tustison, und G. Song, „Advanced normalization tools (ANTS)“, Insight J, Bd. 1–35, Nov. 2008, doi: 10.54294/uvnhin. [doi]
5. C. R. Harris u. a., „Array programming with NumPy“, Nature, Bd. 585, Nr. 7825, S. 357–362, Sep. 2020, doi: 10.1038/s41586-020-2649-2. [doi]
6. S. van der Walt u. a., „scikit-image: Image processing in Python“, PeerJ, Bd. 2, S. e453, Juni 2014, doi: 10.7717/peerj.453. [doi]
7. A. Fringuello Mingo u. a., „Amplifying the Effects of Contrast Agents on Magnetic Resonance Images Using a Deep Learning Method Trained on Synthetic Data“, Invest. Radiol., S. 10.1097/RLI.0000000000000998, doi: 10.1097/RLI.0000000000000998. [doi]
8. S. P. Venkata u. a., „Deep-Learning Based Contrast Boosting Improves Lesion Visualization and Image Quality: A Multi-Center Multi-Reader Study on Clinical Performance with Standard Contrast Enhanced MRI of Brain Tumors“, 13. Juni 2025, medRxiv. doi: 10.1101/2025.06.12.253293 [doi]

Cite this abstract