arXiv subjects

Xuejun Gu

Publications and source records attributed to Xuejun Gu.

28 records · Page 2Linked to original sources

arXiv2011

GPU-based fast Monte Carlo simulation for radiotherapy dose calculation

Monte Carlo (MC) simulation is commonly considered to be the most accurate dose calculation method in radiotherapy. However, its efficiency still requires improvement for many routine clinical applications. In this paper, we present our recent progress towards the development a GPU-based MC dose calculation package, gDPM v2.0. It utilizes the parallel computation ability of a GPU to achieve high efficiency, while maintaining the same particle transport physics as in the original DPM code and hence the same level of simulation accuracy. In GPU computing, divergence of execution paths between threads can considerably reduce the efficiency. Since photons and electrons undergo different physics and hence attain different execution paths, we use a simulation scheme where photon transport and electron transport are separated to partially relieve the thread divergence issue. High performance random number generator and hardware linear interpolation are also utilized. We have also developed various components to handle fluence map and linac geometry, so that gDPM can be used to compute dose distributions for realistic IMRT or VMAT treatment plans. Our gDPM package is tested for its accuracy and efficiency in both phantoms and realistic patient cases. In all cases, the average relative uncertainties are less than 1%. A statistical t-test is performed and the dose difference between the CPU and the GPU results is found not statistically significant in over 96% of the high dose region and over 97% of the entire region. Speed up factors of 69.1 ~ 87.2 have been observed using an NVIDIA Tesla C2050 GPU card against a 2.27GHz Intel Xeon CPU processor. For realistic IMRT and VMAT plans, MC dose calculation can be completed with less than 1% standard deviation in 36.1~39.6 sec using gDPM.

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arXiv2011

A GPU-based finite-size pencil beam algorithm with 3D-density correction for radiotherapy dose calculation

Targeting at the development of an accurate and efficient dose calculation engine for online adaptive radiotherapy, we have implemented a finite size pencil beam (FSPB) algorithm with a 3D-density correction method on GPU. This new GPU-based dose engine is built on our previously published ultrafast FSPB computational framework [Gu et al. Phys. Med. Biol. 54 6287-97, 2009]. Dosimetric evaluations against Monte Carlo dose calculations are conducted on 10 IMRT treatment plans (5 head-and-neck cases and 5 lung cases). For all cases, there is improvement with the 3D-density correction over the conventional FSPB algorithm and for most cases the improvement is significant. Regarding the efficiency, because of the appropriate arrangement of memory access and the usage of GPU intrinsic functions, the dose calculation for an IMRT plan can be accomplished well within 1 second (except for one case) with this new GPU-based FSPB algorithm. Compared to the previous GPU-based FSPB algorithm without 3D-density correction, this new algorithm, though slightly sacrificing the computational efficiency (~5-15% lower), has significantly improved the dose calculation accuracy, making it more suitable for online IMRT replanning.

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arXiv2011

3D tumor localization through real-time volumetric x-ray imaging for lung cancer radiotherapy

Recently we have developed an algorithm for reconstructing volumetric images and extracting 3D tumor motion information from a single x-ray projection. We have demonstrated its feasibility using a digital respiratory phantom with regular breathing patterns. In this work, we present a detailed description and a comprehensive evaluation of the improved algorithm. The algorithm was improved by incorporating respiratory motion prediction. The accuracy and efficiency were then evaluated on 1) a digital respiratory phantom, 2) a physical respiratory phantom, and 3) five lung cancer patients. These evaluation cases include both regular and irregular breathing patterns that are different from the training dataset. For the digital respiratory phantom with regular and irregular breathing, the average 3D tumor localization error is less than 1 mm. On an NVIDIA Tesla C1060 GPU card, the average computation time for 3D tumor localization from each projection ranges between 0.19 and 0.26 seconds, for both regular and irregular breathing, which is about a 10% improvement over previously reported results. For the physical respiratory phantom, an average tumor localization error below 1 mm was achieved with an average computation time of 0.13 and 0.16 seconds on the same GPU card, for regular and irregular breathing, respectively. For the five lung cancer patients, the average tumor localization error is below 2 mm in both the axial and tangential directions. The average computation time on the same GPU card ranges between 0.26 and 0.34 seconds.

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arXiv2010

GPU-based fast gamma index calcuation

The gamma-index dose comparison tool has been widely used to compare dose distributions in cancer radiotherapy. The accurate calculation of gamma-index requires an exhaustive search of the closest Euclidean distance in the high-resolution dose-distance space. This is a computational intensive task when dealing with 3D dose distributions. In this work, we combine a geometric method with a radial pre-sorting technique , and implement them on computer graphics processing units (GPUs). The developed GPU-based gamma-index computational tool is evaluated on eight pairs of IMRT dose distributions. The GPU implementation achieved 20x~30x speedup factor compared to CPU implementation and gamma-index calculations can be finished within a few seconds for all 3D testing cases. We further investigated the effect of various factors on both CPU and GPU computation time. The strategy of pre-sorting voxels based on their dose difference values speed up the GPU calculation by about 2-4 times. For n-dimensional dose distributions, gamma-index calculation time on CPU is proportional to the summation of gamma^n over all voxels, while that on GPU is effected by gamma^n distributions and is approximately proportional to the gamma^n summation over all voxels. We found increasing dose distributions resolution leads to quadratic increase of computation time on CPU, while less-than-quadratic increase on GPU. The values of dose difference (DD) and distance-to-agreement (DTA) criteria also have their impact on gamma-index calculation time.

