Estimating lung ventilation directly from 4D CT Hounsfield unit values. Issue 1 (17th December 2015) - Kipritidis, John; Hofman, Michael S.; Siva, Shankar; Callahan, Jason; Le Roux, Pierre‐Yves; Woodruff, Henry C.; Counter, William B.; Keall, Paul J.
Abstract : Purpose: Computed tomography ventilation imaging (CTVI) aims to visualize air‐volume changes in the lung by quantifying respiratory motion in 4DCT using deformable image registration (DIR). A problem is that DIR‐based CTVI is sensitive both to 4DCT image artifacts and DIR parameters, hindering clinical validation of the technique. To address this, the authors present a streamlined CTVI approach that estimates blood–gas exchange in terms of time‐averaged 4DCT Hounsfield unit (HU) values without relying on DIR. The purpose of this study is to quantify the accuracy of the HU‐based CTVI method using high‐resolution 68 Ga positron emission tomography ("Galligas PET") scans in lung cancer patients. Methods: The authors analyzed Galligas 4D‐PET/CT scans acquired for 25 lung cancer patients at up to three imaging timepoints during lung cancer radiation therapy. For each 4DCT scan, the authors produced three types of CTVIs: (i) the new method ( CTV I HU ¯ ), which takes the 4D time‐averaged product of regional air and tissue densities at each voxel, and compared this to DIR‐based estimates of (ii) breathing‐induced density changes (CTV IDIR‐HU ), and (iii) breathing‐induced volume changes (CTV IDIR‐Jac ) between the exhale/inhale phase images. The authors quantified the accuracy of CTV I HU ¯, CTV IDIR‐HU and CTV IDIR‐Jac versus Galligas PET in terms of voxel‐wise Spearman correlation ( r ) and the separation of mean voxel values between clinically defined defect/nondefect regions. Results: Averaged over 62 scans, CTV I HU ¯ showed better accuracy than CTV IDIR‐HU and CTV IDIR‐Jac in terms of Spearman correlation with Galligas PET, with (mean ± SD) r values of (0.50 ± 0.17), (0.42 ± 0.20), and (0.19 ± 0.23), respectively. A two‐sample Kolmogorov–Smirnov test indicates that CTV I HU ¯ shows statistically significant separation of mean ventilation values between clinical defect/nondefect regions. Qualitatively, CTV I HU ¯ appears concordant with Galligas PET for emphysema related defects, but differences arise in tumor‐obstructed regions (where aeration is overestimated due to motion blur) and for other abnormal morphology (e.g., fluid‐filled or peritumoral lung with HU ≳ − 600) where the assumptions of the HU model may break down. Conclusions: The HU‐based CTVI method can improve voxel‐wise correlations with Galligas PET compared to DIR‐based methods and may be a useful approximation for voxels with HU values in the range (−1000,   − 600). With further clinical verification, HU‐based CTVI could provide a straightforward and reproducible means to estimate lung ventilation using free‐breathing 4DCT.