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Metal artifacts correction based on a physics-informed nonlinear sinogram completion model.

Shuqiong Fan^1,2, Mengfei Li³, Chuwen Huang^1,2

¹School of Mathematical Sciences, Capital Normal University, Beijing 100048, People's Republic of China.

Physics in Medicine and Biology

|February 26, 2025

Summary

This summary is machine-generated.

This study introduces a new nonlinear sinogram decomposition method for metal artifact reduction (MAR) in CT imaging. The approach effectively suppresses artifacts while preserving image details, outperforming existing techniques.

Keywords:

computed tomography metal artifacts reduction nonlinear sinogram decomposition sinogram completion

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Area of Science:

Medical Imaging
Computational Imaging
Image Processing

Background:

Metal artifacts significantly degrade Computed Tomography (CT) image quality.
Existing metal artifact reduction (MAR) methods often provide incomplete correction or introduce new artifacts.

Purpose of the Study:

To develop an advanced sinogram completion approach for improved metal artifact suppression.
To extract and utilize information from corrupted projections for better MAR.

Main Methods:

A two-stage method involving improved normalization for sinogram interpolation and physics-informed nonlinear sinogram decomposition.
A novel nonlinear decomposition model to accurately separate metal and non-metal contributions in the sinogram.
Synergistic compensation between interpolated sinogram and physics-informed correction.

Main Results:

The proposed physics-informed nonlinear sinogram completion method demonstrates competitive performance in artifact suppression.
Experimental results show superior structure preservation and detail recovery compared to existing MAR methods.
Validation on both simulated and real CT data confirms the method's efficacy.

Conclusions:

The developed nonlinear sinogram decomposition model represents a novel approach for metal artifact correction.
This method offers a significant advancement in MAR, potentially improving diagnostic accuracy in CT scans.
The findings may inspire further research into nonlinear models for various sinogram processing tasks.