Intensity modulated treatment plan optimization is a computationally expensive task. The feasibility of advanced applications in intensity modulated radiation therapy as every day treatment planning, frequent re-planning for adaptive radiation therapy and large-scale planning research severely depends on the runtime of the plan optimization implementation. Modern computational systems are built as parallel architectures to yield high performance. The use of GPUs, as one class of parallel systems, has become very popular in the field of medical physics. In contrast we utilize the multi-core central processing unit (CPU), which is the heart of every modern computer and does not have to be purchased additionally. In this work we present an ultra-fast, high precision implementation of the inverse plan optimization problem using a quasi-Newton method on pre-calculated dose influence data sets. We redefined the classical optimization algorithm to achieve a minimal runtime and high scalability on CPUs. Using the proposed methods in this work, a total plan optimization process can be carried out in only a few seconds on a low-cost CPU-based desktop computer at clinical resolution and quality. We have shown that our implementation uses the CPU hardware resources efficiently with runtimes comparable to GPU implementations, at lower costs.

@article{PCIPOMCZKB13,
	author	 = {Peter Ziegenhein and Cornelis Ph Kamerling and Mark Bangert and Julian Kunkel and Uwe Oelfke},
	title	 = {{Performance-optimized clinical IMRT planning on modern CPUs}},
	year	 = {2013},
	month	 = {05},
	publisher	 = {IOP Publishing},
	journal	 = {Physics in Medicine and Biology},
	series	 = {Volume 58 Number 11},
	issn	 = {1361-6560},
	doi	 = {http://dx.doi.org/10.1088/0031-9155/58/11/3705},
	abstract	 = {Intensity modulated treatment plan optimization is a computationally expensive task. The feasibility of advanced applications in intensity modulated radiation therapy as every day treatment planning, frequent re-planning for adaptive radiation therapy and large-scale planning research severely depends on the runtime of the plan optimization implementation. Modern computational systems are built as parallel architectures to yield high performance. The use of GPUs, as one class of parallel systems, has become very popular in the field of medical physics. In contrast we utilize the multi-core central processing unit (CPU), which is the heart of every modern computer and does not have to be purchased additionally. In this work we present an ultra-fast, high precision implementation of the inverse plan optimization problem using a quasi-Newton method on pre-calculated dose influence data sets. We redefined the classical optimization algorithm to achieve a minimal runtime and high scalability on CPUs. Using the proposed methods in this work, a total plan optimization process can be carried out in only a few seconds on a low-cost CPU-based desktop computer at clinical resolution and quality. We have shown that our implementation uses the CPU hardware resources efficiently with runtimes comparable to GPU implementations, at lower costs.},
	url	 = {http://iopscience.iop.org/0031-9155/58/11/3705},
}