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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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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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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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Quantification of Nasal Septal Deviation With Computed Tomography Data.

Erika Denour1,2, Lauren O Roussel2, Albert S Woo2

  • 1Department of Molecular Pharmacology, Physiology, and Biotechnology, Brown University.

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|June 6, 2020
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Summary
This summary is machine-generated.

Objective measures for nasal septal deviation (NSD) severity are challenging. This study found that two-dimensional computed tomography (CT) landmarks, specifically maximum deviation and deviation area, best predict three-dimensional (3D) septal morphology.

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Area of Science:

  • Radiology
  • Otolaryngology
  • Medical Imaging Analysis

Background:

  • Standardized objective measures for evaluating nasal septal deviation (NSD) severity remain a challenge for preoperative planning.
  • Existing literature lacks consistent quantitative methods to assess NSD, hindering accurate surgical preparation.
  • This study addresses the need for reliable 2D computed tomography (CT) landmarks to predict 3D septal morphology.

Purpose of the Study:

  • To quantitatively analyze nasal septal deviation (NSD) using computed tomography (CT) data.
  • To identify the most predictive two-dimensional (2D) CT-landmark for overall three-dimensional (3D) septal morphology.
  • To establish a robust, open-source method for predicting NSD severity from CT images.

Main Methods:

  • A retrospective study of 104 patients who underwent facial CT scans.
  • 3D nasal cavity segmentation was performed using 3D Slicer software for volumetric analysis and 3D NSD ratio determination.
  • 2D NSD measures, including maximum deviation, deviation area, root mean square, and curve to line ratio, were calculated using OsiriX and MATLAB and compared to 3D ratios.

Main Results:

  • Strong correlations were found between the 3D NSD ratio and 2D maximum deviation (r = 0.789) and deviation area (r = 0.775).
  • Moderate positive correlations were observed for deviation area (r = 0.563) and root mean square (r = 0.594).
  • The curve to line ratio showed a non-significant correlation (r = 0.019).

Conclusions:

  • Two-dimensional CT-based landmarks, specifically maximum deviation and deviation area, are the most predictive of NSD severity based on 3D nasal cavity segmentation.
  • The developed open-source method offers a potentially valuable tool for predicting NSD severity in CT imaging.
  • These findings can aid in more accurate preoperative planning for patients with nasal septal deviation.