Modeling parameterized geometry in GPU-based Monte Carlo particle transport simulation for radiotherapy

UMC-PET: a fast and flexible Monte Carlo PET simulator

Objective.The GPU-based Ultra-fast Monte Carlo positron emission tomography simulator (UMC-PET) incorporates the physics of the emission, transport and detection of radiation in PET scanners. It includes positron range, non-colinearity, scatter and attenuation, as well as detector response. The objective of this work is to present and validate UMC-PET as a a multi-purpose, accurate, fast and flexible PET simulator.Approach.We compared UMC-PET against PeneloPET, a well-validated MC PET simulator, both in preclinical and clinical scenarios. Different phantoms for scatter fraction (SF) assessment following NEMA protocols were simulated in a 6R-SuperArgus and a Biograph mMR scanner, comparing energy histograms, NEMA SF, and sensitivity for different energy windows. A comparison with real data reported in the literature on the Biograph scanner is also shown.Main results.NEMA SF and sensitivity estimated by UMC-PET where within few percent of PeneloPET predictions. The discrepancies can be attributed to small differences in the physics modeling. Running in a 11 GB GeForce RTX 2080 Ti GPU, UMC-PET is ∼1500 to ∼2000 times faster than PeneloPET executing in a single core Intel(R) Xeon(R) CPU W-2155 @ 3.30 GHz.Significance.UMC-PET employs a voxelized scheme for the scanner, patient adjacent objects (such as shieldings or the patient bed), and the activity distribution. This makes UMC-PET extremely flexible. Its high simulation speed allows applications such as MC scatter correction, faster SRM estimation for complex scanners, or even MC iterative image reconstruction.

Carbon-11 and Carbon-12 beam range verifications through prompt gamma and annihilation gamma measurements: Monte Carlo simulations

Range uncertainty remains a big concern in particle therapy, as it may cause target dose degradation and normal tissue overdosing. Positron emission tomography (PET) and prompt gamma imaging (PGI) are two promising modalities for range verification. However, the relatively long acquisition time of PET and the relatively low yield of PGI pose challenges for real-time range verification. In this paper, we explore using the primary Carbon-11 (C-11) ion beams to enhance the gamma yield compared to the primary C-12 ion beams to improve PET and PGI by using Monte Carlo simulations of water and PMMA phantoms at four incident energies (95, 200, 300, and 430 MeV u-1). Prompt gammas (PGs) and annihilation gammas (AGs) were recorded for post-processing to mimic PGI and PET imaging, respectively. We used both time-of-flight (TOF) and energy selections for PGI, which boosted the ratio of PGs to background neutrons to 2.44, up from 0.87 without the selections. At the lowest incident energy (100 MeVu-1), PG yield from C-11 was 0.82 times of that from C-12, while AG yield from C-11 was 6 ∼ 11 folds higher than from C-12 in PMMA. At higher energies, PG differences between C-11 and C-12 were much smaller, while AG yield from C-11 was 30%∼90% higher than from C-12 using minute-acquisition. With minute-acquisition, the AG depth distribution of C-11 showed a sharp peak coincident with the Bragg peak due to the decay of the primary C-11 ions, but that of C-12 had no such one. The high AG yield and distinct peaks could lead to more precise range verification of C-11 than C-12. These results demonstrate that using C-11 ion beams for potentially combined PGI and PET has great potential to improve online single-spot range verification accuracy and precision.

