用少量2D CT片生成3D体积，提升体成分分析精度
Robust Body Composition Analysis by Generating 3D CT Volumes from Limited 2D Slices
- 通过变分自编码器与潜空间扩散模型生成3D CT
- 体成分分析误差从23.3%降至15.2%
- 适合低辐射剂量下需精准体成分评估的场景
体成分分析对了解衰老、疾病进展及整体健康状况具有重要意义。由于辐射暴露担忧，临床常采用二维（2D）单切片计算机断层扫描（CT）重复进行体成分分析，但该方法引入显著的空间变异性，影响分析的准确性和鲁棒性。为缓解此问题并促进体成分分析，本文提出一种新方法：利用潜空间扩散模型（LDM）从有限数量的2D切片生成3D CT体积。首先通过变分自编码器将2D切片映射至潜空间，再训练一个LDM以捕捉这些潜表示序列的3D上下文信息。为精确插值中间切片并构建完整3D体积，我们使用器官部位回归确定已获取切片的空间位置与间距。在自建及公开的腹部3D CT数据集上的实验表明，相比传统2D分析，所提方法显著提升了体成分分析性能，误差率由23.3%降低至15.2%。
原文摘要 · Abstract (English)
Body composition analysis provides valuable insights into aging, disease progression, and overall health conditions. Due to concerns of radiation exposure, two-dimensional (2D) single-slice computed tomography (CT) imaging has been used repeatedly for body composition analysis. However, this approach introduces significant spatial variability that can impact the accuracy and robustness of the analysis. To mitigate this issue and facilitate body composition analysis, this paper presents a novel method to generate 3D CT volumes from limited number of 2D slices using a latent diffusion model (LDM). Our approach first maps 2D slices into a latent representation space using a variational autoencoder. An LDM is then trained to capture the 3D context of a stack of these latent representations. To accurately interpolate intermediateslices and construct a full 3D volume, we utilize body part regression to determine the spatial location and distance between the acquired slices. Experiments on both in-house and public 3D abdominal CT datasets demonstrate that the proposed method significantly enhances body composition analysis compared to traditional 2D-based analysis, with a reduced error rate from 23.3% to 15.2%.
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