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Combined Assessment of Pulmonary Ventilation and Perfusion with Single-Energy Computed Tomography and Image Processing: Proof-Of-Principle in a Canine Model

Y Fujita

Y Fujita1*, M Kent2 , E Wisner2 , L Johnson2 , J Stern2 , L Qi2 , J Boone2 , T Yamamoto2 , (1) Tokai University School of Medicine, Kanagawa, Japan, (2) University of California Davis, Davis, CA


WE-G-201-8 (Wednesday, August 2, 2017) 4:30 PM - 6:00 PM Room: 201

Purpose: To establish proof-of-principle for the combined assessment of pulmonary ventilation and perfusion with emerging modalities based on single-energy CT and image processing.

Methods: Breath-hold CT scans were acquired at end-expiration (before intravenous injection of iodinated contrast agents) and end-inspiration (before and after contrast injection) for 17 canines (8 normal lung subjects and 9 diseased lung subjects). Ventilation images were calculated with deformable image registration for spatial mapping of the end-expiratory and end-inspiratory CT images and analysis for regional volume change as a surrogate for ventilation. Perfusion images were calculated based on subtraction of the end-inspiratory precontrast CT from the deformably registered end-inspiratory postcontrast CT, yielding a map of regional Hounsfield unit enhancement as a surrogate for perfusion. We assessed regional correlations between ventilation and perfusion to compare ventilation-perfusion matching between the normal and diseased lung groups. We also assessed spatial heterogeneity and linear regression slopes for the relationship between the ventral-to-dorsal distance and ventilation or perfusion.

Results: Pearson’s and Spearman’s correlation coefficients between ventilation and perfusion were 0.63 (p<0.001) and 0.60 (p <0.001) for normal lung subjects, and 0.49 (p<0.001) and 0.37 (p<0.001) for diseased lung subjects, suggesting better ventilation-perfusion matching in normal lungs. Median coefficient of variation was lower for normal subjects (ventilation: 0.81, perfusion: 0.79) than for diseased subjects (ventilation: 1.02, perfusion: 0.98). Linear regression slopes of gravitationally directed gradient for normal subjects (ventilation: -0.26 with p<0.001, perfusion: -0.18 with p=0.004) were greater than for diseased subjects (ventilation: -0.20 with p=0.001, perfusion: -0.11 with p=0.04).

Conclusion: This canine study demonstrated better ventilation-perfusion matching, higher spatial homogeneity of ventilation and perfusion, and stronger gravitationally directed gradients of ventilation and perfusion for normal lung subjects than for diseased lung subjects, providing proof-of-principle for the combined assessment of pulmonary ventilation and perfusion with single-energy CT and image processing.

Funding Support, Disclosures, and Conflict of Interest: This study was supported by Philips Healthcare/Radiological Society of North America (RSNA) Research Seed Grant No. RSD1458.

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