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A GPU-accelerated fuzzy method for real-time CT volume filtering.

Celia Tendero Delicado1, Mónica Chillarón Pérez1, Josep Arnal García2

  • 1Department of Computer Systems and Computation, Universitat Politècnica de València, Valencia, Valencia, Spain.

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|January 2, 2025
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Summary
This summary is machine-generated.

This study introduces a real-time fuzzy logic filter for medical images, reducing noise in CT scans without compromising diagnostic quality. The GPU-accelerated filter significantly enhances image processing speed and quality, even at low radiation doses.

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Area of Science:

  • Medical Imaging
  • Image Processing
  • Computational Science

Background:

  • Medical images, particularly CT scans, can suffer from noise during acquisition and reconstruction, compromising diagnostic quality.
  • Reducing noise often requires higher radiation doses, posing risks to patients.
  • Existing filtering techniques aim to reduce noise without sacrificing diagnostic information.

Purpose of the Study:

  • To develop and implement a real-time filter for medical images, specifically CT scans.
  • To reduce mixed Gaussian-impulsive noise effectively.
  • To achieve significant speedups through GPU parallelization for practical clinical application.

Main Methods:

  • Development of fuzzy logic-based filter methods.
  • Implementation of filters optimized for Graphics Processing Units (GPUs).
  • Processing of CT scan attenuation coefficients to preserve diagnostic information.
  • Testing on CT volumes from the Mayo Clinic database (full and low-dose simulated).

Main Results:

  • GPU parallelization achieved speedups exceeding 2700x.
  • Filtering of over 300 slices was completed in less than 0.1 seconds.
  • The filter demonstrated competitive performance compared to state-of-the-art algorithmic and AI filters.
  • The method achieved good image quality and real-time processing capabilities.

Conclusions:

  • The proposed fuzzy logic filter effectively reduces noise in CT scans while maintaining diagnostic quality.
  • GPU implementation enables real-time processing, making it suitable for clinical use.
  • The method offers a viable solution for reducing radiation dose without compromising image integrity.