Investigating the robustness of estimating B1+maps by a neural network at different UHF sites.

Motivation: B1+ -maps needed for subject-specific pTx can be derived from localizers using a neural network (NN), omitting separate B1+-mapping. Ideally, a single, general NN applicable to all UHF sites is highly desired. Goal(s): Investigate the robustness of a neural network predicted B1+-field maps. Utilizing receive profiles from an 8Tx/32Rx head coil as neural network input. Approach: Comparing the performance on data from identical and different commercial head coils across multiple MRI sites. Results: Achieving SSIMs as high as 96% and RMSEs as low as 2.7%, with error mapping predominantly localizing discrepancies at the cranial margins, suggesting that larger datasets could enhance Gaussian convergence. Impact: The study suggests that a single NN trained by a large B1+ library for one type of pTx head coil may be disseminated to other UHF sites that use the same coil. This will enable a fast, streamlined pTx calibration.

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
2024-11-26
DOI
https://doi.org/10.58530/2024/4942
Primary Topic
Advanced Adaptive Filtering Techniques
Type
article
Field-Weighted Citation Impact
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article

Investigating the robustness of estimating B1+maps by a neural network at different UHF sites.

Johannes Grimm, Sebastian Schmitter, Layla Tabea Riemann, Simon Schmidt 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 Adaptive Filtering Techniques
article

Investigating the robustness of estimating B1+maps by a neural network at different UHF sites.

Johannes Grimm, Sebastian Schmitter, Layla Tabea Riemann, Simon Schmidt, Mark E. Ladd, Felix Krüger, Kerstin Hammernik, Katja Degenhardt, Gregory J. Metzger, Tobias Schaeffter, Kimon Hadjikiriakos, Felix Zimmermann
article en

Abstract

Motivation: B1+ -maps needed for subject-specific pTx can be derived from localizers using a neural network (NN), omitting separate B1+-mapping. Ideally, a single, general NN applicable to all UHF sites is highly desired. Goal(s): Investigate the robustness of a neural network predicted B1+-field maps. Utilizing receive profiles from an 8Tx/32Rx head coil as neural network input. Approach: Comparing the performance on data from identical and different commercial head coils across multiple MRI sites. Results: Achieving SSIMs as high as 96% and RMSEs as low as 2.7%, with error mapping predominantly localizing discrepancies at the cranial margins, suggesting that larger datasets could enhance Gaussian convergence. Impact: The study suggests that a single NN trained by a large B1+ library for one type of pTx head coil may be disseminated to other UHF sites that use the same coil. This will enable a fast, streamlined pTx calibration.

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 77%
Advanced Adaptive Filtering Techniques
DOISourceOpenAlex
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Investigating the robustness of estimating B1+maps by a neural network at different UHF sites. — Johannes Grimm, Sebastian Schmitter, 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 (2024) | TGRS Research Map | TGRS