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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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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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Evaluating Regional Pulmonary Deposition using Patient-Specific 3D Printed Lung Models
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A computed tomography imaging-based subject-specific whole-lung deposition model.

Xuan Zhang1, Frank Li2, Prathish K Rajaraman3

  • 1Department of Mechanical Engineering, 2406 Seamans Center for the Engineering Art and Science, University of Iowa, Iowa City, Iowa 52242, USA; IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, Iowa, USA.

European Journal of Pharmaceutical Sciences : Official Journal of the European Federation for Pharmaceutical Sciences
|July 31, 2022
PubMed
Summary

This study introduces an advanced imaging-based model for predicting where particles deposit in the lungs. The subject-specific model accurately simulates airflow and particle behavior, improving assessments of drug delivery and health risks.

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Area of Science:

  • Pulmonary medicine
  • Biomedical engineering
  • Computational fluid dynamics

Background:

  • The respiratory tract is a key pathway for inhaled substances, necessitating accurate models for assessing therapeutic responses and disease risks.
  • Existing whole-lung deposition models are limited by compartment, symmetry, or stochastic approaches.
  • Subject-specific modeling is crucial for personalized medicine and accurate risk assessment.

Purpose of the Study:

  • To develop and validate an imaging-based, subject-specific whole-lung deposition model.
  • To improve the accuracy of predicting particle deposition in the conducting and respiratory regions of the lung.
  • To enhance the assessment of drug aerosol delivery and particulate matter risk.

Main Methods:

  • Segmented airway and lobe geometries from CT lung images at total lung capacity (TLC).
  • Calculated regional air-volume changes by registering CT images at TLC and functional residual capacity (FRC).
  • Simulated airflow using computational fluid dynamics and calculated particle deposition fractions with an enhancement factor.

Main Results:

  • The model was validated in silico against existing models and in vivo using CT and SPECT imaging.
  • Subject-specific airway structure increased deposition fraction for 10.0-microm and 0.01-microm particles by approximately 10%.
  • An enhancement factor improved overall deposition fractions, particularly for particle sizes between 0.1 and 1.0 microm.

Conclusions:

  • The proposed imaging-based subject-specific model provides a more accurate prediction of whole-lung particle deposition.
  • This model can enhance the understanding of therapeutic aerosol delivery and inhalation exposure risks.
  • The validated model offers a valuable tool for personalized respiratory medicine and risk assessment.