使用2D图像条件化3D扩散模型：通过En Face信息引导的超分辨率实现标准化OCT体数据
Conditioning 3D Diffusion Models with 2D Images: Towards Standardized OCT Volumes through En Face-Informed Super-Resolution
AI总结：
本文提出利用2D的en face SLO图像作为条件训练3D扩散模型，将高各向异性的OCT体数据标准化，在切片上采样8倍时优于传统插值和无条件扩散模型，提升了结构相干性并减少幻觉。
摘要：
体积医学图像中的高各向异性会导致解剖和病理结构的量化不一致。特别是在光学相干断层扫描（OCT）中，切片间距在数据集、研究和临床实践之间及内部可能存在显著差异。我们提出通过使用en face扫描激光眼底镜（SLO）成像数据（一种临床实践中已普遍可用的2D模态）作为条件来训练3D扩散模型，从而将OCT体数据标准化为各向异性较低的体数据。我们在多中心、多模态的MACUSTAR研究数据上进行了训练和评估。在将切片数量上采样8倍的同时，我们的方法在感知相似度指标上优于三三次插值和没有en face条件化的扩散模型。定性结果表明，相干性和结构相似性得到了改善。我们的方法允许做出更明智的生成决策，从而可能减少幻觉。我们希望这项工作能为实现标准化高质量体积成像提供下一步支持，从而实现更一致的量化。
英文摘要：
High anisotropy in volumetric medical images can lead to the inconsistent quantification of anatomical and pathological structures. Particularly in optical coherence tomography (OCT), slice spacing can substantially vary across and within datasets, studies, and clinical practices. We propose to standardize OCT volumes to less anisotropic volumes by conditioning 3D diffusion models with en face scanning laser ophthalmoscopy (SLO) imaging data, a 2D modality already commonly available in clinical practice. We trained and evaluated on data from the multicenter and multimodal MACUSTAR study. While upsampling the number of slices by a factor of 8, our method outperforms tricubic interpolation and diffusion models without en face conditioning in terms of perceptual similarity metrics. Qualitative results demonstrate improved coherence and structural similarity. Our approach allows for better informed generative decisions, potentially reducing hallucinations. We hope this work will provide the next step towards standardized high-quality volumetric imaging, enabling more consistent quantifications.
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