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

Yanbo Zhang, Hengyong Yu

IEEE Transactions on Medical Imaging

|June 6, 2018

Summary

This study introduces a novel convolutional neural network (CNN) framework for metal artifact reduction (MAR) in X-ray computed tomography (CT) imaging. The method effectively suppresses artifacts, improving image quality near metal implants.

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Area of Science:

Medical Imaging
Computer Vision
Radiology

Background:

Metal artifacts significantly degrade X-ray computed tomography (CT) image quality, posing challenges in clinical diagnosis.
Despite decades of research, effective metal artifact reduction (MAR) remains a critical issue in medical imaging.

Purpose of the Study:

To develop and validate a novel convolutional neural network (CNN)-based framework for enhanced metal artifact reduction (MAR) in X-ray CT.
To improve the visualization and diagnostic accuracy of CT images containing metal implants.

Main Methods:

A two-phase CNN-based open MAR framework was developed, fusing information from original and corrected images.
The framework involves a CNN training phase using a curated database and an MAR phase utilizing trained CNNs for artifact suppression.
Post-CNN processing includes generating a CNN prior and incorporating its forward projections into filtered back-projection (FBP) reconstruction.

Main Results:

The proposed method demonstrated superior artifact suppression compared to existing techniques.
Preservation of anatomical structures adjacent to metal implants was significantly improved.
Validation on both simulated and real clinical data confirmed the method's effectiveness.

Conclusions:

The developed CNN-based MAR framework offers a promising solution for overcoming metal artifact challenges in X-ray CT.
This approach enhances diagnostic confidence by providing clearer images in the presence of metallic hardware.