Medical Foundation Model Features as Perceptual Loss for Brain MRI Contrast Dose Simulation
Perceptual losses are widely used in medical image synthesis because they encourage agreement in high-level structure beyond voxel-wise intensity similarity. In practice, most perceptual losses are still computed with natural-image backbones such as VGG16 or ResNet50, even when the target domain is magnetic resonance imaging (MRI). This mismatch may weaken supervision for anatomy, contrast enhancement, and acquisition variability. We test whether medical foundation model features provide a more suitable perceptual loss for brain MRI contrast dose simulation. The study has two stages. First, we compare RadImageNet, SegVol, and BrainIAC with ImageNet-pretrained VGG16 and ResNet50 as frozen ...
| Authors | Changsheng Fang, Dayang Wang, T. Campbell Arnold et al. |
|---|---|
| Primary category | eess.IV |
| Published | 28 August 2026 |
| Last updated at source | 4 September 2026 |
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