Constrained CycleGAN for effective generation of ultrasound sector images of improved spatial resolution

Sun, Xiaofei; Li, He; Lee, Wei-Ning

doi:10.1088/1361-6560/acd236

Cited by 3 publications

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Super-resolution reconstruction of ultrasound image using a modified diffusion model

Liu,

Han,

Xie

et al. 2024

Phys. Med. Biol.

View full text Add to dashboard Cite

Objective: This study aims to perform super-resolution (SR) reconstruction of ultrasound images using a modified diffusion model, designated as the Diffusion Model for Ultrasound Image Super-Resolution (DMUISR). SR involves converting low-resolution images to high-resolution ones, and the proposed model is designed to enhance the suitability of diffusion models for this task in the context of ultrasound imaging. Approach: DMUISR incorporates a multi-layer self-attention (MLSA) mechanism and a wavelet-transform based low-resolution image (WTLR) encoder to enhance its suitability for ultrasound image SR tasks. The model takes interpolated and magnified images as input and outputs high-quality, detailed SR images. The study utilized 1,334 ultrasound images from the public fetal head-circumference dataset (HC18) for evaluation. Main results: Experiments were conducted at 2×, 4×, and 8× magnification factors. DMUISR outperformed mainstream ultrasound SR methods (Bicubic, VDSR, DECUSR, DRCN, REDNet, SRGAN) across all scales, providing high-quality images with clear structures and rich detailed textures in both hard and soft tissue regions. DMUISR successfully accomplished multiscale SR reconstruction while suppressing over-smoothing and mode collapse problems. Quantitative results showed that DMUISR achieved the best performance in terms of learned perceptual image patch similarity (LPIPS), with a significant decrease of over 50% at all three magnification factors (2×, 4×, and 8×), as well as improvements in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Ablation experiments validated the effectiveness of the MLSA mechanism and WTLR encoder in improving DMUISR's SR performance. Furthermore, by reducing the number of diffusion steps, the computational time of DMUISR was shortened to nearly one-tenth of its original while maintaining image quality without significant degradation. Significance: This study demonstrates that the modified diffusion model, DMUISR, provides superior performance for SR reconstruction of ultrasound images and has potential in improving imaging quality in the medical ultrasound field.

show abstract

Super-resolution reconstruction of ultrasound image using a modified diffusion model

Liu,

Han,

Xie

et al. 2024

Phys. Med. Biol.

View full text Add to dashboard Cite

show abstract

Controllable Quality Improvement of Mobile Ultrasound

Chen,

Guo,

Sun

et al. 2024

2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

View full text Add to dashboard Cite

Enhancing the Super-Resolution of Medical Images: Introducing the Deep Residual Feature Distillation Channel Attention Network for Optimized Performance and Efficiency

Umirzakova,

Mardieva,

Muksimova

et al. 2023

Bioengineering

View full text Add to dashboard Cite

In the advancement of medical image super-resolution (SR), the Deep Residual Feature Distillation Channel Attention Network (DRFDCAN) marks a significant step forward. This work presents DRFDCAN, a model that innovates traditional SR approaches by introducing a channel attention block that is tailored for high-frequency features—crucial for the nuanced details in medical diagnostics—while streamlining the network structure for enhanced computational efficiency. DRFDCAN’s architecture adopts a residual-within-residual design to facilitate faster inference and reduce memory demands without compromising the integrity of the image reconstruction. This design strategy, combined with an innovative feature extraction method that emphasizes the utility of the initial layer features, allows for improved image clarity and is particularly effective in optimizing the peak signal-to-noise ratio (PSNR). The proposed work redefines efficiency in SR models, outperforming established frameworks like RFDN by improving model compactness and accelerating inference. The meticulous crafting of a feature extractor that effectively captures edge and texture information exemplifies the model’s capacity to render detailed images, necessary for accurate medical analysis. The implications of this study are two-fold: it presents a viable solution for deploying SR technology in real-time medical applications, and it sets a precedent for future models that address the delicate balance between computational efficiency and high-fidelity image reconstruction. This balance is paramount in medical applications where the clarity of images can significantly influence diagnostic outcomes. The DRFDCAN model thus stands as a transformative contribution to the field of medical image super-resolution.

show abstract

Constrained CycleGAN for effective generation of ultrasound sector images of improved spatial resolution

Cited by 3 publications

References 59 publications

Super-resolution reconstruction of ultrasound image using a modified diffusion model

Super-resolution reconstruction of ultrasound image using a modified diffusion model

Controllable Quality Improvement of Mobile Ultrasound

Enhancing the Super-Resolution of Medical Images: Introducing the Deep Residual Feature Distillation Channel Attention Network for Optimized Performance and Efficiency

Contact Info

Product

Resources

About