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X-ray Imaging01:24

X-ray Imaging

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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...
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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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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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Updated: Feb 19, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
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High-speed quantitative X-ray multi-contrast imaging with deep learning based modulated pattern analysis.

Zhi Qiao1, Yudong Yao1, Hongyu Chen1

  • 1Center of Transformative Science, ShanghaiTech University, Shanghai 201210, China.

Journal of Synchrotron Radiation
|February 17, 2026
PubMed
Summary
This summary is machine-generated.

Enhanced Scanning Pattern-based Imaging Neural Network (ESPINNet) offers faster, high-resolution X-ray imaging. This advanced AI tool improves multi-contrast visualization for materials and bio-samples in real-time.

Keywords:
X-ray at-wavelength metrologydeep learningphase contrast imagingspeckle trackingwavefront sensing

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Area of Science:

  • Physics
  • Materials Science
  • Biomedical Imaging

Background:

  • X-ray multi-contrast imaging (absorption, phase, dark-field) enables non-destructive visualization.
  • Low efficiency in X-ray pattern analysis limits high-resolution, in situ imaging applications.

Purpose of the Study:

  • Introduce the Enhanced Scanning Pattern-based Imaging Neural Network (ESPINNet) for high-speed, high-resolution quantitative imaging.
  • Enhance the capabilities of previous neural networks by enabling dark-field image generation.

Main Methods:

  • Leveraging scanning patterns for improved resolution and measurement precision.
  • Utilizing a neural network architecture (ESPINNet) adaptable to various modulation patterns (sandpaper, coded masks, gratings).

Main Results:

  • ESPINNet achieves faster data collection compared to correlation-based methods (XSVT, UMPA).
  • Demonstrates balanced performance in resolution and speed, requiring fewer scanning images.
  • Enables real-time 2D and 3D multi-contrast imaging.

Conclusions:

  • ESPINNet is a transformative solution for high-speed, in situ X-ray imaging.
  • Broad applicability in materials science and biomedical research due to its adaptability and enhanced capabilities.