Cross-Site Generalization of CNN-Based $$ {B}_1^{+} $$ Mapping in UHF MRI.
The 2 matches
- [1] § Methods › MRI Data Acquisition and Data Processing ↔ Train_Complex_Model_Save_Model.ipynb, lines 40–122 · score 0.63 · imaginary parts, localizer images, Tx channels, split, magnitude, training
- [2] § Methods › MRI Data Acquisition and Data Processing ↔ Train_Complex_Model_Save_Model.ipynb, lines 603–744 · score 0.54 · trainable parameters, ADAM, epoch, batch, optimizer, validation
Paper
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Jupyter notebook · 762 lines · 34 KB · no license · 2 matches
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Overview
- Physikalisch‐Technische Bundesanstalt Berlin Germany
- Medical Physics in Radiology German Cancer Research Center (DKFZ) Heidelberg Germany
- Faculty of Physics and Astronomy Heidelberg University Heidelberg Germany
- Center for Magnetic Resonance Research University of Minnesota Minneapolis Minnesota USA
- Institute for Applied Medical Informatics University Medical Center Hamburg‐Eppendorf (UKE) Hamburg Germany
- Max Planck Research Group MR Physics, Max Planck Institute for Human Development Berlin Germany
Abstract
Convolutional neural networks (CNNs) can rapidly predict channel‐wise B1+ maps from 7T localizer images, reducing acquisition time to seconds. This paper investigates if a CNN trained on one site's data can generalize to predict B1+ maps for brain imaging at unseen sites supporting the feasibility of a universal network for subject‐specific B1+‐ mapping. We evaluated a U‐Net CNN cross‐site generalization by training on datasets from two different 7T sites and testing its performance across three 7T sites (1 additional testing site) to assess robustness, adaptability, and generalization. The study design included both commercially same systems and the identical physical hardware unit transported between two sites, enabling a more insightful attribution of performance differences to either hardware issues or dataset‐specific variations.
To assess prediction quality, we examined magnitude/
While on‐site B1+ transversal magnitude RMSE scores were as low as 3.0% and 3.1% for the two CNNs, their respective transfer yielded 3.6% and 4.1%. The dynamic pTx‐application showed a CV of 6.5% when using B1+ maps predicted by a network trained on its own site. The transfer case, using a map predicted from a network trained on a different site, yielded an increased CV of 13.7%.
Although cross‐site applications introduced larger deviations, the predicted maps remained qualitatively plausible and enabled practical use cases, such as calculating dynamic‐pTx. These findings support the potential of cross‐site training, suggesting CNNs trained at one site may generalize sufficiently to unseen sites without additional adjustments. This strengthens the feasibility of a transferable training approach where a single network could be deployed across different institutions without extensive retraining.
Reproduced under the paper's license (CC BY), from the paper cited above.
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hkimon/B1P_Mapping_CCN
7d80d9675182f215bbe3e8c16080c05cbc002a27, 23 January 2026Availability: 1 check, the latest on 28 September 2026: the link answers
- 28 September 2026: the link answers
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Data
Datasets cited
- zenodo:18338273 — at Zenodo; found in “Data Availability Statement”
Data Availability Statement
The data supporting the findings of this study are openly available in Zenodo at https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 2, 28 September 2026
- Publisher: — → Wiley
Version 1, 28 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 7 keywords, 5 MeSH terms, 1 funder, 43 references.
Cite
This paper
Hadjikiriakos, K., Krüger, F., Zimmermann, F. F., Grimm, J. A., Schorling, C., Lutz, M., Schmidt, S., Riemann, L. T., Degenhardt, K., Schäffter, T., Ladd, M. E., Metzger, G. J., Aigner, C. S., & Schmitter, S. (2026). Cross-Site Generalization of CNN-Based $$ {B}_1^{+} $$ Mapping in UHF MRI. NMR in biomedicine, 39(5), e70263. https://
BibTeX
@article{hadjikiriakos20
author = {Hadjikiriakos, Kimon and Krüger, Felix and Zimmermann, Felix Frederik and Grimm, Johannes A. and Schorling, Constantin and Lutz, Max and Schmidt, Simon and Riemann, Layla Tabea and Degenhardt, Katja and Schäffter, Tobias and Ladd, Mark E. and Metzger, Gregory J. and Aigner, Christoph Stefan and Schmitter, Sebastian},
title = {{Cross-Site Generalization of CNN-Based \$\$ \{B\}\_1\textasciicircum
journal = {NMR in biomedicine},
year = {2026},
month = may,
volume = {39},
number = {5},
pages = {e70263},
publisher = {Wiley},
issn = {0952-3480},
doi = {10.1002/
url = {https://
pmid = {41937594},
pmcid = {PMC13051333}
}
RIS
TY - JOUR
AU - Hadjikiriakos, Kimon
AU - Krüger, Felix
AU - Zimmermann, Felix Frederik
AU - Grimm, Johannes A.
AU - Schorling, Constantin
AU - Lutz, Max
AU - Schmidt, Simon
AU - Riemann, Layla Tabea
AU - Degenhardt, Katja
AU - Schäffter, Tobias
AU - Ladd, Mark E.
AU - Metzger, Gregory J.
AU - Aigner, Christoph Stefan
AU - Schmitter, Sebastian
TI - Cross-Site Generalization of CNN-Based $$ {B}_1^{+} $$ Mapping in UHF MRI
T2 - NMR in biomedicine
J2 - NMR Biomed
PY - 2026
DA - 2026/
VL - 39
IS - 5
SP - e70263
SN - 0952-3480
PB - Wiley
DO - 10.1002/
UR - https://
LA - en
ER -
CSL-JSON
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