标题： 基于有限二维切片生成三维CT体积的鲁棒人体成分分析 显示英文标题

标题： Robust Body Composition Analysis by Generating 3D CT Volumes from Limited 2D Slices

摘要： 身体成分分析为衰老、疾病进展和整体健康状况提供了有价值的见解。由于辐射暴露的担忧，二维（2D）单切片计算机断层扫描（CT）成像被反复用于身体成分分析。然而，这种方法引入了显著的空间变异性，可能会影响分析的准确性和稳健性。为了缓解这一问题并促进身体成分分析，本文提出了一种使用潜在扩散模型（LDM）从有限数量的2D切片生成3D CT体积的新方法。我们的方法首先使用变分自编码器将2D切片映射到潜在表示空间。然后训练LDM捕捉这些潜在表示堆栈的3D上下文。为了准确插补中间切片并构建完整的3D体积，我们利用身体部位回归来确定获得切片之间的空间位置和距离。在内部和公共的3D腹部CT数据集上的实验表明，与传统的基于2D的分析相比，所提出的方法显著提升了身体成分分析的效果，误差率从23.3%降低到了15.2%。 显示英文摘要

摘要： 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%.