Instead of just undersampling, sample more and faster: 0.6mm isotropic MPRAGE at 7T in under 2 minutes

Motivation: Deep learning-based reconstructions could be used to further accelerate MR acquisitions by optimizing sequence parameters that reduce scan time, at the cost of reduced SNR, as an alternative to simply increasing undersampling factors. Goal(s): To compare a clinical MPRAGE protocol (CS x5, TA: 4min) at 7T with a x12 undersampled acquisition (Fast-Standard) and a time-matched protocol with x5 undersampling but higher turbo factor and optimized parameters to preserve contrast (Fast-Optimized). Approach: Reference and fast MPRAGE protocols were acquired in three subjects. Reconstructed images were compared qualitatively and quantitatively. Results: Fast-Optimized scans were more comparable to the reference than the Fast-Standard scans. Impact: This study demonstrates the effectiveness of using deep learning image reconstruction in combination with optimized sequence parameters for scan speed to further accelerate a clinical MPRAGE acquisition, in contrast to simply increasing undersampling factors.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/1372
Primary Topic
Cardiac Imaging and Diagnostics
Type
article
Field-Weighted Citation Impact
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article

Instead of just undersampling, sample more and faster: 0.6mm isotropic MPRAGE at 7T in under 2 minutes

Tom Hilbert, Patrick Alexander Liebig, Dominik M. Nickel, Bénédicte Maréchal et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Cardiac Imaging and Diagnostics
article

Instead of just undersampling, sample more and faster: 0.6mm isotropic MPRAGE at 7T in under 2 minutes

Tom Hilbert, Patrick Alexander Liebig, Dominik M. Nickel, Bénédicte Maréchal, Gian Franco Piredda, Jocelyn Philippe, Tommaso Di Noto, Thomas Yu, Antoine Klauser, Tobias Kober, Emilie Sleight, Lina Bacha, Natalia Pato Montemayor
article en

Abstract

Motivation: Deep learning-based reconstructions could be used to further accelerate MR acquisitions by optimizing sequence parameters that reduce scan time, at the cost of reduced SNR, as an alternative to simply increasing undersampling factors. Goal(s): To compare a clinical MPRAGE protocol (CS x5, TA: 4min) at 7T with a x12 undersampled acquisition (Fast-Standard) and a time-matched protocol with x5 undersampling but higher turbo factor and optimized parameters to preserve contrast (Fast-Optimized). Approach: Reference and fast MPRAGE protocols were acquired in three subjects. Reconstructed images were compared qualitatively and quantitatively. Results: Fast-Optimized scans were more comparable to the reference than the Fast-Standard scans. Impact: This study demonstrates the effectiveness of using deep learning image reconstruction in combination with optimized sequence parameters for scan speed to further accelerate a clinical MPRAGE acquisition, in contrast to simply increasing undersampling factors.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 65%
Cardiac Imaging and Diagnostics
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