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Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
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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.
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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
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Generating Lung Ventilation Images with Virtual Non-contrast Images from Dual-Energy CT Scans Using Multi-task

Sangyoon Lee1, Changyong Choi2,3, Jongjun Won2,4

  • 1Department of Radiation Oncology, University of Minnesota Medical School, 420 Delaware Street SE, Minneapolis, MN, 55455, USA.

Journal of Imaging Informatics in Medicine
|January 8, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning model generates lung ventilation images from CT scans, offering a promising alternative to xenon-enhanced imaging for obstructive pulmonary diseases like COPD and ACOS.

Keywords:
Deep learningDual-energy CTGenerative adversarial networksLung ventilationPulmonary disease

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pulmonary Medicine

Background:

  • Xenon-enhanced dual-energy CT (Xe-DECT) provides lung ventilation insights for obstructive diseases.
  • Clinical application of Xe-DECT is limited by technical and logistical challenges.

Purpose of the Study:

  • To develop a deep learning model for generating lung ventilation images (DL-Vent) from virtual non-contrast (VNC) CT images.
  • To assess the accuracy and clinical utility of DL-Vent compared to Xe-DECT.

Main Methods:

  • A multi-task conditional generative adversarial network (GAN) was trained on 269 scans from patients with COPD or asthma-COPD overlap syndrome (ACOS).
  • The model simultaneously predicted ventilation images and emphysema masks from paired inspiratory and expiratory VNC images.
  • DL-Vent images were compared against Xe-DECT ventilation images (Xe-Vent) using similarity metrics and correlation with pulmonary function tests.

Main Results:

  • DL-Vent showed high similarity to Xe-Vent (Dice scores 0.56 for defects, 0.88 for regions).
  • Ventilation defect percentages (VDP) from DL-Vent and Xe-Vent were highly correlated (rs=0.82) and similarly correlated with FEV1.
  • Radiologists rated DL-Vent images as fair to good and found they could differentiate defect patterns between COPD and ACOS.

Conclusions:

  • The developed deep learning model offers a viable, non-invasive method for functional lung imaging.
  • DL-Vent provides a promising alternative to Xe-DECT for evaluating obstructive pulmonary diseases.
  • This AI-driven approach may overcome current limitations of xenon-based ventilation imaging.