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

TL;DR

This paper introduces a GAN-based CT reconstruction method from minimal projections that enhances anatomical structures and improves organ segmentation accuracy, aiding prospective dose estimation and risk minimization.

Contribution

It proposes a novel GAN model incorporating organ segmentation and perceptual loss to reconstruct anatomically accurate CT volumes from limited projections.

Findings

Reconstructed CT volumes achieved PSNR of 26.49 and SSIM of 0.64.

Organ segmentation dice score improved to 0.71.

Method effectively enhances organ shape and boundary details.

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.