Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
360-04-010 ISMRM Abstract

Different Optimal AI Acceleration Settings on Different Scanners for Quantitative Measurements Using 3D-QALAS

Primary: Acquisition & Reconstruction - Quantitative Imaging: relaxometry, multi-parametric, MR Fingerprinting, and synthetic MRI
Secondary: Contrast Mechanisms - Relaxometry
360-04-010 · Quantitative Mapping: Acquisition and Reconstruction · Monday, 11 May, 4:10 PM–5:05 PM · Digital Posters Row A
Keywords: MR Fingerprinting/Synthetic MR Quantitative Mapping Reproducibility AI-Accelerated MRI
Accepted
Maarten Naeyaert 1,2, PETER VAN SCHUERBEEK1,2, Manon Roose1,3, Hubert Raeymaekers1,2
1Department of Radiology, Universitair Ziekenhuis Brussel, Brussels, Belgium
2Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel (VUB), Brussels, Belgium
3MFYS, Vrije Universiteit Brussel, Brussels, Belgium
Presenting Author: Maarten Naeyaert

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. Pezzotti N, de Weerdt E, Yousefi S, et al. Adaptive-CS-Net: FastMRI with Adaptive Intelligence. December 2019 https://doi.org/10.48550/arXiv.1912.12259. [doi]
2. Lebel RM. Performance characterization of a novel deep learning-based MR image reconstruction pipeline. August 2020. https://doi.org/10.48550/arXiv.2008.06559. [doi]
3. Matsuyama T, Ohno Y, Yamamoto K, et al. Comparison of utility of deep learning reconstruction on 3D MRCPs obtained with three different k-space data acquisitions in patients with IPMN. Eur Radiol. 2022;32(10):6658-6667. doi:10.1007/s00330-022-08877-2 [doi]
4. Ueda T, Ohno Y, Yamamoto K, et al. Compressed sensing and deep learning reconstruction for women’s pelvic MRI denoising: Utility for improving image quality and examination time in routine clinical practice. Eur J Radiol. 2021;134:109430. doi:10.1016/j.ejrad.2020.109430 [doi]
5. Fransen SJ, Roest C, Simonis FFJ, Yakar D, Kwee TC. The scientific evidence of commercial AI products for MRI acceleration: a systematic review. Eur Radiol. 2025;35(8):4736-4746. doi:10.1007/s00330-025-11423-5 [doi]
6. Kvernby S, Warntjes MJB, Haraldsson H, Carlhäll C johan, Engvall J, Ebbers T. Simultaneous three-dimensional myocardial T1 and T2 mapping in one breath hold with 3D-QALAS. J Cardiovasc Magn Reson. 2014;16(1):102. doi:10.1186/s12968-014-0102-0 [doi]
7. Dalmaz O, Desai AD, Heckel R, Çukur T, Chaudhari AS, Hargreaves BA. Efficient Noise Calculation in Deep Learning-based MRI Reconstructions. May 2025. https://doi.org/10.48550/arXiv.2505.02007. [doi]
8. Tamada D. Review: Noise and artifact reduction for MRI using deep learning. February 2020. https://doi.org/10.48550/arXiv.2002.12889. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/360-04-010