arXiv AI
MRI-to-CT Translation of Spinal Images via Anatomy-Guided Pseudo-Pairing and Adversarial Diffusion
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Prob-BBDM: a Probabilistic Brownian Bridge Diffusion Model for MRI sequence image-to-image translation
arXiv:2606. 24313v1 Announce Type: new Abstract: AI-driven image-to-image synthesis is rapidly advancing, with growing applications in medical imaging.
arXiv AI
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SRUG: A Fusion-Driven Generator Network for Medical Image Translation
SRUG is a supervised, fusion‑driven generator network designed for paired MRI sequence synthesis. It eliminates the generator‑discriminator competition by directly learning a deterministic source‑to‑target mapping, using a residual encoder, full‑resolution stem, and a nested multi‑scale decoder to preserve structural fidelity. Experiments on BraTS 2023 and IXI demonstrate competitive reconstruction quality and cross‑dataset transferability, supporting direct supervised generation as a practical approach for medical image translation.
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VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution
arXiv:2610.20509v1 Announce Type: cross Abstract: Generative super-resolution (SR) can recover sharp detail in computed tomography (CT) images from severely downsampled observations, but the missing...
Hugging Face Trending Papers
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Prob-BBDM: a Probabilistic Brownian Bridge Diffusion Model for MRI sequence image-to-image translation
AI-driven image-to-image synthesis is rapidly advancing, with growing applications in medical imaging. Multi-modal image analysis plays a crucial role in optimizing examination quality, yet acquiring multiple imaging modalities in clinical settings remains resource-intensive and time-consuming, especially for 3D imaging.
arXiv AI
Jul 24
Equivariant Conditional Diffusion Model for Head and Neck CT Image Synthesis from CBCT
arXiv:2509. 21913v2 Announce Type: replace-cross Abstract: Background: Cone-beam computed tomography CBCT is a commonly used modality for image guided radiotherapy.
arXiv Computer Vision
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CAPS: Clinically-Verifiable Anatomy-Prioritized Synthesis for Missing MRI Imputation in Brain Tumor Segmentation
CAPS is a Clinically‑Verifiable Anatomy‑Prioritized Synthesis framework designed to impute missing MRI modalities for brain tumor segmentation. It uses a hybrid loss (L₁ + L_AFP + L_DualDice) to preserve deep anatomical features and topological boundaries, achieving high synthesis fidelity and robust segmentation (82.38% mean Dice) on the BraTS Adult Glioma dataset. The method also generalizes well to unseen cohorts and has been validated by blinded radiologists, who report improved image quality and clinical utility.