  2D low-dose single-slice abdominal computed tomography (CT) slice enables
direct measurements of body composition, which are critical to quantitatively
characterizing health relationships on aging. However, longitudinal analysis of
body composition changes using 2D abdominal slices is challenging due to
positional variance between longitudinal slices acquired in different years. To
reduce the positional variance, we extend the conditional generative models to
our C-SliceGen that takes an arbitrary axial slice in the abdominal region as
the condition and generates a defined vertebral level slice by estimating the
structural changes in the latent space. Experiments on 1170 subjects from an
in-house dataset and 50 subjects from BTCV MICCAI Challenge 2015 show that our
model can generate high quality images in terms of realism and similarity.
External experiments on 20 subjects from the Baltimore Longitudinal Study of
Aging (BLSA) dataset that contains longitudinal single abdominal slices
validate that our method can harmonize the slice positional variance in terms
of muscle and visceral fat area. Our approach provides a promising direction of
mapping slices from different vertebral levels to a target slice to reduce
positional variance for single slice longitudinal analysis. The source code is
available at: https://github.com/MASILab/C-SliceGen.
