Convolutional Neural Network Based Metal Artifact Reduction in X-ray Computed Tomography

TL;DR

This paper introduces a CNN-based framework for metal artifact reduction in X-ray CT images, combining original and corrected images to effectively suppress artifacts while preserving anatomical details.

Contribution

The paper presents a novel open framework that fuses original and corrected CT images using CNNs for improved metal artifact reduction in clinical X-ray CT.

Findings

Outperforms existing methods in artifact suppression

Preserves anatomical structures near metal implants

Validated on both simulated and real data

Abstract

In the presence of metal implants, metal artifacts are introduced to x-ray CT images. Although a large number of metal artifact reduction (MAR) methods have been proposed in the past decades, MAR is still one of the major problems in clinical x-ray CT. In this work, we develop a convolutional neural network (CNN) based open MAR framework, which fuses the information from the original and corrected images to suppress artifacts. The proposed approach consists two phases. In the CNN training phase, we build a database consisting of metal-free, metal-inserted and pre-corrected CT images, and image patches are extracted and used for CNN training. In the MAR phase, the uncorrected and pre-corrected images are used as the input of the trained CNN to generate a CNN image with reduced artifacts. To further reduce the remaining artifacts, water equivalent tissues in a CNN image are set to a uniform value to yield a CNN prior, whose forward projections are used to replace the metal-affected projections, followed by the FBP reconstruction. The effectiveness of the proposed method is validated on both simulated and real data. Experimental results demonstrate the superior MAR capability of the proposed method to its competitors in terms of artifact suppression and preservation of anatomical structures in the vicinity of metal implants.