  Ultrahigh-field (UHF) magnetic resonance imaging (MRI), i.e., 7T MRI,
provides superior anatomical details of internal brain structures owing to its
enhanced signal-to-noise ratio and susceptibility-induced contrast. However,
the widespread use of 7T MRI is limited by its high cost and lower
accessibility compared to low-field (LF) MRI. This study proposes a
deep-learning framework that systematically fuses the input LF magnetic
resonance feature representations with the inferred 7T-like feature
representations for brain image segmentation tasks in a 7T-absent environment.
Specifically, our adaptive fusion module aggregates 7T-like features derived
from the LF image by a pre-trained network and then refines them to be
effectively assimilable UHF guidance into LF image features. Using
intensity-guided features obtained from such aggregation and assimilation,
segmentation models can recognize subtle structural representations that are
usually difficult to recognize when relying only on LF features. Beyond such
advantages, this strategy can seamlessly be utilized by modulating the contrast
of LF features in alignment with UHF guidance, even when employing arbitrary
segmentation models. Exhaustive experiments demonstrated that the proposed
method significantly outperformed all baseline models on both brain tissue and
whole-brain segmentation tasks; further, it exhibited remarkable adaptability
and scalability by successfully integrating diverse segmentation models and
tasks. These improvements were not only quantifiable but also visible in the
superlative visual quality of segmentation masks.
