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  • THERAPY  (16)
  • OPTIMIZATION  (10)
  • PROSTATE  (9)
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  • 1
    Keywords: radiation ; OPTIMIZATION ; treatment ; TECHNOLOGY ; inverse planning ; ONCOLOGY ; RADIATION ONCOLOGY
    Type of Publication: Book chapter
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  • 2
    Keywords: radiotherapy ; CANCER ; THERAPY ; CT ; IMAGES ; radiation ; RADIATION-THERAPY ; NECK ; HEAD ; CT images ; NECK-CANCER ; head and neck cancer ; radiation therapy ; THERAPIES ; NECK CANCER
    Type of Publication: Book chapter
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  • 3
    Keywords: RADIATION-THERAPY ; LOCALIZATION ; DATABASE ; IMRT ; radiation ; SYSTEM ; THERAPY ; THERAPIES ; radiation therapy
    Type of Publication: Book chapter
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  • 4
    Keywords: OPTIMIZATION ; SPECTRA ; radiotherapy ; evaluation ; MODEL ; THERAPY ; SYSTEM ; SYSTEMS ; VOLUME ; RISK ; radiation ; TIME ; PATIENT ; BASE ; treatment ; TARGET ; RADIATION-THERAPY ; adaptive triangulation ; clustering techniques ; multi-criteria optimization ; representative pareto solutions
    Abstract: Radiation therapy planning is often a tightrope walk between dangerous insufficient dose in the target volume and life threatening overdosing of organs at risk. Finding ideal balances between these inherently contradictory goals challenges dosimetrists and physicians in their daily practice. Todays inverse planning systems calculate treatment plans based on a single evaluation function that measures the quality of a radiation treatment plan. Unfortunately, such a one dimensional approach cannot satisfactorily map the different backgrounds of physicians and the patient dependent necessities. So, too often a time consuming iterative optimization process between evaluation of the dose distribution and redefinition of the evaluation function is needed. In this paper we propose a generic multi-criteria approach based on Pareto's solution concept. For each entity of interest - target volume or organ at risk - a structure dependent evaluation function is defined measuring deviations from ideal doses that are calculated from statistical functions. A reasonable bunch of clinically meaningful Pareto optimal solutions are stored in a data base, which can be interactively searched by physicians. The system guarantees dynamic planning as well as the discussion of tradeoffs between different entities. Mathematically, we model the inverse problem as a multi-criteria linear programming problem. Because of the large scale nature of the problem it is not possible to solve the problem in a 3D-setting without adaptive reduction by, appropriate approximation schemes. Our approach is twofold: First, the discretization of the continuous problem results from an adaptive hierarchical clustering process which is used for a local refinement of constraints during the optimization procedure. Second, the set of Pareto optimal solutions is approximated by an adaptive grid of representatives that are found by a hybrid process of calculating extreme compromises and interpolation methods
    Type of Publication: Journal article published
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  • 5
    Keywords: OPTIMIZATION ; radiotherapy ; Germany ; THERAPY ; ALGORITHM ; ALGORITHMS ; imaging ; NUCLEAR-MEDICINE ; radiation ; SEQUENCE ; treatment ; DISTRIBUTIONS ; RADIATION-THERAPY ; DIFFERENCE ; NUMBER ; BEAM ; DELIVERY ; STRATEGIES ; INTENSITY-MODULATED RADIOTHERAPY ; MULTILEAF COLLIMATOR ; SEGMENTS ; nuclear medicine ; IMRT ; APPROXIMATION ; MAPS ; radiology ; PROGRAM ; THERAPIES ; radiation therapy ; intensity modulated radiotherapy ; NUCLEAR ; technique ; BEAMS ; MEDICINE ; ERROR ; CONSTRAINTS
    Abstract: In inverse planning for intensity-modulated radiotherapy ( IMRT), the fluence distribution of each treatment beam is usually calculated in an optimization process. The delivery of the resulting treatment plan using multileaf collimators ( MLCs) is performed either in the step-and-shoot or sliding window technique. For step-and-shoot delivery, the arbitrary beam fluence distributions have to be transformed into an applicable sequence of subsegments. In a stratification step the complexity of the fluence maps is reduced by assigning each beamlet to discrete intensity values, followed by the sequencing step that generates the subsegments. In this work, we concentrate on the stratification for step-and-shoot delivery. Different concepts of stratification are formally introduced. In addition to already used strategies that minimize the difference between original and stratified beam intensities, we propose an original stratification principle that minimizes the error of the resulting dose distribution. It could be shown that for a comparable total number of subsegments the dose-oriented stratification results in a better approximation of the original, unsequenced plan. The presented algorithm can replace the stratification routine in existing sequencer programs and can also be applied to interpolated plans that are generated in an interactive decision making process of multicriteria inverse planning programs
    Type of Publication: Journal article published
    PubMed ID: 17881818
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  • 6
    Keywords: CANCER ; radiotherapy ; tumor ; carcinoma ; Germany ; THERAPY ; CT ; FOLLOW-UP ; imaging ; SURGERY ; radiation ; PATIENT ; prognosis ; CONTRAST ; RADIATION-THERAPY ; chemotherapy ; DELIVERY ; AD ; ESOPHAGUS ; RANDOMIZED-TRIAL ; IMRT ; radiology ; GUIDANCE ; THERAPIES ; LIBRARIES ; chemoradiation ; radiation therapy ; CT SCANS ; LIBRARY ; ESOPHAGEAL CANCER ; IMAGE GUIDANCE ; JUNCTION ; ATRIAL-FIBRILLATION ; outcome ; GUIDED RADIOTHERAPY ; RADIOCHEMOTHERAPY ; POSITION ; SCAN ; STRATEGY ; LIMITATIONS ; Esophageal carcinoma
