Ucgnet: Capsule-Guided Gan for Ultrasound Image Reconstruction From Single-Plane Wave Rf Data Publisher



Samani M A ; Gharekhani A ; Farnia P ; Makki Abadi B
Source: Frontiers in Biomedical Technologies Published:2026

Abstract

Purpose: This study aims to improve ultrasound image reconstruction from single-plane wave RF data using Capsule Neural Networks, which can produce comparable image quality to Convolutional Neural Networks while requiring significantly fewer parameters. In addition to reducing model size, the proposed approach preserves clinically important image features and is better suited for real-time implementation in embedded systems with constrained computational resources. Materials and Methods: We propose a novel ultrasound image reconstruction architecture, UCGNet (U-CapsGAN Network), which combines Capsule Networks with a Generative Adversarial Network framework. UCGNet reconstructs high-quality B-mode ultrasound images directly from single-plane wave RF data and is evaluated on the Plane-wave Imaging Challenge in Medical Ultrasound (PICMUS) dataset. Capsule Networks play a key role in achieving parameter efficiency by encoding spatial hierarchies through vectorized feature representations. Their dynamic routing mechanism captures part-whole relationships and pose variations, enabling the network to preserve fine structural details essential for diagnostic imaging, without relying on deep, redundant convolutional layers. This makes the proposed architecture particularly well-suited for real-time applications in embedded systems with limited computational resources. Results: The reconstructed images achieved a mean Signal-to-Noise Ratio (SNR) of 18.45 and a Peak Signal-to-Noise Ratio (PSNR) of 40.92, outperforming the baseline UNet model in terms of image quality. Additionally, UCGNet required about 23 of the training parameters compared to UNet, demonstrating its suitability for real-time applications on resource-constrained devices. Conclusion: UCGNet provides an efficient and accurate solution for ultrasound image reconstruction from raw RF data. Its improved image fidelity and reduced computational complexity make it a strong candidate for practical use in portable and embedded medical imaging systems. Copyright © 2026 Tehran University of Medical Sciences. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International license (https://creativecommons.org/licenses/by-nc/4.0/). Noncommercial uses of the work are permitted, provided the original work is properly cited.