  Purpose: Functional imaging is emerging as an important tool for lung cancer
treatment planning and evaluation. Compared with traditional methods such as
nuclear medicine ventilation-perfusion (VQ), positron emission tomography
(PET), single photon emission computer tomography (SPECT), or magnetic
resonance imaging (MRI), which use contrast agents to form 2D or 3D functional
images, ventilation imaging obtained from 4DCT lung images is convenient and
cost-effective because of its availability during radiation treatment planning.
Current methods of obtaining ventilation images from 4DCT lung images involve
deformable image registration (DIR) and a density (HU) change-based algorithm
(DIR/HU); therefore the resulting ventilation images are sensitive to the
selection of DIR algorithms. Methods: We propose a deep convolutional neural
network (CNN)-based method to derive the ventilation images from 4DCT directly
without explicit DIR, thereby improving consistency and accuracy of ventilation
images. A total of 82 sets of 4DCT and ventilation images from patients with
lung cancer were studied using this method. Results: The predicted images were
comparable to the label images of the test data. The similarity index and
correlation coefficient averaged over the ten-fold cross validation were
0.883+-0.034 and 0.878+-0.028, respectively. Conclusions: The results
demonstrate that deep CNN can generate ventilation imaging from 4DCT without
explicit deformable image registration, reducing the associated uncertainty.
