Mapping lung ventilation through stress maps derived from biomechanical models of the lung.

Abstract

Most existing computed tomography (CT)-ventilation imaging techniques are based on deformable image registration (DIR) of different respiratory phases of a four-dimensonal CT (4DCT) scan of the lung, followed by the quantification of local breathing-induced changes in Hounsfield Units (HU) or volume. To date, only moderate correlations have been reported between these CT-ventilation metrics and standard ventilation imaging modalities for adaptive lung radiation therapy. This study evaluates the use of stress maps derived from biomechanical model-based DIR as an alternative CT-ventilation metric. Six patients treated for lung cancer with conventional radiation therapy were retrospectively analyzed. For each patient, a 4DCT scan and Tc-99m SPECT-V image acquired for treatment planning were collected. Biomechanical model-based DIR was applied between the inhale and exhale phase of the 4DCT scans and stress maps were calculated. The voxel-wise correlation between the reference SPECT-V image and map of the maximum principal stress was measured with a Spearman correlation coefficient. The overlap between high (above the 75th percentile) and low (below the 25th percentile) functioning volumes extracted from the reference SPECT-V and the stress maps was measured with Dice similarity coefficients (DSC). The results were compared to those obtained when using two classical CT-ventilation metrics: the change in HU and Jacobian determinant. The mean Spearman correlation coefficients were: 0.37±18 and 0.39±13 and 0.59±0.13 considering the changes in HU, Jacobian and maximum principal stress, respectively. The corresponding mean DSC coefficients were 0.38±0.09, 0.37±0.07 and 0.52±0.07 for the high ventilation function volumes and 0.48±0.13, 0.44±0.09 and 0.52±0.07 for the low ventilation function volumes. For presenting a significantly stronger and more consistent correlation with standard SPECT-V images than previously proposed CT-ventilation metrics, stress maps derived with the proposed method appear to be a promising tool for incorporation into functional lung avoidance strategies.