Implementation and evaluation of various demons deformable image registration algorithms on GPU

TL;DR

This paper implements and evaluates GPU-accelerated demons deformable image registration algorithms for rapid online adaptive radiation therapy, demonstrating significant speed improvements with maintained accuracy.

Contribution

It introduces GPU-based implementations of demons DIR algorithms and compares their accuracy and efficiency, highlighting the original passive force demons as the most effective variant.

Findings

GPU implementation reduces registration time to 7-11 seconds

Original passive force demons outperform variants in accuracy and efficiency

Average 3D error around 1.5 to 1.8 mm

Abstract

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.