Estimating lung ventilation directly from 4D CT Hounsfield unit values

Authors


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 68Ga 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 IHU¯), 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 IHU¯, 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 IHU¯ 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 IHU¯ shows statistically significant separation of mean ventilation values between clinical defect/nondefect regions. Qualitatively, CTV IHU¯ 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.

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