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Related Concept Videos

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
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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...
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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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Thinned-skull Cortical Window Technique for In Vivo Optical Coherence Tomography Imaging
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Fast and customizable image formation model for optical coherence tomography.

Andrea Mazzolani1, Callum Macdonald1, Peter R T Munro1

  • 1Department of Medical Physics and Biomedical Engineering, University College London, Malet Place, Gower Street, London WC1E 6BT, UK.

Biomedical Optics Express
|December 16, 2024
PubMed
Summary
This summary is machine-generated.

A new, faster optical coherence tomography (OCT) image formation model improves realism for applications like optical coherence elastography and deep learning training. This computationally efficient model aids OCT image interpretation and signal processing validation.

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Area of Science:

  • Biomedical Optics
  • Medical Imaging Technology

Background:

  • Optical coherence tomography (OCT) provides high-resolution 3D imaging of biological tissues.
  • Accurate OCT image formation models are crucial for image interpretation and signal processing validation.
  • Existing models often compromise between realism and computational efficiency, limiting applications like C-scan generation.

Purpose of the Study:

  • To develop a computationally efficient and realistic OCT image formation model.
  • To enable simulations for phase-sensitive OCT applications, including optical coherence elastography (OCE) and Doppler OCT.
  • To facilitate the creation of large datasets for training deep learning models in OCT signal processing.

Main Methods:

  • Developed a novel OCT image formation model utilizing the first-order Born approximation.
  • Focused on achieving a high degree of realism with significantly reduced computation time compared to existing models.
  • Ensured the model's compatibility with phase-sensitive OCT simulations.

Main Results:

  • The proposed model demonstrates significantly faster computation times than existing realistic models.
  • The model maintains a high level of realism in OCT image formation.
  • The model's efficiency is suitable for simulating C-scans and generating large datasets for deep learning.

Conclusions:

  • The novel OCT image formation model offers a balance of speed and realism.
  • It supports advanced OCT techniques like OCE and Doppler OCT.
  • The model is a valuable tool for OCT research, particularly for deep learning applications.