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SciFusion is a multimodal framework that uses a latent diffusion backbone to generate unified 3D scientific images. It introduces a compositional conditioning scheme that separates each modality into domain, object, and contrast representations, allowing knowledge transfer and zero‑shot synthesis of unseen compositions. The model also incorporates physics‑informed objectives specific to MRI, CT, and electron microscopy to ensure generated images respect modality‑dependent image formation processes, and demonstrates improved fidelity and cross‑domain generalization across 15 imaging modalities.
Reconstructing 3D Computed Tomography (CT) images from a few X-ray projections is a highly ill-posed inverse problem due to the loss of volumetric information. We propose PhyDiCT, a training-free fram...
arXiv:2610.09253v1 Announce Type: new Abstract: Reconstructing 3D Computed Tomography (CT) images from a few X-ray projections is a highly ill-posed inverse problem due to the loss of volumetric info...
arXiv:2608. 08135v1 Announce Type: cross Abstract: Cross-modality medical image translation can reduce the burden of multi-modal acquisitions, yet the field remains constrained by two coupled limitations: methods operate on 2D slices or 3D patches rather than whole volumes, and train a separate model for each translation task.
Generating CT volumes from MRI and CBCT can improve treatment planning in adaptive radiotherapy while avoiding additional radiation exposure. However, direct regression of CT intensities is challenged by the inherently high dynamic range and long-tailed distributions, thereby averaging out sparse yet clinically important structures.
arXiv:2606. 17989v1 Announce Type: cross Abstract: Multi-contrast magnetic resonance imaging (MRI) provides complementary information for clinical diagnosis.