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Related Concept Videos

Computed Tomography01:10

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
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Related Experiment Video

Updated: Sep 7, 2025

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
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Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography.

Illia Horenko1, Lukáš Pospíšil2, Edoardo Vecchi3

  • 1Faculty of Mathematics, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany.

Journal of Imaging
|June 23, 2022
PubMed
Summary

We developed a new method to create personalized CT images, reducing radiation exposure and lifetime risk. Our advanced denoising technique significantly lowers cancer risk for all ages, especially infants.

Keywords:
LAR reductionMumford–Shah formalismdenoisingnonparametric methods

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Area of Science:

  • Medical Imaging
  • Computational Radiology
  • Radiation Safety

Background:

  • Computer Tomography (CT) imaging involves radiation exposure, posing a lifetime attributable risk (LAR).
  • Existing denoising algorithms vary in effectiveness and computational cost for personalized CT image enhancement.
  • Reducing CT-induced LAR while maintaining image quality is crucial for patient safety.

Purpose of the Study:

  • To propose a pipeline for synthetic generation of personalized CT images.
  • To evaluate patient-specific CT-induced LAR reduction and computational scalability of denoising algorithms.
  • To introduce and validate a novel parallel Probabilistic Mumford−Shah (PMS) denoising model.

Main Methods:

  • Development of a synthetic image generation pipeline with radiation exposure and LAR assessment.
  • Comparative performance evaluation of various denoising algorithms (deep learning, wavelets, Mumford-Shah variants).
  • Introduction and implementation of a parallel Probabilistic Mumford−Shah (PMS) denoising model.

Main Results:

  • The PMS model significantly outperforms common denoising methods in quality and scalability.
  • Achieved ~22-fold LAR reduction in infants and 10-fold in adults.
  • Robust denoising (>90% SSIM) of large, ultra-noisy images on standard hardware.

Conclusions:

  • The proposed pipeline and PMS model offer effective, low-cost CT image denoising.
  • Significant reduction in patient-specific CT-induced LAR is achievable.
  • Open-access code facilitates further research and application in medical imaging.