Medical Foundation Model Features as Perceptual Loss for Brain MRI Contrast Dose Simulation

This work introduces MedForj, a suite of 3D foundational generative priors based on diffusion models. The MedForj models were trained on $72{,}659$ 1~mm isotropic 3D $T_1$-weighted MRI human brain image volumes from $38{,}174$ subjects, drawn from a curated corpus of $80{,}675$ volumes from $42{,}506$ subjects spanning $38$ publicly available datasets. These training images were manually inspected to exclude those with poor quality and excessive pathology, and otherwise were minimally processed. The models include six different diffusion training strategies: rectified flow, latent diffusion rectified flow, flow matching, velocity prediction, clean prediction, and noise prediction. Image samples produced by each of these models were compared to each other and against real, ground truth data under downstream segmentation distributions, FID, five inverse problems, and blind human inspection in an observer study. Flow matching was the strongest strategy overall, achieving the best inverse problem solving results at $28.80$~dB PSNR and $0.874$ SSIM averaged over the five forward problems, the highest rate of reconstructions judged real by blind human raters at $72.6\%$, and the closest per-structure match to real segmented anatomy in a permutation test. It was not best everywhere: rectified flow produced the most convincing unconditional samples in the observer study and the best FID, and the latent rectified-flow model achieved the smallest joint distributional distance to real anatomy. No other strategy, however, performed consistently well across all four evaluations. We therefore recommend flow matching as the default MedForj prior, while releasing every strategy so that the choice can be revisited per application. All model weights and corresponding code are publicly available at https://github.com/piksl-research/medforj.

MRI sequence synthesis aims to recover missing image contrast while preserving patient-specific anatomy. The choice of generation mechanism affects both optimization and the way source information reaches the synthesized image. In this study, we propose SRUG, a supervised standalone fusion-driven generator that learns a deterministic source-to-target mapping for paired MRI synthesis. Its direct reconstruction formulation removes generator-discriminator competition and requires neither variational latent sampling nor iterative diffusion denoising. To support structural fidelity within this formulation, SRUG uses a residual encoder adapted for image reconstruction, with a full-resolution convolutional stem, pooling-separated feature stages, and channel recalibration embedded in the residual branches. A nested multi-scale decoder (NMD) repeatedly fuses the resulting spatial features and predicts the target sequence through a single output head. L1 loss and Multi-Scale Structural Loss (MSS loss) jointly supervise the prediction, connecting the direct generation objective to anatomical detail recovery. Experiments on BraTS 2023 show competitive reconstruction fidelity and structural consistency across three MRI translation tasks. Architecture ablations assess the effects of channel recalibration, NMD, and structural supervision within the same generator framework. Additional evaluation on IXI supports applicability to another dataset and modality pair, while zero-shot testing on BraTS 2019 provides preliminary evidence of cross-dataset transferability. These results support direct supervised generation as a practical approach to paired MRI sequence synthesis, with the adapted encoding and reconstruction pathway providing the basis for structural preservation. The SRUG implementation is publicly available at https://github.com/RisingRich/SRUG.

Dynamic contrast-enhanced breast MRI (DCE-MRI) is rich in anatomical and perfusion information, but its reliance on gadolinium-based contrast agents raises safety concerns and adds cost. Virtual contrast enhancement, synthesizing post-contrast from pre-contrast images, is a promising alternative. We address the MAMA-SYNTH challenge task of predicting peak-enhancement breast MRI. Rather than adopting the full machinery of diffusion or flow matching, we observe that under a rectified, straight-line path the generative process collapses to a single difference prediction: the synthetic peak image is the pre-contrast image plus a predicted enhancement map, recovered in one forward pass. Around this we build Anguinus Sculpturae, a compositional pipeline in which nnU-Net segmentations of lesion, foreground and breast region guide two generators - one optimized for global fidelity, one for lesion structure through an asymmetric Tversky term routed via a frozen segmenter - composited region-wise with Gaussian-weighted blending. On the held-out Duke subset of MAMA-MIA our model achieves the best FRD and Dice among all evaluated variants, showing that single-step difference prediction with segmentation guidance suffices to recover both global fidelity and lesion structure. Code is available at https://github.com/MIC-DKFZ/AnguinusSculpturae.