Feature-Aware Deep-Learning Reconstruction for Context-Sensitive X-ray Computed Tomography

Abstract

It is common for CT images to be reconstructed differently for different clinical examination purposes. It is difficult for conventional filtered backprojection (FBP) methods and standard model-based iterative reconstruction (MBIR) methods to produce a single context-sensitive image without multiple reconstructions. In this article, we address this challenge by leveraging the power of deep learning. We propose to train a deep convolution neural network to reconstruct a universal image from one FBP image. We present a new data argumentation method that generates the specific training target image, specifically the feature-aware target. The resulting method, called feature-aware deep-learning reconstruction (DLR), requires only one FBP image as input and is much faster than MBIR. In experiments, we investigate one application of low-dose CT feature-aware DLR which aims to achieve noise and resolution consistency across different body parts. We evaluate the performance of the proposed method using both simulated and real clinical low-dose CT scans. The results show that our feature-aware DLR outperforms both FBP and standard MBIR by producing improved CT image quality potentially suitable for a broad range of clinical diagnoses.