pGPUMCD: an efficient GPU-based Monte Carlo code for accurate proton dose calculations

Monte Carlo Simulation of TRIM Algorithm in Ceramic Biomaterial in Proton Therapy

Biomaterials play a crucial role in enhancing human health and quality of life. They are employed in applications such as tissue substitution, diagnostic tools, medical supplies, therapeutic treatments, regenerative medicine, and radiation dosimetric studies. However, their predisposition to proton therapy, which is a trending treatment in the world, has not been adequately studied. Ceramic biomaterials, known for their hardness and durability, offer versatile uses, especially in bone tissue replacements. The wide range of physical, mechanical, and chemical properties exhibited by ceramics has spurred extensive research, development, and application in this field. This study focuses on investigating and analyzing the ionization, recoils, phonon release, collision events, and lateral scattering properties of ceramic biomaterials that closely resemble bone tissue in proton therapy applications. Monte Carlo (MC) Transport of Ions in Matter (TRIM) simulation tools were utilized for this analysis. The results showed that Silicon dioxide exhibited the Bragg peak position closest to bone tissue, with a deviation of 10.6%. The average recoils differed by 1.7%, and the lateral scattering differed by 3.6%. The main innovation of this study lies in considering interactions such as recoil, collision events, phonon production, and lateral scattering when selecting biomaterials, despite their limited digitization and understanding. By evaluating all these interactions, the study aimed to identify the most suitable ceramic biomaterial to replace bone tissue in proton therapy.

MOQUI: an open-source GPU-based Monte Carlo code for proton dose calculation with efficient data structure

Objective.Monte Carlo (MC) codes are increasingly used for accurate radiotherapy dose calculation. In proton therapy, the accuracy of the dose calculation algorithm is expected to have a more significant impact than in photon therapy due to the depth-dose characteristics of proton beams. However, MC simulations come at a considerable computational cost to achieve statistically sufficient accuracy. There have been efforts to improve computational efficiency while maintaining sufficient accuracy. Among those, parallelizing particle transportation using graphic processing units (GPU) achieved significant improvements. Contrary to the central processing unit, a GPU has limited memory capacity and is not expandable. It is therefore challenging to score quantities with large dimensions requiring extensive memory. The objective of this study is to develop an open-source GPU-based MC package capable of scoring those quantities.Approach.We employed a hash-table, one of the key-value pair data structures, to efficiently utilize the limited memory of the GPU and score the quantities requiring a large amount of memory. With the hash table, only voxels interacting with particles will occupy memory, and we can search the data efficiently to determine their address. The hash-table was integrated with a novel GPU-based MC code, moqui.Main results.The developed code was validated against an MC code widely used in proton therapy, TOPAS, with homogeneous and heterogeneous phantoms. We also compared the dose calculation results of clinical treatment plans. The developed code agreed with TOPAS within 2%, except for the fall-off and regions, and the gamma pass rates of the results were >99% for all cases with a 2 mm/2% criteria.Significance.We can score dose-influence matrix and dose-rate on a GPU for a 3-field H&N case with 10 GB of memory using moqui, which would require more than 100 GB of memory with the conventionally used array data structure.

Adaptive proton therapy

Radiation therapy treatments are typically planned based on a single image set, assuming that the patient's anatomy and its position relative to the delivery system remains constant during the course of treatment. Similarly, the prescription dose assumes constant biological dose-response over the treatment course. However, variations can and do occur on multiple time scales. For treatment sites with significant intra-fractional motion, geometric changes happen over seconds or minutes, while biological considerations change over days or weeks. At an intermediate timescale, geometric changes occur between daily treatment fractions. Adaptive radiation therapy is applied to consider changes in patient anatomy during the course of fractionated treatment delivery. While traditionally adaptation has been done off-line with replanning based on new CT images, online treatment adaptation based on on-board imaging has gained momentum in recent years due to advanced imaging techniques combined with treatment delivery systems. Adaptation is particularly important in proton therapy where small changes in patient anatomy can lead to significant dose perturbations due to the dose conformality and finite range of proton beams. This review summarizes the current state-of-the-art of on-line adaptive proton therapy and identifies areas requiring further research.

Clinical validation of a GPU-based Monte Carlo dose engine of a commercial treatment planning system for pencil beam scanning proton therapy

PURPOSE

To perform the validation of the GPU-based (Graphical Processing Unit based) proton Monte Carlo (MC) dose engine implemented in a commercial TPS (RayStation 10B) and to report final dose calculation times for clinical cases.

MATERIALS AND METHODS

440 patients treated at the Proton Therapy Center of Trento, Italy, between 2018 and 2019 were selected for this study. 636 approved plans with 3361 beams computed with the clinically implemented CPU-MC dose engine (version 4.2 and 4.5), were used for the validation of the new algorithm. For each beam, the dose was recalculated using the new GPU-MC dose engine with the initial CPU computation settings and compared to the original CPU-MC dose. Beam dose difference distributions were studied to ensure that the two dose distributions were equal within the expected fluctuations of the MC statistical uncertainty (s) of each computation. Plan dose distributions were compared with respect to the dosimetric indices D₉₈, D₅₀ and D₁ of all ROIs defined as targets. A complete assessment of the computation time as a function of s and dose grid voxel size was done.

RESULTS

The median over all mean beam dose differences between CPU- and GPU-MC was -0.01% and the median of the corresponding standard deviations was close to (√2s) both for simulations with an s of 0.5% and 1.0% per beam. This shows that the two dose distributions can be considered equal. All the DVH indices showed an average difference below 0.04%. About half of the plans were computed with 1.0% statistical uncertainty on a 2 mm dose calculation grid, for which the median computation time was 5.2 s. The median computational speed for all plans in the study was 8.4 million protons/second.

CONCLUSION

A validation of a clinical MC algorithm running on GPU was performed on a large pool of patients treated with pencil beam scanning proton therapy. We demonstrated that the differences with the previous CPU-based MC were only due to the intrinsic statistical fluctuations of the MC method, which translated to insignificant differences on plan dose level. The significant increase in dose calculation speed is expected to facilitate new clinical workflows.