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Related Concept Videos

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.
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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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...
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...
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
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Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo
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Binary compressed imaging.

Aurélien Bourquard1, Michael Unser

  • 1Biomedical Imaging Group, École polytechnique fédérale de Lausanne, Lausanne, Switzerland. aurelien.bourquard@epfl.ch

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 30, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for binary compressed sensing tailored for images. It enables high-quality image reconstruction from minimal, one-bit quantized measurements.

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Area of Science:

  • Signal Processing
  • Image Acquisition
  • Computer Vision

Background:

  • Compressed sensing reduces sampling needs but requires reconstruction.
  • Quantized measurements, including one-bit, offer robust compressed sensing.
  • High-dimensional image data presents unique challenges for binary compressed sensing.

Purpose of the Study:

  • To design a binary compressed sensing framework specifically for image data.
  • To develop an acquisition and reconstruction approach suitable for high-dimensional images.
  • To achieve satisfactory visual quality in reconstructed images.

Main Methods:

  • A forward model based on physical principles: optical random convolutions, sampling, and binary thresholding.
  • A reconstruction problem formulated as minimizing a compound convex cost with total-variation regularization.
  • An efficient reconstruction algorithm derived from convex optimization principles.

Main Results:

  • The proposed approach handles high-dimensional image data effectively.
  • Reconstructions achieve satisfactory visual quality.
  • Experimental results on standard images demonstrate practical utility.

Conclusions:

  • The developed framework offers a viable solution for binary compressed sensing in imaging.
  • The method balances reduced sampling with high-quality image reconstruction.
  • This work highlights the potential of binary compressed sensing for image acquisition.