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MARGANVAC: metal artifact reduction method based on generative adversarial network with variable constraints.

Guang Li1, Longyin Ji1, Chenyu You2

  • 1Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, People's Republic of China.

Physics in Medicine and Biology
|September 11, 2023
PubMed
Summary
This summary is machine-generated.

A new deep learning method, Metal Artifact Reduction Generative Adversarial Network with Variable Constraints (MARGANVAC), enhances CT imaging by reducing metal artifacts. It overcomes data limitations and performs comparably to advanced techniques in various CT scenarios.

Keywords:
metal artifacts reductioncomputed tomography (CT)generative adversarial networkvariable constraint

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Metal artifact reduction (MAR) is crucial in CT imaging, with deep learning showing promise.
  • Existing deep learning MAR methods face challenges with paired training data and performance limitations in image-domain vs. end-to-end approaches.
  • Image-domain methods offer broad applicability but often lack performance, while high-performing end-to-end methods are limited to specific CT types (fan-beam) due to memory constraints.

Purpose of the Study:

  • To introduce a novel image-domain MAR method, MARGANVAC, to improve MAR performance.
  • To address the lack of paired training data in real-world clinical settings.
  • To develop a method applicable to various CT scenarios (fan-beam and cone-beam) with performance comparable to dual-domain approaches.

Main Methods:

  • Proposed a Metal Artifact Reduction Generative Adversarial Network with Variable Constraints (MARGANVAC), an image-domain deep learning model.
  • Introduced a variable constraint (time-varying cost function) that adjusts fidelity constraints during training.
  • Developed a transfer method to generate paired training data by extracting real metal traces and adding them to artifact-free images.

Main Results:

  • The MARGANVAC method demonstrated superior performance in both simulated fan-beam and real cone-beam CT experiments.
  • Quantitative and qualitative evaluations confirmed the effectiveness of the proposed approach over competing methods.
  • The metal artifact transfer method successfully generated realistic paired data for model training.

Conclusions:

  • MARGANVAC is a versatile image-domain MAR model applicable to diverse CT systems (fan-beam, cone-beam).
  • The model achieves performance on par with state-of-the-art dual-domain MAR techniques.
  • The proposed data generation method facilitates practical deployment of supervised MAR in real clinical scenarios.