Deep conditional generative models for longitudinal single-slice
  abdominal computed tomography harmonization
Xin Yu Qi Yang Yucheng Tang Riqiang Gao Shunxing Bao Leon Y. Cai Ho Hin Lee Yuankai Huo Ann Zenobia Moore Luigi Ferrucci Bennett A. Landman
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:
https://github.com/MASILab/C-SliceGen.