  Online adaptive radiation therapy (ART) promises the ability to deliver an
optimal treatment in response to daily patient anatomic variation. A major
technical barrier for the clinical implementation of online ART is the
requirement of rapid image segmentation. Deformable image registration (DIR)
has been used as an automated segmentation method to transfer tumor/organ
contours from the planning image to daily images. However, the current
computational time of DIR is insufficient for online ART. In this work, this
issue is addressed by using computer graphics processing units (GPUs). A
grey-scale based DIR algorithm called demons and five of its variants were
implemented on GPUs using the Compute Unified Device Architecture (CUDA)
programming environment. The spatial accuracy of these algorithms was evaluated
over five sets of pulmonary 4DCT images with an average size of 256x256x100 and
more than 1,100 expert-determined landmark point pairs each. For all the
testing scenarios presented in this paper, the GPU-based DIR computation
required around 7 to 11 seconds to yield an average 3D error ranging from 1.5
to 1.8 mm. It is interesting to find out that the original passive force demons
algorithms outperform subsequently proposed variants based on the combination
of accuracy, efficiency, and ease of implementation.
