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Metal artifact reduction in x-ray computed tomography (CT) by constrained optimization.

Xiaomeng Zhang¹, Jing Wang, Lei Xing

¹Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA.

Medical Physics

|April 2, 2011

Summary

This study introduces an iterative CT reconstruction algorithm using constrained optimization to reduce metal artifacts from implants. The method effectively suppresses noise and enhances soft-tissue visibility, outperforming existing techniques.

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Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences

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Published on: May 17, 2018

Area of Science:

Medical Imaging
Computational Imaging
Image Reconstruction

Background:

Metal artifacts in CT imaging limit diagnostic accuracy.
Existing methods struggle to fully resolve artifacts caused by metal implants.

Purpose of the Study:

To develop an iterative algorithm for metal artifact reduction (MAR) in CT using constrained optimization.
To improve image quality and soft-tissue visibility in the presence of metal implants.

Main Methods:

Automatic identification and segmentation of metal objects and their corresponding 'metal shadows' in projection data.
Image reconstruction via constrained optimization, minimizing a priori image knowledge subject to projection data constraints.
Iterative solution using projection-onto-convex-sets and steepest gradient descent, with enforced non-negativity.

Main Results:

Significant reduction in metal artifacts and noise.
Enhanced soft-tissue conspicuity and diagnostic image quality.
Outperformance compared to Filtered Back Projection (FBP), Algebraic Reconstruction Technique (ART), and Expectation-Maximization (EM) methods.

Conclusions:

Constrained optimization offers an effective solution for CT reconstruction with metallic objects.
The proposed algorithm is applicable to general image reconstruction problems with missing data.