GPU-based ultra-fast dose calculation using a finite pencil beam model

Gu, Xuejun; Choi, Du Young; Men, Chunhua; Pan, Hubert Y.; Majumdar, Amitava; Jiang, Steve B

doi:10.1088/0031-9155/54/20/017

Cited by 79 publications

(81 citation statements)

References 28 publications

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“…The DDC matrix is a sparse matrix, as each beamlet only has influence on a small fraction of all the voxels. In our system, the DDC matrix is first stored in a coordinate list format (COO) when it is first generated by a finite-size pencil beam dose calculation algorithm 29,30 via our in-house developed dose engine. This format stores three vectors, namely, a vector consisting of all the non-zero entries of the DDC matrix, a row index vector and a column index vector which denotes the voxel indices and beamlet indices for these non-zero entries, respectively.…”

Section: Multi-gpu Data Storagementioning

confidence: 99%

Multi-GPU implementation of a VMAT treatment plan optimization algorithm

et al. 2015

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Purpose: VMAT optimization is a computationally challenging problem due to its large data size, high degrees of freedom, and many hardware constraints. Highperformance graphics processing units (GPUs) have been used to speed up the computations. However, GPU's relatively small memory size cannot handle cases with a large dose-deposition coefficient (DDC) matrix in cases of, e.g., those with a large target size, multiple targets, multiple arcs and/or small beamlet size. The main purpose of this paper is to report an implementation of a column-generation based VMAT algorithm, previously developed in our group, on a multi-GPU platform to solve the memory limitation problem. While the column-generation based VMAT algorithm has been previously developed, the GPU implementation details have not been reported. Hence, another purpose is to present detailed techniques employed for GPU implementation. We also would like to utilize this particular problem as an example problem to study the feasibility of using a multi-GPU platform to solve large-scale problems in medical physics. Methods: The column-generation approach generates VMAT apertures sequentially by solving a pricing problem (PP) and a master problem (MP) iteratively. In our method, the sparse DDC matrix is first stored on CPU in coordinate list format (COO). On the GPU side, this matrix is split into four sub-matrices according to beam angles, which are stored on four GPUs in compressed sparse row (CSR) format. Computation of beamlet price, the first step in PP, is accomplished using multi-GPUs. A fast inter-GPU data transfer scheme is designed using peer-to-peer (P2P) access. The remaining steps of PP and MP problems are implemented on CPU or a single GPU due to their modest problem scale and computational loads. Barzilai and Borwein (BB) algorithm with subspace step scheme is adopted here to solve the MP problem. A head and neck (H&N) cancer case was used to validate our method. We also compare our multi-GPU implementation with three different single GPU implementation strategies: truncating DDC matrix (S1), repeatedly transferring DDC matrix between CPU and GPU (S2), and porting computations involving DDC matrix to CPU (S3), in terms of both plan quality and computational efficiency. Two 2 more H&N patient cases and three prostate cases were also used to demonstrate the advantages of our method. Results: Our multi-GPU implementation can finish the optimization process within ~1 minute for the H&N patient case. S1 leads to an inferior plan quality although its total time was 10 seconds shorter than the multi-GPU implementation due to the reduced matrix size. S2 and S3 yield the same plan quality as the multi-GPU implementation but take ~4 minutes and ~6 minutes, respectively. High computational efficiency was consistently achieved for the other 5 patient cases tested, with VMAT plans of clinically acceptable quality obtained within 23~46 seconds. Conversely, to obtain clinically comparable or acceptable plans for all these 6 VMAT cases that we have tested in t...

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Section: Multi-gpu Data Storagementioning

confidence: 99%

Multi-GPU implementation of a VMAT treatment plan optimization algorithm

et al. 2015

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“…We can obtain 's for a 9-field prostate IMRT clinical case using our in-house dose calculation engine implemented on GPU (Gu et al, 2009) in 1 second.…”

Section: Direct Aperture Optimization Model 30mentioning

confidence: 99%

“…IMRT plan of 5×5 mm 2 beamlet size and 2.5×2.5×2.5 mm 3 voxel size, and we can finish the re-optimization in 2.8 seconds (Men et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

GPU-based ultra-fast direct aperture optimization for online adaptive radiation therapy

2010

Self Cite

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Online adaptive radiation therapy (ART) has great promise to significantly 10 reduce normal tissue toxicity and/or improve tumor control through real-time treatment adaptations based on the current patient anatomy. However, the major technical obstacle for clinical realization of online ART, namely the inability to achieve real-time efficiency in treatment re-planning, has yet to be solved. To overcome this challenge, this paper presents our work on the implementation of 15 an intensity modulated radiation therapy (IMRT) direct aperture optimization (DAO) algorithm on graphics processing unit (GPU) based on our previous work on CPU. We formulate the DAO problem as a large-scale convex programming problem, and use an exact method called column generation approach to deal with its extremely large dimensionality on GPU. Five 9-field 20 prostate and five 5-field head-and-neck IMRT clinical cases with 5×5 mm 2 beamlet size and 2.5×2.5×2.5 mm 3 voxel size were used to evaluate our algorithm on GPU. It takes only 0.7~2.5 seconds for our implementation to generate optimal treatment plans using 50 MLC apertures on an NVIDIA Tesla C1060 GPU card. Our work has therefore solved a major problem in developing 25 ultra-fast (re-)planning technologies for online ART. C Men et al.2

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“…Meanwhile, general purpose graphic processing units (GPUs) have offered us a promising prospect of increasing efficiencies of heavy duty tasks in radiotherapy, such as CBCT FDK reconstruction [28][29][30][31][32] , deformable image registration 30, 33-90 34 , dose calculation [35][36][37][38] , and treatment plan optimization 39 . Taking advantages of these developments, the computation efficiency of TV-based CBCT reconstruction is expected to be enhanced considerably.…”

Section: Introductionmentioning

confidence: 99%

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

Jia

Lou

et al. 2010

International Journal of Radiation Oncology*Biology*Physics

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Purpose: Cone-beam CT (CBCT) has been widely used in image guided radiation therapy (IGRT) to acquire updated volumetric anatomical information 15 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. It is the goal of this work to develop a fast GPU-based 20 algorithm to reconstruct high quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose. Methods: 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 25 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. Results: It is found that 40 x-ray projections are sufficient to reconstruct CBCT 30 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 35 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. a) Electronic mail: sbjiang@ucsd.edu 2 X. Jia et al. Conclusions:The developed GPU-based CBCT reconstruction algorithm is capable of lowering imaging dose considerably. The high computational 40 efficiency makes this iterative CBCT reconstruction approach feasible in real clinical environments.

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GPU-based ultra-fast dose calculation using a finite pencil beam model

Cited by 79 publications

References 28 publications

Multi-GPU implementation of a VMAT treatment plan optimization algorithm

Multi-GPU implementation of a VMAT treatment plan optimization algorithm

GPU-based ultra-fast direct aperture optimization for online adaptive radiation therapy

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

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