Peter Ziegenhein

GPU-based High-Performance Computing for Radiation Therapy

Recent developments in radiotherapy therapy demand high computation powers to solve challenging problems in a timely fashion in a clinical environment. The graphics processing unit (GPU), as an emerging high-performance computing platform, has been introduced to radiotherapy. It is particularly attractive due to its high computational power, small size, and low cost for facility deployment and maintenance. Over the past few years, GPU-based high-performance computing in radiotherapy has experienced rapid developments. A tremendous amount of study has been conducted, in which large acceleration factors compared with the conventional CPU platform have been observed. In this paper, we will first give a brief introduction to the GPU hardware structure and programming model. We will then review the current applications of GPU in major imaging-related and therapy-related problems encountered in radiotherapy. A comparison of GPU with other platforms will also be presented.

Performance-optimized clinical IMRT planning on modern CPUs.

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.

Characterizing the combinatorial beam angle selection problem

The beam angle selection (BAS) problem in intensity-modulated radiation therapy is often interpreted as a combinatorial optimization problem, i.e. finding the best combination of η beams in a discrete set of candidate beams. It is well established that the combinatorial BAS problem may be solved efficiently with metaheuristics such as simulated annealing or genetic algorithms. However, the underlying parameters of the optimization process, such as the inclusion of non-coplanar candidate beams, the angular resolution in the space of candidate beams, and the number of evaluated beam ensembles as well as the relative performance of different metaheuristics have not yet been systematically investigated. We study these open questions in a meta-analysis of four strategies for combinatorial optimization in order to provide a reference for future research related to the BAS problem in intensity-modulated radiation therapy treatment planning. We introduce a high-performance inverse planning engine for BAS. It performs a full fluence optimization for ≈3600 treatment plans per hour while handling up to 50 GB of dose influence data (≈1400 candidate beams). For three head and neck patients, we compare the relative performance of a genetic, a cross-entropy, a simulated annealing and a naive iterative algorithm. The selection of ensembles with 5, 7, 9 and 11 beams considering either only coplanar or all feasible candidate beams is studied for an angular resolution of 5°, 10°, 15° and 20° in the space of candidate beams. The impact of different convergence criteria is investigated in comparison to a fixed termination after the evaluation of 10 000 beam ensembles. In total, our simulations comprise a full fluence optimization for about 3000 000 treatment plans. All four combinatorial BAS strategies yield significant improvements of the objective function value and of the corresponding dose distributions compared to standard beam configurations with equi-spaced coplanar beams. The genetic and the cross-entropy algorithms showed faster convergence in the very beginning of the optimization but the simulated annealing algorithm eventually arrived at almost the same objective function values. These three strategies typically yield clinically equivalent treatment plans. The iterative algorithm showed the worst convergence properties. The choice of the termination criterion had a stronger influence on the performance of the simulated annealing algorithm than on the performance of the genetic and the cross-entropy algorithms. We advocate to terminate the optimization process after the evaluation of 1000 beam combinations without objective function decrease. For our simulations, this resulted in an average deviation of the objective function from the reference value after 10 000 evaluated beam ensembles of 0.5% for all metaheuristics. On average, there was only a minor improvement when increasing the angular resolution in the space of candidate beam angles from 20° to 5°. However, we observed significant improvements when considering non-coplanar candidate beams for challenging head and neck cases.

Comparison of beam angle selection strategies for intracranial IMRT.

