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  • 1
    Abstract: Prompt gamma-ray imaging with a knife-edge shaped slit camera provides the possibility of verifying proton beam range in tumor therapy. Dedicated experiments regarding the characterization of the camera system have been performed previously. Now, we aim at implementing the prototype into clinical application of monitoring patient treatments. Focused on this goal of translation into clinical operation, we systematically addressed remaining challenges and questions. We developed a robust energy calibration routine and corresponding quality assurance protocols. Furthermore, with dedicated experiments, we determined the positioning precision of the system to 1.1 mm (2sigma). For the first time, we demonstrated the application of the slit camera, which was intentionally developed for pencil beam scanning, to double scattered proton beams. Systematic experiments with increasing complexity were performed. It was possible to visualize proton range shifts of 2-5 mm with the camera system in phantom experiments in passive scattered fields. Moreover, prompt gamma-ray profiles for single iso-energy layers were acquired by synchronizing time resolved measurements to the rotation of the range modulator wheel of the treatment system. Thus, a mapping of the acquired profiles to different anatomical regions along the beam path is feasible and additional information on the source of potential range shifts can be obtained. With the work presented here, we show that an application of the slit camera in clinical treatments is possible and of potential benefit.
    Type of Publication: Journal article published
    PubMed ID: 27779120
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  • 2
    Abstract: A variety of compartment models are used for the quantitative analysis of dynamic positron emission tomography (PET) data. Traditionally, these models use an iterative fitting (IF) method to find the least squares between the measured and calculated values over time, which may encounter some problems such as the overfitting of model parameters and a lack of reproducibility, especially when handling noisy data or error data. In this paper, a machine learning (ML) based kinetic modeling method is introduced, which can fully utilize a historical reference database to build a moderate kinetic model directly dealing with noisy data but not trying to smooth the noise in the image. Also, due to the database, the presented method is capable of automatically adjusting the models using a multi-thread grid parameter searching technique. Furthermore, a candidate competition concept is proposed to combine the advantages of the ML and IF modeling methods, which could find a balance between fitting to historical data and to the unseen target curve. The machine learning based method provides a robust and reproducible solution that is user-independent for VOI-based and pixel-wise quantitative analysis of dynamic PET data.
    Type of Publication: Journal article published
    PubMed ID: 28379842
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  • 3
  • 4
    Abstract: The sensitivity of intensity-modulated proton therapy (IMPT) treatment plans to uncertainties can be quantified and mitigated with robust/min-max and stochastic/probabilistic treatment analysis and optimization techniques. Those methods usually rely on sparse random, importance, or worst-case sampling. Inevitably, this imposes a trade-off between computational speed and accuracy of the uncertainty propagation. Here, we investigate analytical probabilistic modeling (APM) as an alternative for uncertainty propagation and minimization in IMPT that does not rely on scenario sampling. APM propagates probability distributions over range and setup uncertainties via a Gaussian pencil-beam approximation into moments of the probability distributions over the resulting dose in closed form. It supports arbitrary correlation models and allows for efficient incorporation of fractionation effects regarding random and systematic errors. We evaluate the trade-off between run-time and accuracy of APM uncertainty computations on three patient datasets. Results are compared against reference computations facilitating importance and random sampling. Two approximation techniques to accelerate uncertainty propagation and minimization based on probabilistic treatment plan optimization are presented. Runtimes are measured on CPU and GPU platforms, dosimetric accuracy is quantified in comparison to a sampling-based benchmark (5000 random samples). APM accurately propagates range and setup uncertainties into dose uncertainties at competitive run-times (GPU [Formula: see text] min). The resulting standard deviation (expectation value) of dose show average global [Formula: see text] pass rates between 94.2% and 99.9% (98.4% and 100.0%). All investigated importance sampling strategies provided less accuracy at higher run-times considering only a single fraction. Considering fractionation, APM uncertainty propagation and treatment plan optimization was proven to be possible at constant time complexity, while run-times of sampling-based computations are linear in the number of fractions. Using sum sampling within APM, uncertainty propagation can only be accelerated at the cost of reduced accuracy in variance calculations. For probabilistic plan optimization, we were able to approximate the necessary pre-computations within seconds, yielding treatment plans of similar quality as gained from exact uncertainty propagation. APM is suited to enhance the trade-off between speed and accuracy in uncertainty propagation and probabilistic treatment plan optimization, especially in the context of fractionation. This brings fully-fledged APM computations within reach of clinical application.
    Type of Publication: Journal article published
    PubMed ID: 28649976
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  • 5
    Abstract: Radiation therapy treatment planning requires an incorporation of uncertainties in order to guarantee an adequate irradiation of the tumor volumes. In current clinical practice, uncertainties are accounted for implicitly with an expansion of the target volume according to generic margin recipes. Alternatively, it is possible to account for uncertainties by explicit minimization of objectives that describe worst case treatment scenarios, the expectation value of the treatment, or the coverage probability of the target volumes during treatment planning.
 
 In this note we show that approaches relying on objectives to induce a specific coverage of the clinical target volumes are inevitably sensitive to variation of the relative weighting of the objectives. To address this issue, we introduce coverage-based constraints for intensity-modulated radiation therapy treatment planning. Our implementation follows the concept of coverage optimized planning that considers explicit error scenarios to calculate and optimize patient specific probabilities q(d,v) of covering a specific target volume fraction v with a certain dose d. Using a constraint-based reformulation of coverage-based objectives we eliminate the trade-off between coverage and competing objectives during treatment planning.
