用能量模型提升腰椎厚层MRI重建质量，更快更准。
Energy-Based Prior Latent Space Diffusion model for Reconstruction of Lumbar Vertebrae from Thick Slice MRI
- 在潜在空间构建能量基扩散先验，优化图像生成
- Dice和VS评分优于现有方法，3D结构还原更真实
- 适合医学影像重建、低样本高精度场景使用
腰椎疾病普遍，治疗规划与介入引导需精准成像。尽管高分辨率高对比度的CT是首选，但MRI可无辐射同时成像骨与软组织，仅需更长采集时间。为平衡对比质量与扫描时长，'厚层MRI'优先保证平面内高分辨率，但存在对比度不均和纵切面分辨率低的问题。现有后处理流程通过分割厚层图像并使用变分自编码器增强重建质量。本文提出一种基于能量的潜在空间扩散先验方法，利用扩散模型的高质量生成能力，通过学习能量基潜在表示降低计算开销与样本需求。实验表明，该方法在Dice和VS评分上优于现有技术，更忠实还原三维结构特征。
原文摘要 · Abstract (English)
Lumbar spine problems are ubiquitous, motivating research into targeted imaging for treatment planning and guided interventions. While high resolution and high contrast CT has been the modality of choice, MRI can capture both bone and soft tissue without the ionizing radiation of CT albeit longer acquisition time. The critical trade-off between contrast quality and acquisition time has motivated 'thick slice MRI', which prioritises faster imaging with high in-plane resolution but variable contrast and low through-plane resolution. We investigate a recently developed post-acquisition pipeline which segments vertebrae from thick-slice acquisitions and uses a variational autoencoder to enhance quality after an initial 3D reconstruction. We instead propose a latent space diffusion energy-based prior to leverage diffusion models, which exhibit high-quality image generation. Crucially, we mitigate their high computational cost and low sample efficiency by learning an energy-based latent representation to perform the diffusion processes. Our resulting method outperforms existing approaches across metrics including Dice and VS scores, and more faithfully captures 3D features.
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