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Related Concept Videos

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.
Computed Tomography01:10

Computed Tomography

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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
X-ray Imaging01:24

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with X-rays, and by 1900, X-ray was widely...

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Related Experiment Video

Updated: May 20, 2026

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

Simultaneous super resolution and denoising in X-ray CT using deep learning.

Masakazu Matsuura1,2, Tzu-Cheng Lee3, Xiaofeng Niu3

  • 1Canon Medical Systems Corporation, Otawara, Tochigi, Japan.

Journal of X-Ray Science and Technology
|May 19, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning reconstruction method simultaneously enhances spatial resolution and reduces noise in X-ray computed tomography (CT) images. This advanced technique, SR-Denoise DLR, improves image quality for clinical applications.

Keywords:
chest CT imagesdeep learning reconstructionnoise reductionsuper resolutionultra-high-resolution computed tomography

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Non-invasive 3D-Visualization with Sub-micron Resolution Using Synchrotron-X-ray-tomography
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Last Updated: May 20, 2026

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
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08:51

Non-invasive 3D-Visualization with Sub-micron Resolution Using Synchrotron-X-ray-tomography

Published on: May 27, 2008

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Radiology

Background:

  • X-ray computed tomography (CT) image reconstruction often requires balancing spatial resolution and noise reduction.
  • Current methods may involve separate processes for super-resolution (SR) and denoising, impacting efficiency.

Purpose of the Study:

  • To develop a single neural network capable of performing both super-resolution (SR) and denoising simultaneously.
  • To introduce a novel deep learning reconstruction (DLR) method named SR-Denoise DLR.

Main Methods:

  • Utilized ultra-high-resolution CT (UHR-CT) data to generate normal-resolution, high-noise (NR-CT) input data.
  • Trained a single neural network (SR-Denoise DLR) on simulated NR-CT data to achieve SR and denoising.
  • Quantitatively assessed performance using modulation transfer function and evaluated with simulated and real clinical NR-CT data against UHR-CT ground truth.

Main Results:

  • SR-Denoise DLR demonstrated equivalent performance to individually trained SR and denoising methods.
  • The proposed method significantly outperformed existing clinical CT reconstruction techniques.
  • Achieved substantial noise reduction while maintaining high spatial resolution.

Conclusions:

  • SR-Denoise DLR effectively leverages UHR-CT data for high spatial resolution.
  • The method significantly reduces noise in NR-CT data, enhancing diagnostic image quality.
  • Presents a promising integrated approach for advanced CT image reconstruction.