Two-View Topogram-Based Anatomy-Guided CT Reconstruction for Prospective Risk Minimization

TL;DR

An optimized CT reconstruction model based on a generative adversarial network (GAN) based on an anterior-posterior and a lateral CT projection is proposed, which effectively enhances the organ shapes and boundaries and allows for a straight-forward identification of the relevant anatomical structures.

Abstract

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.

Figures (8)

• Figure 1: Illustration of prospective and retrospective CT organ dose estimation pipelines. Many existing methods to estimate CT organ dose are designed as retrospective pipelines, which can only be applied after the scanning. For application like CT risk minimization, a prospective pipeline for organ dose estimation is required.
• Figure 2: Exemplary slices of the reconstructed CT volumes using the proposed and baseline method, in comparison with the ground truth.
• Figure 3: Comparison of the organ segmentation masks generated in different experiments. In the top three rows, the slices of the organ segmentation mask and the CT volumes are shown and in the bottom rows the organ segmentation is shown as mesh visualization.
• Figure 4: Reconstruction and organ segmentation performance of the proposed method with varying $\lambda$, in comparison with applying $L_p$ and $L_s$ independently.
• Figure 5: Example slices that GANs fails to reconstruct anatomical structures in volumes. Column (a) is our proposed method with enhanced anatomical structures. (b) and (c) illustrate the CT reconstruction with deteriorated anatomical structure but high reconstruction metrics.