  CBCTs in image-guided radiotherapy provide crucial anatomy information for
patient setup and plan evaluation. Longitudinal CBCT image registration could
quantify the inter-fractional anatomic changes. The purpose of this study is to
propose an unsupervised deep learning based CBCT-CBCT deformable image
registration. The proposed deformable registration workflow consists of
training and inference stages that share the same feed-forward path through a
spatial transformation-based network (STN). The STN consists of a global
generative adversarial network (GlobalGAN) and a local GAN (LocalGAN) to
predict the coarse- and fine-scale motions, respectively. The network was
trained by minimizing the image similarity loss and the deformable vector field
(DVF) regularization loss without the supervision of ground truth DVFs. During
the inference stage, patches of local DVF were predicted by the trained
LocalGAN and fused to form a whole-image DVF. The local whole-image DVF was
subsequently combined with the GlobalGAN generated DVF to obtain final DVF. The
proposed method was evaluated using 100 fractional CBCTs from 20 abdominal
cancer patients in the experiments and 105 fractional CBCTs from a cohort of 21
different abdominal cancer patients in a holdout test. Qualitatively, the
registration results show great alignment between the deformed CBCT images and
the target CBCT image. Quantitatively, the average target registration error
(TRE) calculated on the fiducial markers and manually identified landmarks was
1.91+-1.11 mm. The average mean absolute error (MAE), normalized cross
correlation (NCC) between the deformed CBCT and target CBCT were 33.42+-7.48
HU, 0.94+-0.04, respectively. This promising registration method could provide
fast and accurate longitudinal CBCT alignment to facilitate inter-fractional
anatomic changes analysis and prediction.
