Two-dimensional single-slice abdominal computed tomography (CT) provides a
detailed tissue map with high resolution allowing quantitative characterization
of relationships between health conditions and aging. However, longitudinal
analysis of body composition changes using these scans is difficult due to
positional variation between slices acquired in different years, which leading
to different organs/tissues captured. To address this issue, we propose
C-SliceGen, which takes an arbitrary axial slice in the abdominal region as a
condition and generates a pre-defined vertebral level slice by estimating
structural changes in the latent space. Our experiments on 2608 volumetric CT
data from two in-house datasets and 50 subjects from the 2015 Multi-Atlas
Abdomen Labeling Challenge dataset (BTCV) Challenge demonstrate that our model
can generate high-quality images that are realistic and similar. We further
evaluate our method's capability to harmonize longitudinal positional variation
on 1033 subjects from the Baltimore Longitudinal Study of Aging (BLSA) dataset,
which contains longitudinal single abdominal slices, and confirmed that our
method can harmonize the slice positional variance in terms of visceral fat
area. This approach provides a promising direction for mapping slices from
different vertebral levels to a target slice and reducing positional variance
for single-slice longitudinal analysis. The source code is available at:
this https URL

通过使用 C-SliceGen 生成预定义的椎体水平切片，从而解决了不同年份获取的切片之间的位置变化导致不同器官 / 组织被捕捉到的问题，并通过评估我们的方法在多个数据集上的能力，证实了我们的方法在减少体腔脂肪面积方面可以协调纵向位置方差，为单片纵向分析提供了一个有希望的方向。