Purpose : Monte Carlo methods are considered the gold standard for dosimetric computations in radiotherapy. Their execution time is however still an obstacle to the routine use of Monte Carlo packages in a clinical setting. To address this problem, a completely new, and designed from the ground up for the GPU, Monte Carlo dose calculation package for voxelized geometries is proposed: GPUMCD.Method : GPUMCD implements a coupled photon-electron Monte Carlo simulation for energies in the range 0.01 MeV to 20 MeV. An analogue simulation of photon interactions is used and a Class II condensed history method has been implemented for the simulation of electrons. A new GPU random number generator, some divergence reduction methods as well as other optimization strategies are also described. GPUMCD was run on a NVIDIA GTX480 while single threaded implementations of EGSnrc and DPM were run on an Intel Core i7 860.Results : Dosimetric results obtained with GPUMCD were compared to EGSnrc. In all but one test case, 98% or more of all significant voxels passed a gamma criteria of 2%-2mm. In terms of execution speed and efficiency, GPUMCD is more than 900 times faster than EGSnrc and more than 200 times faster than DPM, a Monte Carlo package aiming fast executions. Absolute execution times of less than 0.3 s are found for the simulation of 1M electrons and 4M photons in water for monoenergetic beams of 15 MeV, including GPU-CPU memory transfers.Conclusion : GPUMCD, a new GPU-oriented Monte Carlo dose calculation platform, has been compared to EGSnrc and DPM in terms of dosimetric results and execution speed. Its accuracy and speed make it an interesting solution for full Monte Carlo dose calculation in radiation oncology.

A new hybrid imaging-treatment modality, the MRI-Linac, involves the irradiation of the patient in the presence of a strong magnetic field. This field acts on the charged particles, responsible for depositing dose, through the Lorentz force. These conditions require a dose calculation engine capable of taking into consideration the effect of the magnetic field on the dose distribution during the planning stage. Also in the case of a change in anatomy at the time of treatment, a fast online replanning tool is desirable. It is improbable that analytical solutions such as pencil beam calculations can be efficiently adapted for dose calculations within a magnetic field. Monte Carlo simulations have therefore been used for the computations but the calculation speed is generally too slow to allow online replanning. In this work, GPUMCD, a fast graphics processing unit (GPU)-based Monte Carlo dose calculation platform, was benchmarked with a new feature that allows dose calculations within a magnetic field. As a proof of concept, this new feature is validated against experimental measurements. GPUMCD was found to accurately reproduce experimental dose distributions according to a 2%-2 mm gamma analysis in two cases with large magnetic field-induced dose effects: a depth-dose phantom with an air cavity and a lateral-dose phantom surrounded by air. Furthermore, execution times of less than 15 s were achieved for one beam in a prostate case phantom for a 2% statistical uncertainty while less than 20 s were required for a seven-beam plan. These results indicate that GPUMCD is an interesting candidate, being fast and accurate, for dose calculations for the hybrid MRI-Linac modality.

The numerical calculation of dose is central to treatment planning in radiation therapy and is at the core of optimization strategies for modern delivery techniques. In a clinical environment, dose calculation algorithms are required to be accurate and fast. The accuracy is typically achieved through the integration of patient-specific data and extensive beam modeling, which generally results in slower algorithms. In order to alleviate execution speed problems, the authors have implemented a modern dose calculation algorithm on a massively parallel hardware architecture. More specifically, they have implemented a convolution-superposition photon beam dose calculation algorithm on a commodity graphics processing unit (GPU). They have investigated a simple porting scenario as well as slightly more complex GPU optimization strategies. They have achieved speed improvement factors ranging from 10 to 20 times with GPU implementations compared to central processing unit (CPU) implementations, with higher values corresponding to larger kernel and calculation grid sizes. In all cases, they preserved the numerical accuracy of the GPU calculations with respect to the CPU calculations. These results show that streaming architectures such as GPUs can significantly accelerate dose calculation algorithms and let envision benefits for numerically intensive processes such as optimizing strategies, in particular, for complex delivery techniques such as IMRT and are therapy.

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