Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Computed Tomography

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A Comparative Study of Radiation Exposure in Conventional and Robotic Bronchoscopy.

Chest·2026

Same author

Community-based tuberculosis screening with computer-aided detection technology alone and in combination with point-of-care C-reactive protein testing: a paired screen-positive trial.

The Lancet. Infectious diseases·2026

Same author

Assessment of modifications to a blind-sweep ultrasound protocol for improved lower-uterus imaging by novice operators.

Scientific reports·2026

Same author

Benchmarking of AI and Radiologists for Indeterminate Lung Nodule Malignancy Risk Estimation on Screening CT: The LUNA25 Challenge.

Radiology. Artificial intelligence·2026

Same author

Pulsed Electric Field Ablation Reprograms Tumor Immunity and Stimulates Germinal Center Formation in Tertiary Lymphoid Structures in Patients with Non-Small Cell Lung Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026

Same author

Robotic <i>versus</i> cone-beam computed tomography navigation bronchoscopy: propensity-matched analysis of diagnostic yield.

ERJ open research·2026

Same journal

Correction to "On the shape of the radiation survival curve in tumor spheroids: The role of oxygen heterogeneity".

Medical physics·2026

Same journal

Multi-view constrained semi-supervised vertebra detection for 3D ultrasound spine volume.

Medical physics·2026

Same journal

Accuracy of quantitative <sup>177</sup>Lu SPECT/CT imaging: A systematic review.

Medical physics·2026

Same journal

Physics-constrained dual-domain network for CBCT reconstruction from orthogonal X-rays in gynecologic radiotherapy.

Medical physics·2026

Same journal

Decomposition-based harmonization for quantitative PET imaging across scanners and radiotracers.

Medical physics·2026

Same journal

Development and evaluation of an in vivo dose-based monitoring system for electron FLASH radiation therapy.

Medical physics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 4, 2025

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

Estimating lung function from computed tomography at the patient and lobe level using machine learning.

Luuk H Boulogne¹, Jean-Paul Charbonnier², Colin Jacobs¹

¹Radboud University Medical Center, Nijmegen, The Netherlands.

Medical Physics

|February 8, 2024

Summary

This summary is machine-generated.

This study introduces I3Dr, a deep learning model that estimates pulmonary function test (PFT) results from CT scans and determines individual lung lobe contributions. This advances CT applications in diagnosing and managing lung diseases.

Keywords:

computed tomography convolutional neural network pulmonary function test weakly supervised learning

More Related Videos

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Related Experiment Videos

Last Updated: Jul 4, 2025

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Pulmonary Medicine

Background:

Computed Tomography (CT) can aid in screening, diagnosis, and staging of restrictive pulmonary diseases.
Estimating lung function per lobe from CT is crucial for surgical risk assessment and lung volume reduction procedures.

Purpose of the Study:

To automatically estimate Pulmonary Function Test (PFT) results from CT scans.
To disentangle the individual contribution of pulmonary lobes to a patient's lung function.

Main Methods:

Proposed I3Dr, a deep learning architecture for estimating global image measures and individual part contributions.
Applied I3Dr to CT scans, utilizing lobe-level and patient-level models trained with patient lung function data.
Trained and evaluated I3Dr on a large dataset of 8,433 CT volumes for training, 1,775 for validation, and 1,873 for testing.

Main Results:

Demonstrated model viability by showing implicit learning of individual digit values from image sums.
Successfully estimated lobe-level quantities like COVID-19 severity, pulmonary volume (PV), and functional pulmonary volume (FPV) from CT.
Achieved mean absolute errors of 0.377 L for FEV1, 0.297 L for FVC, and 2.800 mL/min/mm Hg for DLCO estimates.

Conclusions:

I3Dr effectively estimates global image measures and individual component contributions.
Offers a promising method for PFT estimation from CT and lobe-specific lung function analysis.
Potential to enhance CT's role in diagnosing and managing restrictive lung diseases and in surgical planning.