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Updated: Dec 29, 2025
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
Published on: April 12, 2024
Quantitative lobar pulmonary perfusion assessment on dual-energy CT pulmonary angiography: applications in pulmonary
Ramandeep Singh1,2, Ryan Zipan Nie3,4, Fatemeh Homayounieh3,4
1Department of Thoracic Imaging, Massachusetts General Hospital, 75 Blossom Court, Boston, MA, USA. rsingh17@mgh.harvard.edu.
Purpose:
To assess quantitative lobar pulmonary perfusion on DECT-PA in patients with and without pulmonary embolism (PE).
Materials And Methods:
Our retrospective study included 88 adult patients (mean age 56 ± 19 years; 38 men, 50 women) who underwent DECT-PA (40 PE present; 48 PE absent) on a 384-slice, third-generation, dual-source CT. All DECT-PA examinations were reviewed to record the presence and location of occlusive and non-occlusive PE. Transverse thin (1 mm) DECT images (80/150 kV) were de-identified and exported offline for processing on a stand-alone deep learning-based prototype for automatic lung lobe segmentation and to obtain the mean attenuation numbers (in HU), contrast amount (in mg), and normalized iodine concentration per lung and lobe. The zonal volumes and mean enhancement were obtained from the Lung Analysis™ application. Data were analyzed with receiver operating characteristics (ROC) and analysis of variance (ANOVA).
Results:
The automatic lung lobe segmentation was accurate in all DECT-PA (88; 100%). Both lobar and zonal perfusions were significantly lower in patients with PE compared with those without PE (p < 0.0001). The mean attenuation numbers, contrast amounts, and normalized iodine concentrations in different lobes were significantly lower in the patients with PE compared with those in the patients without PE (AUC 0.70-0.78; p < 0.0001). Patients with occlusive PE had significantly lower quantitative perfusion compared with those without occlusive PE (p < 0.0001).
Conclusion:
The deep learning-based prototype enables accurate lung lobe segmentation and assessment of quantitative lobar perfusion from DECT-PA.
Key Points:
• Deep learning-based prototype enables accurate lung lobe segmentation and assessment of quantitative lobar perfusion from DECT-PA. • Quantitative lobar perfusion parameters (AUC up to 0.78) have a higher predicting presence of PE on DECT-PA examinations compared with the zonal perfusion parameters (AUC up to 0.72). • The lobar-normalized iodine concentration has the highest AUC for both presence of PE and for differentiating occlusive and non-occlusive PE.
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