Towards real time radiotherapy simulation

Conference paper (2019)

Authors

Nils Voss Imperial College London, Maxeler Technologies
Peter Ziegenhein Royal Marsden NHS Foundation Trust
Lukas Vermond Maxeler Technologies, Student
J.J. Hoozemans Maxeler Technologies
Oskar Mencer Maxeler Technologies
Uwe Oelfke Royal Marsden NHS Foundation Trust
Wayne Luk Imperial College London
G. Gaydadjiev Imperial College London, Data-Intensive Systems - EEMCS , Maxeler Technologies
Research Group
Data-Intensive Systems (EEMCS) (TU Delft)
Copyright: © 2019 Nils Voss, Peter Ziegenhein, Lukas Vermond, J.J. Hoozemans, Oskar Mencer, Uwe Oelfke, Wayne Luk, G. Gaydadjiev
DOI
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https://doi.org/10.1109/ASAP.2019.000-6
Monte Carlo Simulation Dose Calculation Radiotherapy Dataflow FPGA Acceleration
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Published Date

01-07-2019

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Data-Intensive Systems

Abstract

We propose a novel reconfigurable hardware architecture to implement Monte Carlo based simulation of physical dose accumulation for intensity-modulated adaptive radiotherapy. The long term goal of our effort is to provide accurate online dose calculation in real-time during patient treatment. This will allow wider adoption of personalised patient therapies which has the potential to significantly reduce dose exposure to the patient as well as shorten treatment and greatly reduce costs. The proposed architecture exploits the inherent parallelism of Monte Carlo simulations to perform domain decomposition and provide high resolution simulation without being limited by on-chip memory capacity. We present our architecture in detail and provide a performance model to estimate execution time, hardware area and bandwidth utilisation. Finally, we evaluate our architecture on a Xilinx VU9P platform and show that three cards are sufficient to meet our real time target of 100 million randomly generated particle histories per second.