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Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

361
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
361

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ErisNet: A Deep Learning Model for Noise Reduction in CT Images.

Fabio Mattiussi1, Francesco Magoga1, Andrea Cozzi1

  • 1Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), 6900 Lugano, Switzerland.

Bioengineering (Basel, Switzerland)
|September 27, 2025
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Summary

ErisNet, a new AI model, significantly reduces noise in Computed Tomography (CT) images. This AI tool enhances image quality and shows promise for low-dose scan processing.

Keywords:
CT imagesdeep learningmedical imagingneural networknoise reduction

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Computed Tomography (CT) imaging is crucial for medical diagnosis.
  • Image noise reduction is essential for accurate interpretation of CT scans.
  • Existing noise reduction methods may compromise image details.

Purpose of the Study:

  • To introduce ErisNet, a novel artificial intelligence (AI) model designed for noise reduction in CT images.
  • To evaluate the objective and qualitative performance of ErisNet in denoising CT scans.
  • To assess the potential of ErisNet in improving the quality of low-dose CT scans.

Main Methods:

  • ErisNet was trained on 23 post-mortem whole-body CT scans.
  • Objective performance was assessed using metrics like MSE, PSNR, SSIM, VIF, EPI, and NV.
  • Qualitative assessment involved six radiologists evaluating image quality, noise suppression, and diagnostic confidence in specific regions of interest (ROIs).

Main Results:

  • ErisNet demonstrated significant noise reduction across various tissues, including brain parenchyma (18% decrease in SD of HU) and liver/spleen (15-19% decrease).
  • Objective metrics showed favorable results: MSE 64.07, PSNR 31.32 dB, SSIM 0.93.
  • Radiologists reported high scores for overall quality (4.5/5), noise suppression/detail preservation (4.7/5), and diagnostic confidence (4.8/5).

Conclusions:

  • ErisNet effectively reduces noise in CT images while preserving important details.
  • The AI model shows strong potential for enhancing the quality of low-dose CT scans.
  • ErisNet offers a promising solution for improving diagnostic accuracy in CT imaging.