DF-GAM: Cross-Domain Ultrasound Image High-Quality Reconstruction Using a Dual Frequency-Domain Guided Adaptation Model.
Ultrasound Med Biol
; 50(9): 1403-1414, 2024 Sep.
Article
em En
| MEDLINE
| ID: mdl-38942620
ABSTRACT
OBJECTIVE:
To enhance the quality of low-resolution (LR) ultrasound images and mitigate artifacts and speckle noise, which can impede accurate medical diagnosis, a novel method called the dual frequency-domain guided adaptation model (DF-GAM) is proposed. The method aims to achieve high-quality image reconstruction across diverse domains, including different ultrasound machines, diseases and phantom images.METHODS:
DF-GAM utilizes a dual-branch network architecture combined with frequency-domain self-adaptation and self-supervised edge regression. This approach enables cross-domain enhancement by focusing on the reconstruction of clear tissue structures and speckle patterns. The model is designed to adapt to various ultrasound imaging (USI) scenarios, ensuring its applicability in real-world clinical settings.RESULTS:
Experimental evaluations of DF-GAM were conducted using five different datasets. The results demonstrated the method's effectiveness, with DF-GAM outperforming existing enhancement techniques. The average peak signal-to-noise ratio (PSNR) achieved was 34.62, and the structural similarity index (SSIM) was 0.91, indicating a significant improvement in image quality compared to other methods.CONCLUSION:
DF-GAM shows great potential in improving medical image diagnosis and interpretation. Its ability to enhance LR ultrasound images across various domains without the need for extensive training data makes it a valuable tool for clinical use. The high PSNR and SSIM scores validate the method's effectiveness, suggesting that DF-GAM could significantly contribute to the field of USI diagnostics.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Ultrassonografia
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Imagens de Fantasmas
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article