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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Updated: Aug 9, 2025

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

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

Published on: May 17, 2018

Physics-informed sinogram completion for metal artifact reduction in CT imaging.

Manman Zhu^1,2, Qisen Zhu², Yuyan Song^1,2

¹School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China.

Physics in Medicine and Biology

|February 22, 2023

Summary

This summary is machine-generated.

This study introduces a novel physics-informed sinogram completion (PISC) method to reduce metal artifacts in computed tomography (CT) imaging. PISC effectively preserves structural details, overcoming limitations of existing metal artifact reduction (MAR) techniques.

Keywords:

computed tomography metal artifacts reduction physical correction sinogram completion

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Published on: September 29, 2019

Area of Science:

Medical Imaging
Computational Imaging
Image Processing

Background:

Metal artifacts in computed tomography (CT) imaging significantly hinder clinical diagnosis and treatment.
Existing metal artifact reduction (MAR) methods often cause over-smoothing and loss of structural details near metal implants, especially those with irregular shapes.

Purpose of the Study:

To present a novel physics-informed sinogram completion (PISC) method for MAR in CT imaging.
To reduce metal artifacts and enhance the recovery of structural textures near metal implants.

Main Methods:

The PISC method involves sinogram completion using normalized linear interpolation and beam-hardening correction based on physical models.
A pixel-wise adaptive weight fusion combines corrected sinograms, guided by implant shape and material information.
A post-processing frequency split algorithm is applied after sinogram reconstruction to further refine image quality.

Main Results:

The PISC method was evaluated on simulated and real CT datasets, demonstrating effective artifact correction for various metal implant shapes and materials.
Results show significant improvement in artifact suppression while preserving crucial structural details near implants.
Qualitative and quantitative analyses confirmed the efficacy of the PISC method.

Conclusions:

The proposed PISC method offers a sinogram-domain approach to MAR, addressing the over-smoothing issue prevalent in other techniques.
By incorporating physical prior knowledge, PISC enhances artifact reduction and structure preservation.
This method shows potential for improving the performance of deep learning-based MAR approaches.