Two-View Topogram-Based Anatomy-Guided CT Reconstruction for Prospective
  Risk Minimization
Chang Liu Laura Klein Yixing Huang Edith Baader Michael Lell Marc Kachelrieß Andreas Maier
To facilitate a prospective estimation of CT effective dose and risk
minimization process, a prospective spatial dose estimation and the known
anatomical structures are expected. To this end, a CT reconstruction method is
required to reconstruct CT volumes from as few projections as possible, i.e. by
using the topograms, with anatomical structures as correct as possible. In this
work, an optimized CT reconstruction model based on a generative adversarial
network (GAN) is proposed. The GAN is trained to reconstruct 3D volumes from an
anterior-posterior and a lateral CT projection. To enhance anatomical
structures, a pre-trained organ segmentation network and the 3D perceptual loss
are applied during the training phase, so that the model can then generate both
organ-enhanced CT volume and the organ segmentation mask. The proposed method
can reconstruct CT volumes with PSNR of 26.49, RMSE of 196.17, and SSIM of
0.64, compared to 26.21, 201.55 and 0.63 using the baseline method. In terms of
the anatomical structure, the proposed method effectively enhances the organ
shape and boundary and allows for a straight-forward identification of the
relevant anatomical structures. We note that conventional reconstruction
metrics fail to indicate the enhancement of anatomical structures. In addition
to such metrics, the evaluation is expanded with assessing the organ
segmentation performance. The average organ dice of the proposed method is 0.71
compared with 0.63 in baseline model, indicating the enhancement of anatomical
structures.