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arXiv2010

GPU-based Fast Low-dose Cone Beam CT Reconstruction via Total Variation

Cone-beam CT (CBCT) has been widely used in image guided radiation therapy (IGRT) to acquire updated volumetric anatomical information before treatment fractions for accurate patient alignment purpose. However, the excessive x-ray imaging dose from serial CBCT scans raises a clinical concern in most IGRT procedures. The excessive imaging dose can be effectively reduced by reducing the number of x-ray projections and/or lowering mAs levels in a CBCT scan. The goal of this work is to develop a fast GPU-based algorithm to reconstruct high quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose. The CBCT is reconstructed by minimizing an energy functional consisting of a data fidelity term and a total variation regularization term. We developed a GPU-friendly version of the forward-backward splitting algorithm to solve this model. A multi-grid technique is also employed. We test our CBCT reconstruction algorithm on a digital NCAT phantom and a head-and-neck patient case. The performance under low mAs is also validated using a physical Catphan phantom and a head-and-neck Rando phantom. It is found that 40 x-ray projections are sufficient to reconstruct CBCT images with satisfactory quality for IGRT patient alignment purpose. Phantom experiments indicated that CBCT images can be successfully reconstructed with our algorithm under as low as 0.1 mAs/projection level. Comparing with currently widely used full-fan head-and-neck scanning protocol of about 360 projections with 0.4 mAs/projection, it is estimated that an overall 36 times dose reduction has been achieved with our algorithm. Moreover, the reconstruction time is about 130 sec on an NVIDIA Tesla C1060 GPU card, which is estimated ~100 times faster than similar iterative reconstruction approaches.

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arXiv2010

Real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy

Purpose: To develop an algorithm for real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy. Methods: Given a set of volumetric images of a patient at N breathing phases as the training data, we perform deformable image registration between a reference phase and the other N-1 phases, resulting in N-1 deformation vector fields (DVFs). These DVFs can be represented efficiently by a few eigenvectors and coefficients obtained from principal component analysis (PCA). By varying the PCA coefficients, we can generate new DVFs, which, when applied on the reference image, lead to new volumetric images. We then can reconstruct a volumetric image from a single projection image by optimizing the PCA coefficients such that its computed projection matches the measured one. The 3D location of the tumor can be derived by applying the inverted DVF on its position in the reference image. Our algorithm was implemented on graphics processing units (GPUs) to achieve real-time efficiency. We generated the training data using a realistic and dynamic mathematical phantom with 10 breathing phases. The testing data were 360 cone beam projections corresponding to one gantry rotation, simulated using the same phantom with a 50% increase in breathing amplitude. Results: The average relative image intensity error of the reconstructed volumetric images is 6.9% +/- 2.4%. The average 3D tumor localization error is 0.8 mm +/- 0.5 mm. On an NVIDIA Tesla C1060 GPU card, the average computation time for reconstructing a volumetric image from each projection is 0.24 seconds (range: 0.17 and 0.35 seconds). Conclusions: We have shown the feasibility of reconstructing volumetric images and localizing tumor positions in 3D in near real-time from a single x-ray image.

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arXiv2010

Development of a GPU-based Monte Carlo dose calculation code for coupled electron-photon transport

Monte Carlo simulation is the most accurate method for absorbed dose calculations in radiotherapy. Its efficiency still requires improvement for routine clinical applications, especially for online adaptive radiotherapy. In this paper, we report our recent development on a GPU-based Monte Carlo dose calculation code for coupled electron-photon transport. We have implemented the Dose Planning Method (DPM) Monte Carlo dose calculation package (Sempau et al, Phys. Med. Biol., 45(2000)2263-2291) on GPU architecture under CUDA platform. The implementation has been tested with respect to the original sequential DPM code on CPU in phantoms with water-lung-water or water-bone-water slab geometry. A 20 MeV mono-energetic electron point source or a 6 MV photon point source is used in our validation. The results demonstrate adequate accuracy of our GPU implementation for both electron and photon beams in radiotherapy energy range. Speed up factors of about 5.0 ~ 6.6 times have been observed, using an NVIDIA Tesla C1060 GPU card against a 2.27GHz Intel Xeon CPU processor.

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arXiv2009

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.

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arXiv2009

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.

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arXiv2009

GPU-based ultra fast IMRT plan optimization

The widespread adoption of on-board volumetric imaging in cancer radiotherapy has stimulated research efforts to develop online adaptive radiotherapy techniques to handle the inter-fraction variation of the patient's geometry. Such efforts face major technical challenges to perform treatment planning in real-time. To overcome this challenge, we are developing a supercomputing online re-planning environment (SCORE) at the University of California San Diego (UCSD). As part of the SCORE project, this paper presents our work on the implementation of an intensity modulated radiation therapy (IMRT) optimization algorithm on graphics processing units (GPUs). We adopt a penalty-based quadratic optimization model, which is solved by using a gradient projection method with Armijo's line search rule. Our optimization algorithm has been implemented in CUDA for parallel GPU computing as well as in C for serial CPU computing for comparison purpose. A prostate IMRT case with various beamlet and voxel sizes was used to evaluate our implementation. On an NVIDIA Tesla C1060 GPU card, we have achieved speedup factors of 20-40 without losing accuracy, compared to the results from an Intel Xeon 2.27 GHz CPU. For a specific 9-field prostate IMRT case with 5x5 mm^2 beamlet size and 2.5x2.5x2.5 mm^3 voxel size, our GPU implementation takes only 2.8 seconds to generate an optimal IMRT plan. Our work has therefore solved a major problem in developing online re-planning technologies for adaptive radiotherapy.

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