12 research outputs found
GPU-based ultra fast dose calculation using a finite pencil beam model
Online adaptive radiation therapy (ART) is an attractive concept that
promises the ability to deliver an optimal treatment in response to the
inter-fraction variability in patient anatomy. However, it has yet to be
realized due to technical limitations. Fast dose deposit coefficient
calculation is a critical component of the online planning process that is
required for plan optimization of intensity modulated radiation therapy (IMRT).
Computer graphics processing units (GPUs) are well-suited to provide the
requisite fast performance for the data-parallel nature of dose calculation. In
this work, we develop a dose calculation engine based on a finite-size pencil
beam (FSPB) algorithm and a GPU parallel computing framework. The developed
framework can accommodate any FSPB model. We test our implementation on a case
of a water phantom and a case of a prostate cancer patient with varying beamlet
and voxel sizes. All testing scenarios achieved speedup ranging from 200~400
times when using a NVIDIA Tesla C1060 card in comparison with a 2.27GHz Intel
Xeon CPU. The computational time for calculating dose deposition coefficients
for a 9-field prostate IMRT plan with this new framework is less than 1 second.
This indicates that the GPU-based FSPB algorithm is well-suited for online
re-planning for adaptive radiotherapy.Comment: submitted Physics in Medicine and Biolog
Implementation and evaluation of various demons deformable image registration algorithms on GPU
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.Comment: Submitted to Physics in Medicine and Biolog