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Related Concept Videos

Imaging Studies III: Computed Tomography01:27

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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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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.
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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Time Domain Near-Infrared Optical Tomography Utilizing Full Temporal Data: A Simulation Study.

Letizia Lanini1,2, Alexander Kalyanov3, Meret Ackermann3

  • 1Department of Physics, ETH Zürich, Zürich, Switzerland. laninil@student.ethz.ch.

Advances in Experimental Medicine and Biology
|October 16, 2023
PubMed
Summary
This summary is machine-generated.

Full temporal data in time-domain near-infrared optical tomography (TD NIROT) offers superior 3D image reconstruction, especially for deep tissue. However, noise significantly impacts performance, necessitating denoising development.

Keywords:
Image qualityImage reconstructionTime Domain Near-Infrared Optical Tomography

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Area of Science:

  • Biomedical optics
  • Medical imaging
  • Tissue optical properties

Background:

  • Time-domain near-infrared optical tomography (TD NIROT) provides high-resolution tissue property analysis.
  • Handling large datasets from TD NIROT for 3D image reconstruction presents challenges.
  • Comparison with frequency domain (FD) and temporal moments methods is crucial for evaluating TD data utility.

Purpose of the Study:

  • To utilize full temporal data from TD NIROT for improved 3D image reconstruction.
  • To compare the performance of TD data-based reconstruction against FD and temporal moments methods.
  • To assess the impact of noise on TD data reconstruction accuracy.

Main Methods:

  • Developed and evaluated an iterative 3D image reconstruction algorithm using full temporal TD NIROT data.
  • Simulated in-silico data with both noiseless and noisy conditions.
  • Compared reconstruction results with those obtained from frequency domain (FD) data and temporal moments analysis.

Main Results:

  • Noiseless simulations showed superior image quality with full temporal data, particularly for deeper inclusions (≥20 mm).
  • In the presence of noise comparable to measured data, full temporal data did not outperform FD data or temporal moments.
  • Reconstruction quality degraded significantly with noise in TD data.

Conclusions:

  • Full temporal data inherently contains richer information for TD NIROT.
  • Improved image quality is achievable with full temporal data, especially in noise-free scenarios.
  • Development of effective denoising techniques for TD NIROT data is essential for practical applications.