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Purpose  Ventilation images can be derived from four‐dimensional computed tomography (4DCT) by analyzing the change in  HU  values and deformable vector fields between different respiration phases of computed tomography ( CT ). As deformable image registration ( DIR ) is involved, accuracy of 4 DCT ‐derived ventilation image is sensitive to the choice of  DIR  algorithms. To overcome the uncertainty associated with  DIR , we develop a method based on deep convolutional neural network ( CNN ) to derive ventilation images directly from the 4 DCT  without explicit image registration.    Methods  A total of 82 sets of 4 DCT  and ventilation images from patients with lung cancer were used in this study. In the proposed  CNN  architecture, the  CT  two‐channel input data consist of  CT  at the end of exhale and the end of inhale phases. The first convolutional layer has 32 different kernels of size 5 × 5 × 5, followed by another eight convolutional layers each of which is equipped with an activation layer (Re LU ). The loss function is the mean‐squared‐error ( MSE ) to measure the intensity difference between the predicted and reference ventilation images.    Results  The predicted images were comparable to the label images of the test data. The similarity index, correlation coefficient, and Gamma index passing rate averaged over the tenfold cross validation were 0.880 ± 0.035, 0.874 ± 0.024, and 0.806 ± 0.014, respectively.    Conclusions  The results demonstrate that deep  CNN  can generate ventilation imaging from 4 DCT  without explicit deformable image registration, reducing the associated uncertainty.

Technical Note: Deriving ventilation imaging from 4 DCT by deep convolutional neural network