 
 In-depth convergence tests including 324 treatment plan optimizations demonstrate the reliability of coverage-based constraints for varying levels of probability, dose, and volume. General clinical applicability of coverage-based constraints is demonstrated for two cases. A sensitivity analysis regarding penalty variations within this planing study based on IMRT treatment planning using (1) coverage-based constraints, (2) coverage-based objectives, (3) probabilistic optimization, (4) robust optimization, and (5) conventional margins illustrates the potential benefit of coverage-based constraints which do not require tedious adjustment of target volume objectives.
    Type of Publication: Journal article published
    PubMed ID: 28741600
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  • 6
    Abstract: The detection of spherical markers in x-ray projections is an important task in a variety of applications, e.g. geometric calibration and detector distortion correction. Therein, the projection of the sphere center on the detector is of particular interest as the used spherical beads are no ideal point-like objects. Only few methods have been proposed to estimate this respective position on the detector with sufficient accuracy and surrogate positions, e.g. the center of gravity, are used, impairing the results of subsequent algorithms. We propose to estimate the projection of the sphere center on the detector using a simulation-based method matching an artificial projection to the actual measurement. The proposed algorithm intrinsically corrects for all polychromatic effects included in the measurement and absent in the simulation by a polynomial which is estimated simultaneously. Furthermore, neither the acquisition geometry nor any object properties besides the fact that the object is of spherical shape need to be known to find the center of the bead. It is shown by simulations that the algorithm estimates the center projection with an error of less than [Formula: see text] of the detector pixel size in case of realistic noise levels and that the method is robust to the sphere material, sphere size, and acquisition parameters. A comparison to three reference methods using simulations and measurements indicates that the proposed method is an order of magnitude more accurate compared to these algorithms. The proposed method is an accurate algorithm to estimate the center of spherical markers in CT projections in the presence of polychromatic effects and noise.
    Type of Publication: Journal article published
    PubMed ID: 28632499
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  • 7
    Abstract: The relative stopping power (RSP) uncertainty is the largest contributor to the range uncertainty in proton therapy. The purpose of this work was to develop a systematic method that yields accurate and patient-specific RSPs by combining (1) pre-treatment x-ray CT and (2) daily proton radiography of the patient. The method was formulated as a penalized least squares optimization problem (argmin([Formula: see text])). The parameter A represents the cumulative path-length crossed by the proton in each material, separated by thresholding on the HU. The material RSPs (water equivalent thickness/physical thickness) are denoted by x. The parameter b is the list-mode proton radiography produced using Geant4 simulations. The problem was solved using a non-negative linear-solver with [Formula: see text]. A was computed by superposing proton trajectories calculated with a cubic or linear spline approach to the CT. The material's RSP assigned in Geant4 were used for reference while the clinical HU-RSP calibration curve was used for comparison. The Gammex RMI-467 phantom was first investigated. The standard deviation between the estimated material RSP and the calculated RSP is 0.45%. The robustness of the techniques was then assessed as a function of the number of projections and initial proton energy. Optimization with two initial projections yields precise RSP (1.0%) for 330 MeV protons. 250 MeV protons have shown higher uncertainty (2.0%) due to the loss of precision in the path estimate. Anthropomorphic phantoms of the head, pelvis, and lung were subsequently evaluated. Accurate RSP has been obtained for the head ([Formula: see text]), the lung ([Formula: see text]) and the pelvis ([Formula: see text]). The range precision has been optimized using the calibration curves obtained with the algorithm, yielding a mean [Formula: see text] difference to the reference of 0.11 +/-0.09%, 0.28 +/- 0.34% and [Formula: see text] in the same order. The solution's accuracy is limited by the assumed HU/RSP bijection, neglecting inherent degeneracy. The proposed formulation of the problem with prior knowledge x-ray CT demonstrates potential to increase the accuracy of present RSP estimates.
    Type of Publication: Journal article published
    PubMed ID: 28657550
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  • 8
    Abstract: Currently there is a rising interest in helium ion beams for radiotherapy. For benchmarking of the physical beam models used in treatment planning, there is a need for experimental data on the composition and spatial distribution of mixed ion fields. Of particular interest are the attenuation of the primary helium ion fluence and the build-up of secondary hydrogen ions due to nuclear interactions. The aim of this work was to provide such data with an enhanced precision. Moreover, the validity and limits of the mixed ion field equivalence between water and PMMA targets were investigated. Experiments with a 220.5 MeV/u helium ion pencil beam were performed at the Heidelberg Ion-Beam Therapy Center in Germany. The compact detection system used for ion tracking and identification was solely based on Timepix position-sensitive semiconductor detectors. In comparison to standard techniques, this system is two orders of magnitude smaller, and provides higher precision and flexibility. The numbers of outgoing helium and hydrogen ions per primary helium ion as well as the lateral particle distributions were quantitatively investigated in the forward direction behind water and PMMA targets with 5.2-18 cm water equivalent thickness (WET). Comparing water and PMMA targets with the same WET, we found that significant differences in the amount of outgoing helium and hydrogen ions and in the lateral particle distributions arise for target thicknesses above 10 cm WET. The experimental results concerning hydrogen ions emerging from the targets were reproduced reasonably well by Monte Carlo simulations using the FLUKA code. Concerning the amount of outgoing helium ions, significant differences of 3-15% were found between experiments and simulations. We conclude that if PMMA is used in place of water in dosimetry, differences in the dose distributions could arise close to the edges of the field, in particular for deep seated targets.
    Type of Publication: Journal article published
    PubMed ID: 28825918
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  • 9
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  • 10
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