VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution

Hugging Face Daily Papers Papers
medical-imaging diffusion-models super-resolution ct-reconstruction vascular-structure 3d-representation

Summary

VascuSR 是一个迭代式解剖感知扩散框架，将显式3D血管引导与CT超分辨率结合，以提升重建的解剖准确性与血管连通性，在MSD和HVM数据集上显著优于现有基线。

Generative super-resolution (SR) can recover sharp detail in computed tomography (CT) images from severely downsampled observations, but the missing high-frequency content is not uniquely determined by the low-resolution input. Consequently, visually plausible reconstructions may contain anatomically unsupported vascular detail, such as missing, spurious, or incorrectly connected vessels. To overcome this challenge, we introduce VascuSR, an iterative anatomy-aware diffusion framework that combines image reconstruction with explicit 3D vascular guidance. First, to provide richer anatomical guidance, we use an explicit 3D vascular representation that captures structural information beyond individual slices. This representation also simplifies vascular refinement by allowing a lightweight 3D generative refiner to focus on vascular structure rather than complex CT appearance. Second, to guide slice-wise SR, a lightweight anatomy controller injects the refined structure into the diffusion decoder, while iterative feedback updates this guidance from each new SR volume. On re-annotated MSD Task08 and HVM datasets, VascuSR outperforms interpolation and learning-based SR baselines in segmentation accuracy (Dice and clDice) and 3D vascular structure (connectivity and mean centerline distance), while maintaining image quality comparable to its SR backbone. Compared with learning-based baselines, VascuSR reduces class-averaged connected-component counts by over 20% on both datasets and mean centerline distance by over 10% on MSD.
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Paper page - VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution

Source: https://huggingface.co/papers/2610.20509

Abstract

VascuSR is an iterative diffusion framework that integrates 3D vascular guidance with super-resolution to improve anatomical accuracy and vascular connectivity in CT reconstructions.

Generative super-resolution(SR) can recover sharp detail in computed tomography (CT) images from severely downsampled observations, but the missing high-frequency content is not uniquely determined by the low-resolution input. Consequently, visually plausible reconstructions may contain anatomically unsupported vascular detail, such as missing, spurious, or incorrectly connected vessels. To overcome this challenge, we introduce VascuSR, an iterative anatomy-awarediffusion frameworkthat combines image reconstruction with explicit 3D vascular guidance. First, to provide richer anatomical guidance, we use an explicit3D vascular representationthat captures structural information beyond individual slices. This representation also simplifies vascular refinement by allowing a lightweight 3D generative refiner to focus on vascular structure rather than complex CT appearance. Second, to guide slice-wise SR, a lightweightanatomy controllerinjects the refined structure into thediffusion decoder, whileiterative feedbackupdates this guidance from each new SR volume. On re-annotated MSD Task08 and HVM datasets, VascuSR outperforms interpolation and learning-based SR baselines insegmentation accuracy(Dice andclDice) and 3D vascular structure (connectivity and mean centerline distance), while maintaining image quality comparable to its SR backbone. Compared with learning-based baselines, VascuSR reduces class-averaged connected-component counts by over 20% on both datasets and mean centerline distance by over 10% on MSD.

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