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In radiation therapy, regional lung function assessment can help physicians make decision to spare healthy and functional lung tissues while maintaining target coverage. Fluorodeoxyglucose (F-18 FDG) PET/CT is usually acquired once before treatment to evaluate nodal involvement and enhance the tumor region for target definition. The F-18 FDG PET/CT could highlight the FDG-avid tumor regions, however, it does not provide regional lung function information, such as ventilation, perfusion, and tissue elasticity. 4DCT is routinely acquired in CT simulation to assess the tumor motion for respiratory motion management and ITV contouring. 4DCT which bins the whole respiratory cycle into ten phases can be used to model lung motion and evaluate regional lung function. In this study, we proposed a deep learning (DL) based image registration method to derive lung volumetric strain from the deformation vector field (DVF). A total of 20 4DCT datasets were used to train the network and another 5 datasets including both 4DCT and F-18 FDG PET/CT from lung cancer patients were collected to evaluate the performance of the proposed method. The proposed DL-based registration network was trained to predict the DVF to register a pair of 3DCT images. The resultant DVF was used to calculate the volumetric strain for regional lung function evaluation. To assess the accuracy of volumetric strain map in lung function evaluation, the strain map was used to contour regions with low absolute volumetric strain, indicating stiff tissue regions, and compared to the tumor region segmented on the CT images. Dice similarity coefficient (DSC) was reported for 5 testing patients. The average DSC is 0.65±0.07, indicating the accuracy of volumetric strain in regional lung stiffness evaluation. The proposed method has the potential to be used in regional lung function evaluation and lung tissue stiffness-based lung tumor staging.
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