Cross-Site Generalization of CNN-Based B 1 + $$ {B}_1^{+} $$ Mapping in UHF MRI

Convolutional neural networks (CNNs) can rapidly predict channel-wise $B_1^{+}$ 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 $B_1^{+}$ maps for brain imaging at unseen sites supporting the feasibility of a universal network for subject-specific $B_1^{+}$ - 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/phase images, error maps, correlation plots, Pearson coefficients, and residual spread plots. Quantitative evaluation included RMSE and SSIM scores. Finally, we calculated 4 kT-points pulses with both on-site and cross-site CNNs to evaluate the effectiveness of the obtained $B_1^{+}$ maps for parallel-transmission (pTx). While on-site $B_1^{+}$ 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 $B_1^{+}$ 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.