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Semi-supervised dual generative adversarial network for low-dose CT artifact suppression.

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This study introduces a semi-supervised dual generative adversarial network for low-dose computed tomography (LDCT) denoising. The method effectively reduces noise and artifacts in LDCT images using both paired and unpaired data.

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Deep learning, particularly supervised methods, has advanced low-dose computed tomography (LDCT) denoising.
Acquiring sufficient paired training data for LDCT denoising is challenging.
Supervised learning is difficult to apply directly to unpaired CT image data.

Purpose of the Study:

To develop a novel semi-supervised deep learning approach for LDCT denoising.
To effectively utilize both paired and unpaired CT data for improved network performance.
To enhance artifact and noise suppression in LDCT images.

Main Methods:

Designed a semi-supervised dual generative adversarial network (GAN).
Employed alternating iterations of supervised and unsupervised training using paired and unpaired data.

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Utilized interactive adversarial training between two GANs for image generation and noise reduction.

Incorporated a multi-description loss function to improve training stability and performance.

Main Results:

Achieved effective suppression of artifacts and noise in LDCT images.
Demonstrated superior noise reduction compared to existing supervised and unsupervised methods.
Preserved the original structural information of CT images effectively.

Conclusions:

The proposed semi-supervised dual GAN effectively denoises LDCT images.
This approach overcomes limitations of data acquisition and utilization of unpaired data.
It offers a promising method for improving LDCT image quality while maintaining anatomical integrity.