gPET: a GPU-based, accurate and efficient Monte Carlo simulation tool for PET

Monte Carlo (MC) simulation method plays an essential role in the refinement and development of positron emission tomography (PET) systems. However, most existing MC simulation packages suffer from long execution time for practical PET simulations. To fully address this issue, we developed and validated gPET, a graphics processing unit (GPU)-based MC simulation tool for PET. gPET was built on the NVidia CUDA platform. The simulation process was modularized into three functional parts and carried out by the GPU parallel threads: (1) source management, including positron decay, transport and annihilation; (2) gamma transport inside the phantom; and (3) signal detection and processing inside the detector. A hybrid of voxelized (for patient phantoms) and parametrized (for detectors) geometries were employed to sufficiently support particle navigations. Multiple inputs and outputs were available. Hence, a user can flexibly examine different aspects of a PET simulation. We evaluated the performance of gPET in three test cases with benchmark work from GATE8.0, in terms of the testing of the functional modules, the physics models used for gamma transport inside the detector, and the geometric configuration of an irregularly shaped PET detector. Both accuracy and efficiency were quantified. In all test cases, the differences between gPET and GATE for the coincidences with respect to the energy and crystal index distributions are below 3.18% and 2.54%, respectively. The speedup factor is 500 for gPET on a single Titan Xp GPU (1.58 GHz) over GATE8.0 on a single core of Intel i7-6850K CPU (3.6 GHz) for all test cases. In summary, gPET is an accurate and efficient MC simulation tool for PET.

Accuracy and efficiency of graphics processing unit (GPU) based Acuros XB dose calculation within the Varian Eclipse treatment planning system

To evaluate, in terms of dosimetric accuracy and calculation efficiency, the implementation of a graphic processing unit (GPU)-based Acuros XB dose calculation engine within version 15.5 of the Varian Eclipse treatment planning system. Initial phantom based calculations and a range of 101 clinical cases were analyzed on a dedicated test system. Dosimetric differences, based on dose-volume histrogram parameters and plan comparison, were compared between central processing unit (CPU) and GPU based calculation. Calculation times were also compared between CPU and GPU, as well as PLAN and FIELD modes. No dosimetric differences were found between CPU and GPU. CPU based calculations ranged from 25 to 533 seconds per plan, reducing to 13 to 70 seconds for GPU. GPU was 4.4 times more efficient than CPU. FIELD mode was up to 1.3 times more efficient than PLAN mode. For the clinical cases and version of Eclipse used, no dosimetric differences were found between CPU and GPU. Based on this, GPU architecture has been safely implemented and is ready for clinical use. GPU based calculation times were superior to CPU, being on average, 4.4 times faster.

Multi-GPU configuration of 4D intensity modulated radiation therapy inverse planning using global optimization

We report on the design, implementation and characterization of a multi-graphic processing unit (GPU) computational platform for higher-order optimization in radiotherapy treatment planning. In collaboration with a commercial vendor (Varian Medical Systems, Palo Alto, CA), a research prototype GPU-enabled Eclipse (V13.6) workstation was configured. The hardware consisted of dual 8-core Xeon processors, 256 GB RAM and four NVIDIA Tesla K80 general purpose GPUs. We demonstrate the utility of this platform for large radiotherapy optimization problems through the development and characterization of a parallelized particle swarm optimization (PSO) four dimensional (4D) intensity modulated radiation therapy (IMRT) technique. The PSO engine was coupled to the Eclipse treatment planning system via a vendor-provided scripting interface. Specific challenges addressed in this implementation were (i) data management and (ii) non-uniform memory access (NUMA). For the former, we alternated between parameters over which the computation process was parallelized. For the latter, we reduced the amount of data required to be transferred over the NUMA bridge. The datasets examined in this study were approximately 300 GB in size, including 4D computed tomography images, anatomical structure contours and dose deposition matrices. For evaluation, we created a 4D-IMRT treatment plan for one lung cancer patient and analyzed computation speed while varying several parameters (number of respiratory phases, GPUs, PSO particles, and data matrix sizes). The optimized 4D-IMRT plan enhanced sparing of organs at risk by an average reduction of [Formula: see text] in maximum dose, compared to the clinical optimized IMRT plan, where the internal target volume was used. We validated our computation time analyses in two additional cases. The computation speed in our implementation did not monotonically increase with the number of GPUs. The optimal number of GPUs (five, in our study) is directly related to the hardware specifications. The optimization process took 35 min using 50 PSO particles, 25 iterations and 5 GPUs.