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  1. Home
  3. Gradient Guided Co-retention Feature Pyramid Network For Ldct Image Denoising.
  1. Home
  3. Gradient Guided Co-retention Feature Pyramid Network For Ldct Image Denoising.

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Gradient Guided Co-Retention Feature Pyramid Network for LDCT Image Denoising.

Li Zhou1, Dayang Wang1, Yongshun Xu1

  • 1University of Massachusetts Lowell, Lowell MA 01854, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|September 19, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

We developed a Gradient Guided Co-Retention Feature Pyramid Network (G2CR-FPN) to improve low-dose computed tomography (LDCT) image quality. This method enhances feature maps, reducing noise and artifacts in medical imaging.

Keywords:
Directional gradientsFeature pyramidLDCT denoisingRetention

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Low-dose computed tomography (LDCT) reduces radiation exposure but introduces image noise and artifacts.
  • Conventional Feature Pyramid Networks (FPNs) struggle to balance spatial resolution and semantic value in feature extraction.
  • Existing methods often generalize details in upper layers, diminishing fine image features.

Purpose of the Study:

  • To propose a novel network, the Gradient Guided Co-Retention Feature Pyramid Network (G2CR-FPN), for enhancing LDCT images.
  • To address the trade-off between spatial resolution and semantic information in feature maps derived from LDCT scans.
  • To improve the quality of CT images obtained with reduced radiation doses.

Main Methods:

  • The G2CR-FPN employs a three-path structure: bottom-up, lateral, and top-down.
  • The bottom-up path generates hierarchical feature maps using FPN principles.
  • The lateral path integrates directional gradient approximations for edge enhancement, while the top-down path uses a co-retention block guided by these gradients to preserve semantic value.

    • Main Results:

    • Experimental results on clinical CT images demonstrated the effectiveness of the G2CR-FPN.
    • The proposed network successfully addressed the challenge of maintaining both spatial detail and semantic richness in LDCT feature maps.
    • The G2CR-FPN showed promising performance in improving the quality of low-dose CT images.

    Conclusions:

    • The G2CR-FPN offers a significant advancement in processing LDCT images by effectively managing the spatial-semantic trade-off.
    • This novel approach holds potential for enhancing diagnostic accuracy in medical imaging applications utilizing low-dose CT scans.
    • The developed G2CR-FPN framework provides a robust solution for noise and artifact reduction in low-dose CT imaging.