• Francesca Pennati, Caterina Salito, Guido Baroni, Jason Woods, and Andrea Aliverti.
• Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, P.zza L. da Vinci, 32, 20133 Milano, Italy.
• Acad Radiol. 2014 Oct 1;21(10):1268-75.

Rationale And ObjectivesThe assessment of regional ventilation is of critical importance when investigating lung function during disease progression and planning of pulmonary interventions. Recently, different computed tomography (CT)-based parameters have been proposed as surrogates of lung ventilation. The aim of the present study was to compare these parameters, namely variations of density (ΔHU), specific volume (sVol), and specific gas volume (ΔSVg) between different lung volumes, in relation to their topographic distribution within the lung.Materials And MethodsTen healthy volunteers were scanned via high-resolution CT at residual volume (RV) and total lung capacity (TLC); ΔHU, sVol, and ΔSVg were mapped voxel by voxel after registering TLC onto RV. Variations of the three parameters along the vertical and horizontal directions were analyzed.ResultsAlong the vertical direction (from ventral to dorsal regions), a strong dependence on gravity was found in ΔHU and sVol, with greater values in the dorsal regions of the lung (P < .001), whereas ΔSVg was more homogeneously distributed within the lung. Conversely, along the caudocranial direction (from lung bases to apexes) where no gravitational gradient is present, the three parameters behaved similarly, with lower values at the apices.ConclusionsΔHU, sVol, and ΔSVg behave differently along the gravity direction. As the greater amount of air delivered to the dependent portion of the lung supplies a larger number of alveoli, the amount of gas delivered to alveoli compared to the mass of tissue is not gravity dependent. The minimization of gravity dependence in the distribution of ventilation when using ΔSVg suggests that this parameter is more reliable to discriminate healthy from pathologic regions.Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

27 keywords...

hide…