@inproceedings{161396dcb2ec4794b7291dc45fffb01d,
title = "Towards real time radiotherapy simulation",
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.",
keywords = "Dataflow, Dose Calculation, FPGA Acceleration, Monte Carlo Simulation, Radiotherapy",
author = "Nils Voss and Peter Ziegenhein and Lukas Vermond and Joost Hoozemans and Oskar Mencer and Uwe Oelfke and Wayne Luk and Georgi Gaydadjiev",
note = "Accepted author manuscript; 30th IEEE International Conference on Application-Specific Systems, Architectures and Processors, ASAP 2019 ; Conference date: 15-07-2019 Through 17-07-2019",
year = "2019",
month = jul,
day = "1",
doi = "10.1109/ASAP.2019.000-6",
language = "English",
volume = "2019-July",
series = "2019 IEEE 30TH INTERNATIONAL CONFERENCE ON APPLICATION-SPECIFIC SYSTEMS, ARCHITECTURES AND PROCESSORS (ASAP 2019)",
publisher = "IEEE",
pages = "173--180",
booktitle = "Proceedings - 2019 IEEE 30th International Conference on Application-Specific Systems, Architectures and Processors, ASAP 2019",
address = "United States",
}