Field of View Extension in Computed Tomography Using Deep Learning Prior

TL;DR

This paper introduces a data consistent deep learning-based method for extending the field of view in CT imaging, effectively reducing artifacts and improving image accuracy compared to traditional post-processing approaches.

Contribution

The study presents a novel reconstruction framework combining deep learning prior with iterative reconstruction to ensure data fidelity in FOV extension for CT images.

Findings

Achieved an average RMSE of 24 HU inside the FOV.

Attained a structure similarity index of 0.993 for whole-body CT.

Demonstrated improved artifact reduction over existing methods.

Abstract

In computed tomography (CT), data truncation is a common problem. Images reconstructed by the standard filtered back-projection algorithm from truncated data suffer from cupping artifacts inside the field-of-view (FOV), while anatomical structures are severely distorted or missing outside the FOV. Deep learning, particularly the U-Net, has been applied to extend the FOV as a post-processing method. Since image-to-image prediction neglects the data fidelity to measured projection data, incorrect structures, even inside the FOV, might be reconstructed by such an approach. Therefore, generating reconstructed images directly from a post-processing neural network is inadequate. In this work, we propose a data consistent reconstruction method, which utilizes deep learning reconstruction as prior for extrapolating truncated projections and a conventional iterative reconstruction to constrain the reconstruction consistent to measured raw data. Its efficacy is demonstrated in our study, achieving small average root-mean-square error of 24 HU inside the FOV and a high structure similarity index of 0.993 for the whole body area on a test patient's CT data.