Ion beam therapy is a rapidly growing technique for tumor radiation therapy. Ions allow for a high dose deposition in the tumor region, while sparing the surrounding healthy tissue. For this reason, the highest possible accuracy in the calculation of dose and its spatial distribution is required in treatment planning. On one hand, commonly used treatment planning software solutions adopt a simplified beam–body interaction model by remapping pre-calculated dose distributions into a 3D water-equivalent representation of the patient morphology. On the other hand, Monte Carlo (MC) simulations, which explicitly take into account all the details in the interaction of particles with human tissues, are considered to be the most reliable tool to address the complexity of mixed field irradiation in a heterogeneous environment. However, full MC calculations are not routinely used in clinical practice because they typically demand substantial computational resources. Therefore MC simulations are usually only used to check treatment plans for a restricted number of difficult cases. The advent of general-purpose programming GPU cards prompted the development of trimmed-down MC-based dose engines which can significantly reduce the time needed to recalculate a treatment plan with respect to standard MC codes in CPU hardware. In this work, we report on the development of fred, a new MC simulation platform for treatment planning in ion beam therapy. The code can transport particles through a 3D voxel grid using a class II MC algorithm. Both primary and secondary particles are tracked and their energy deposition is scored along the trajectory. Effective models for particle–medium interaction have been implemented, balancing accuracy in dose deposition with computational cost. Currently, the most refined module is the transport of proton beams in water: single pencil beam dose–depth distributions obtained with fred agree with those produced by standard MC codes within 1–2% of the Bragg peak in the therapeutic energy range. A comparison with measurements taken at the CNAO treatment center shows that the lateral dose tails are reproduced within 2% in the field size factor test up to 20cm. The tracing kernel can run on GPU hardware, achieving 10 million primary s−1 on a single card. This performance allows one to recalculate a proton treatment plan at 1% of the total particles in just a few minutes.

Fred: A GPU-accelerated fast-Monte Carlo code for rapid treatment plan recalculation in ion beam therapy / Schiavi, A.; Senzacqua, M.; Pioli, S.; Mairani, A.; Magro, G.; Molinelli, S.; Ciocca, M.; Battistoni, G.; Patera, V.. - In: PHYSICS IN MEDICINE AND BIOLOGY. - ISSN 0031-9155. - 62:18(2017), pp. 7482-7504. [10.1088/1361-6560/aa8134]

Fred: A GPU-accelerated fast-Monte Carlo code for rapid treatment plan recalculation in ion beam therapy

Schiavi, A.;Senzacqua, M.;Pioli, S.;Patera, V.
2017

Abstract

Ion beam therapy is a rapidly growing technique for tumor radiation therapy. Ions allow for a high dose deposition in the tumor region, while sparing the surrounding healthy tissue. For this reason, the highest possible accuracy in the calculation of dose and its spatial distribution is required in treatment planning. On one hand, commonly used treatment planning software solutions adopt a simplified beam–body interaction model by remapping pre-calculated dose distributions into a 3D water-equivalent representation of the patient morphology. On the other hand, Monte Carlo (MC) simulations, which explicitly take into account all the details in the interaction of particles with human tissues, are considered to be the most reliable tool to address the complexity of mixed field irradiation in a heterogeneous environment. However, full MC calculations are not routinely used in clinical practice because they typically demand substantial computational resources. Therefore MC simulations are usually only used to check treatment plans for a restricted number of difficult cases. The advent of general-purpose programming GPU cards prompted the development of trimmed-down MC-based dose engines which can significantly reduce the time needed to recalculate a treatment plan with respect to standard MC codes in CPU hardware. In this work, we report on the development of fred, a new MC simulation platform for treatment planning in ion beam therapy. The code can transport particles through a 3D voxel grid using a class II MC algorithm. Both primary and secondary particles are tracked and their energy deposition is scored along the trajectory. Effective models for particle–medium interaction have been implemented, balancing accuracy in dose deposition with computational cost. Currently, the most refined module is the transport of proton beams in water: single pencil beam dose–depth distributions obtained with fred agree with those produced by standard MC codes within 1–2% of the Bragg peak in the therapeutic energy range. A comparison with measurements taken at the CNAO treatment center shows that the lateral dose tails are reproduced within 2% in the field size factor test up to 20cm. The tracing kernel can run on GPU hardware, achieving 10 million primary s−1 on a single card. This performance allows one to recalculate a proton treatment plan at 1% of the total particles in just a few minutes.
2017
graphics processing units; Monte Carlo codes; particle therapy; treatment planning; Humans; Neoplasms; Protons; Radiotherapy Dosage; Radiotherapy Planning, Computer-Assisted; Software; Algorithms; Computer Graphics; Monte Carlo Method; Phantoms, Imaging; Radiological and Ultrasound Technology; Radiology, Nuclear Medicine and Imaging
01 Pubblicazione su rivista::01a Articolo in rivista
Fred: A GPU-accelerated fast-Monte Carlo code for rapid treatment plan recalculation in ion beam therapy / Schiavi, A.; Senzacqua, M.; Pioli, S.; Mairani, A.; Magro, G.; Molinelli, S.; Ciocca, M.; Battistoni, G.; Patera, V.. - In: PHYSICS IN MEDICINE AND BIOLOGY. - ISSN 0031-9155. - 62:18(2017), pp. 7482-7504. [10.1088/1361-6560/aa8134]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1084677
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