Deep-ERx2: Deep Learning Reconstruction for fast high-resolution non-Cartesian Compressed-Sensing MR Spectroscopic Imaging at 3T and 7T

Motivation: High-Resolution magnetic resonance spectroscopic imaging (MRSI) is a powerful, non-invasive method for detailed imaging of brain metabolism. However, traditional methods for reconstructing accelerated high-resolution whole-brain MRSI are time-consuming, posing challenges for its routine clinical application. Goal(s): Reconstruction of high-resolution whole-brain MRSI acquired at 3T and 7T in a timely fashion. Approach: Deep-learning reconstruction using recurring interlaced convolutional layers with joint dual-space feature representation for non-Cartesian Compressed-Sensing MRSI acquired by 3D ECCENTRIC sampling. Results: Deep learning ECCENTRIC reconstruction (Deep-ER) speeds up 600 times the reconstruction of high-resolution ECCENTRIC (k,t) data. Deep-learning reconstruction provides improved SNR metabolic maps across acceleration factors. Impact: Deep-ER enables high-resolution (3.4 mm isotropic) metabolic imaging with clinically feasible acquisition (4-9 min) and reconstruction times (1 min) at 3T and 7T. These times are compatible with the clinical workflow.

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/10.58530/2025/4606
Primary Topic
Advanced MRI Techniques and Applications
Type
article
Field-Weighted Citation Impact
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article

Deep-ERx2: Deep Learning Reconstruction for fast high-resolution non-Cartesian Compressed-Sensing MR Spectroscopic Imaging at 3T and 7T

Gülnur Ungan, Daniel P. Cahill, Wolfgang Bogner, Stanislav Motyka 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
Advanced MRI Techniques and Applications
article

Deep-ERx2: Deep Learning Reconstruction for fast high-resolution non-Cartesian Compressed-Sensing MR Spectroscopic Imaging at 3T and 7T

Gülnur Ungan, Daniel P. Cahill, Wolfgang Bogner, Stanislav Motyka, Paul Weiser, Jörg Dietrich, Bernhard Strasser, Ovidiu C. Andronesi, Erik J. Uhlmann, Georg Langs, Tracy T. Batchelor, Antoine Klauser, Polina Golland, Malte Hoffmann, Nalini Singh
article en

Abstract

Motivation: High-Resolution magnetic resonance spectroscopic imaging (MRSI) is a powerful, non-invasive method for detailed imaging of brain metabolism. However, traditional methods for reconstructing accelerated high-resolution whole-brain MRSI are time-consuming, posing challenges for its routine clinical application. Goal(s): Reconstruction of high-resolution whole-brain MRSI acquired at 3T and 7T in a timely fashion. Approach: Deep-learning reconstruction using recurring interlaced convolutional layers with joint dual-space feature representation for non-Cartesian Compressed-Sensing MRSI acquired by 3D ECCENTRIC sampling. Results: Deep learning ECCENTRIC reconstruction (Deep-ER) speeds up 600 times the reconstruction of high-resolution ECCENTRIC (k,t) data. Deep-learning reconstruction provides improved SNR metabolic maps across acceleration factors. Impact: Deep-ER enables high-resolution (3.4 mm isotropic) metabolic imaging with clinically feasible acquisition (4-9 min) and reconstruction times (1 min) at 3T and 7T. These times are compatible with the clinical workflow.

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 63%
Advanced MRI Techniques and Applications
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