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RFDNet: Robust Frequency-Based Denoising Network for 3D Ultrasound Vascular Imaging Using a Row-Column Addressed

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Summary

A new Robust Frequency-based Denoising Network (RFDNet) effectively reduces ramp-shaped noise in 3D ultrasound vascular imaging (UVI). This method enhances image quality and reliability for better clinical applications.

Keywords:
deep-learningfrequency filteringreductionrow column addressedultrasound

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Area of Science:

  • Medical Imaging
  • Ultrasound Technology
  • Artificial Intelligence in Medicine

Background:

  • Three-dimensional ultrasound vascular imaging (3D UVI) is crucial for visualizing complex vascular structures.
  • Row-column addressed (RCA) arrays, common in 3D UVI, introduce ramp-shaped noise due to point spread function (PSF) anisotropy, degrading image quality.
  • Existing denoising methods struggle with domain shift bias, specific data needs, and inter-slice inconsistencies from 2D slice-wise training.

Purpose of the Study:

  • To develop a novel denoising method, Robust Frequency-based Denoising Network (RFDNet), to overcome limitations in 3D UVI.
  • To suppress ramp-shaped noise and improve image consistency in 3D UVI.
  • To enhance robustness against domain shifts and inter-slice intensity variations.

Main Methods:

  • Proposed RFDNet integrating a Deep Frequency Filtering (DFF) module into a standard denoising model.
  • The DFF module adaptively filters frequency components in the encoder to suppress noise and balance spectral content.
  • Evaluated performance using Doppler phantom, carotid artery, and abdominal datasets.

Main Results:

  • RFDNet significantly outperformed conventional methods in peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean squared error (RMSE).
  • 2D frequency spectrum analysis confirmed the DFF module's ability to dynamically adjust frequency components and maintain spectral balance.
  • Spectral KL divergence analysis demonstrated robustness against slice-wise normalization inconsistencies.

Conclusions:

  • RFDNet effectively reduces noise artifacts and improves imaging consistency in 3D UVI.
  • The adaptive frequency filtering enhances domain generalization and clinical applicability by improving imaging reliability.
  • Future work includes exploring 3D training and architectural refinements for improved computational efficiency.