PURPOSE Various strategies to select beneficial beam ensembles for intensity-modulated radiation therapy (IMRT) have been suggested over the years. These beam angle selection (BAS) strategies are usually evaluated against reference configurations applying equispaced coplanar beams but they are not compared to one another. Here, the authors present a meta analysis of four BAS strategies that incorporates fluence optimization (FO) into BAS by combinatorial optimization (CO) and one BAS strategy that decouples FO from BAS, i.e., spherical cluster analysis (SCA). The underlying parameters of the BAS process are investigated and the dosimetric benefits of the BAS strategies are quantified. METHODS For three intracranial lesions in proximity to organs at risk (OARs) the authors compare treatment plans applying equispaced coplanar beam ensembles with treatment plans using five different BAS strategies, i.e., four CO techniques and SCA, to establish coplanar and noncoplanar beam ensembles. Treatment plans applying 5, 7, 9, and 11 beams are investigated. For the CO strategies the authors perform BAS runs with a 5°, 10°, 15°, and 20° angular resolution, which corresponds to a minimum of 18 coplanar and a maximum of 1400 noncoplanar candidate beams. In total 272 treatment plans with different BAS settings are generated for every patient. The quality of the treatment plans is compared based on the protection of OARs yet integral dose, target homogeneity, and target conformity are also considered. RESULTS It is possible to reduce the average mean and maximum doses in OARs by more than 4 Gy (1 Gy) with optimized noncoplanar (coplanar) beam ensembles found with BAS by CO or SCA. For BAS including FO by CO, the individual algorithm used and the angular resolution in the space of candidate beams does not have a crucial impact on the quality of the resulting treatment plans. All CO algorithms yield similar target conformity and slightly improved target homogeneity in comparison to equispaced coplanar setups. Furthermore, optimized coplanar (noncoplanar) beam ensembles enabled more than a 6% (5%) reduction of the integral dose. For SCA, however, integral dose was increased and target conformity was decreased in comparison to equispaced coplanar setups-especially for a small number of beams. CONCLUSION Both BAS strategies incorporating FO by CO and independent BAS strategies excluding FO provide dose savings in OARs for optimized coplanar and especially noncoplanar beam ensembles; they should not be neglected in the clinic.

Boosting runtime-performance of photon pencil beam algorithms for radiotherapy treatment planning.

Pencil beam algorithms are still considered as standard photon dose calculation methods in Radiotherapy treatment planning for many clinical applications. Despite their established role in radiotherapy planning their performance and clinical applicability has to be continuously adapted to evolving complex treatment techniques such as adaptive radiation therapy (ART). We herewith report on a new highly efficient version of a well-established pencil beam convolution algorithm which relies purely on measured input data. A method was developed that improves raytracing efficiency by exploiting the capability of modern CPU architecture for a runtime reduction. Since most of the current desktop computers provide more than one calculation unit we used symmetric multiprocessing extensively to parallelize the workload and thus decreasing the algorithmic runtime. To maximize the advantage of code parallelization, we present two implementation strategies - one for the dose calculation in inverse planning software, and one for traditional forward planning. As a result, we could achieve on a 16-core personal computer with AMD processors a superlinear speedup factor of approx. 18 for calculating the dose distribution of typical forward IMRT treatment plans.

Assessment of MLC tracking performance during hypofractionated prostate radiotherapy using real-time dose reconstruction

Abstract By adapting to the actual patient anatomy during treatment, tracked multi-leaf collimator (MLC) treatment deliveries offer an opportunity for margin reduction and healthy tissue sparing. This is assumed to be especially relevant for hypofractionated protocols in which intrafractional motion does not easily average out. In order to confidently deliver tracked treatments with potentially reduced margins, it is necessary to monitor not only the patient anatomy but also the actually delivered dose during irradiation. In this study, we present a novel real-time online dose reconstruction tool which calculates actually delivered dose based on pre-calculated dose influence data in less than 10 ms at a rate of 25 Hz. Using this tool we investigate the impact of clinical target volume (CTV) to planning target volume (PTV) margins on CTV coverage and organ-at-risk dose. On our research linear accelerator, a set of four different CTV-to-PTV margins were tested for three patient cases subject to four different motion conditions. Based on this data, we can conclude that tracking eliminates dose cold spots which can occur in the CTV during conventional deliveries even for the smallest CTV-to-PTV margin of 1 mm. Changes of organ-at-risk dose do occur frequently during MLC tracking and are not negligible in some cases. Intrafractional dose reconstruction is expected to become an important element in any attempt of re-planning the treatment plan during the delivery based on the observed anatomy of the day.

Fast CPU-based Monte Carlo simulation for radiotherapy dose calculation

Monte-Carlo (MC) simulations are considered to be the most accurate method for calculating dose distributions in radiotherapy. Its clinical application, however, still is limited by the long runtimes conventional implementations of MC algorithms require to deliver sufficiently accurate results on high resolution imaging data. In order to overcome this obstacle we developed the software-package PhiMC, which is capable of computing precise dose distributions in a sub-minute time-frame by leveraging the potential of modern many- and multi-core CPU-based computers. PhiMC is based on the well verified dose planning method (DPM). We could demonstrate that PhiMC delivers dose distributions which are in excellent agreement to DPM. The multi-core implementation of PhiMC scales well between different computer architectures and achieves a speed-up of up to 37[Formula: see text] compared to the original DPM code executed on a modern system. Furthermore, we could show that our CPU-based implementation on a modern workstation is between 1.25[Formula: see text] and 1.95[Formula: see text] faster than a well-known GPU implementation of the same simulation method on a NVIDIA Tesla C2050. Since CPUs work on several hundreds of GB RAM the typical GPU memory limitation does not apply for our implementation and high resolution clinical plans can be calculated.

A 3D isodose manipulation tool for interactive dose shaping

The interactive dose shaping (IDS) planning paradigm aims to perform interactive local dose adaptations of an IMRT plan without compromising already established valuable dose features in real-time. In this work we introduce an interactive 3D isodose manipulation tool which enables local modifications of a dose distribution intuitively by direct manipulation of an isodose surface. We developed an in-house IMRT TPS framework employing an IDS engine as well as a 3D GUI for dose manipulation and visualization. In our software an initial dose distribution can be interactively modified through an isodose surface manipulation tool by intuitively clicking on an isodose surface. To guide the user interaction, the position of the modification is indicated by a sphere while the mouse cursor hovers the isodose surface. The spheres radius controls the locality of the modification. The tool induces a dose modification as a direct change of dose in one or more voxels, which is incrementally obtained by fluence adjustments. A subsequent recovery step identifies voxels with violated dose features and aims to recover their original dose. We showed a proof of concept study for the proposed tool by adapting the dose distribution of a prostate case (9 beams, coplanar). Single dose modifications take less than 2 seconds on an actual desktop PC.

Speed optimized influence matrix processing in inverse treatment planning tools.

An optimal plan in modern treatment planning tools is found through the use of an iterative optimization algorithm, which deals with a high amount of patient-related data and number of treatment parameters to be optimized. Thus, calculating a good plan is a very time-consuming process which limits the application for patients in clinics and for research activities aiming for more accuracy. A common technique to handle the vast amount of radiation dose data is the concept of the influence matrix (DIJ), which stores the dose contribution of each bixel to the patient in the main memory of the computer. This study revealed that a bottleneck for the optimization time arises from the data transfer of the dose data between the memory and the CPU. In this note, we introduce a new method which speeds up the data transportation from stored dose data to the CPU. As an example we used the DIJ approach as is implemented in our treatment planning tool KonRad, developed at the German Cancer Research Center (DKFZ) in Heidelberg. A data cycle reordering method is proposed to take the advantage of modern memory hardware. This induces a minimal eviction policy which results in a memory behaviour exhibiting a 2.6 times faster algorithm compared to the naive implementation. Although our method is described for the DIJ approach implemented in KonRad, we believe that any other planning tool which uses a similar approach to store the dose data will also benefit from the described methods.

Interactive dose shaping part 2: proof of concept study for six prostate patients

Abstract Recently we introduced interactive dose shaping (IDS) as a new IMRT planning strategy. This planning concept is based on a hierarchical sequence of local dose modification and recovery operations. The purpose of this work is to provide a feasibility study for the IDS planning strategy based on a small set of six prostate patients. The IDS planning paradigm aims to perform interactive local dose adaptations of an IMRT plan without compromising already established valuable dose features in real-time. Various IDS tools were developed in our in-house treatment planning software Dynaplan and were utilized to create IMRT treatment plans for six patients with an adeno-carcinoma of the prostate. The sequenced IDS treatment plans were compared to conventionally optimized clinically approved plans (9 beams, co-planar). For each patient, several IDS plans were created, with different trade-offs between organ sparing and target coverage. The reference dose distributions were imported into Dynaplan. For each patient, the IDS treatment plan with a similar or better trade-off between target coverage and OAR sparing was selected for plan evaluation, guided by a physician. For this initial study we were able to generate treatment plans for prostate geometries in 15–45 min. Individual local dose adaptations could be performed in less than one second. The average differences compared to the reference plans were for the mean dose: 0.0 Gy (boost) and 1.2 Gy (PTV), for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}