    Abstract: Background: Despite maximum therapy the prognosis of esophageal carcinoma still remains extremely poor. New treatment strategies including improved radiation therapy techniques promise better outcome by improving local control through precise dose delivery due to higher conformality. Case Report: A 62-year-old patient with locally advanced carcinoma of the gastroesophageal junction underwent definitive radiochemotherapy with intensity-modulated radiation therapy (IMRT). On positioning control with the in-room CT, the distal. esophagus, and hence the tumor, was found to be highly mobile exhibiting changes in position of up to 4 cm from fraction to fraction. Result: IMRT plans were created for various positions establishing a plan library to choose from as appropriate. CT scans were performed prior to each treatment fraction to clarify esophagus position in order to choose the adequate treatment plan. Conclusion. Image guidance was crucial in this unusual case of esophageal carcinoma. Without the information from position control CTs, the tumor would have received only about half the prescribed dose due to variations in position. For this specific case, in-room CT scans are probably superior to kilo- or megavoltage CTs due to the higher soft-tissue contrast enabling detection of positioning variation of the organ and offering the possibility to use the CT for treatment planning
    Type of Publication: Journal article published
    PubMed ID: 19714309
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  • 7
    Keywords: THERAPY ; ALGORITHM ; validation ; IMAGE REGISTRATION ; algorithm validation ; evaluation registration ; landmark acquisition ; registration algorithm ; REGISTRATION TECHNIQUES ; software assistant
    Abstract: It is crucial to evaluate registration algorithms in order to make them available in clinical practice. Several evaluation strategies have been proposed in the past, and one approach is to evaluate these algorithms with intrinsic anatomical landmarks identified by a health professional. The acquisition and handling of large amounts of these landmark data is a time-consuming task for the health professional, and it is vulnerable to errors and inconsistencies. Additionally, limited access to appropriate tools makes dealing with landmark data considerably more difficult. We introduce a strategy for the acquisition of landmarks for the landmark-based evaluation of registration algorithms and we present an ontology-driven software tool that assists the different partners involved to act according to that strategy. This tool provides the user with intrinsic knowledge of the registration problems, the possibility to conveniently make the acquired data available to further processing, and an easy-to-use graphical interface.
    Type of Publication: Journal article published
    PubMed ID: 20888204
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  • 8
    Keywords: radiotherapy ; LUNG ; THERAPY ; ALGORITHM ; IMAGES ; RISK ; ACCURACY ; validation ; radiation ; PATIENT ; ASSOCIATION ; RADIATION-THERAPY ; UNCERTAINTY ; sensitivity ; DEFORMABLE IMAGE REGISTRATION ; ERRORS ; 4D CT IMAGES ; b-spline registration ; dose accumulation ; dose mapping ; fractionated radiation therapy
    Abstract: Purpose: In fractionated radiation therapy, image guidance with daily tomographic imaging becomes more and more clinical routine. In principle, this allows for daily computation of the delivered dose and for accumulation of these daily dose distributions to determine the actually delivered total dose to the patient. However, uncertainties in the mapping of the images can translate into errors of the accumulated total dose, depending on the dose gradient. In this work, an approach to estimate the uncertainty of mapping between medical images is proposed that identifies areas bearing a significant risk of inaccurate dose accumulation.Methods: This method accounts for the geometric uncertainty of image registration and the heterogeneity of the dose distribution, which is to be mapped. Its performance is demonstrated in context of dose mapping based on b-spline registration. It is based on evaluation of the sensitivity of dose mapping to variations of the b-spline coefficients combined with evaluation of the sensitivity of the registration metric with respect to the variations of the coefficients. It was evaluated based on patient data that was deformed based on a breathing model, where the ground truth of the deformation, and hence the actual true dose mapping error, is known.Results: The proposed approach has the potential to distinguish areas of the image where dose mapping is likely to be accurate from other areas of the same image, where a larger uncertainty must be expected.Conclusions: An approach to identify areas where dose mapping is likely to be inaccurate was developed and implemented. This method was tested for dose mapping, but it may be applied in context of other mapping tasks as well.
    Type of Publication: Journal article published
    PubMed ID: 22482640
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  • 9
    Keywords: CANCER ; carcinoma ; PROSTATE ; QUANTIFICATION ; DISEASE ; TISSUE ; QUALITY ; MRI ; SPECTROSCOPY ; prostate cancer ; LOCALIZATION ; PATTERN ; pattern recognition ; postprocessing ; proton MR spectroscopic imaging
    Abstract: RATIONALE AND OBJECTIVES: The aim of this study was to assess (1) automated analysis methods versus manual evaluation by human experts of three-dimensional proton magnetic resonance spectroscopic imaging (MRSI) data from patients with prostate cancer and (2) the contribution of spatial information to decision making. MATERIALS AND METHODS: Three-dimensional proton MRSI was applied at 1.5 T. MRSI data from 10 patients with histologically proven prostate adenocarcinoma, scheduled either for prostatectomy or intensity-modulated radiation therapy, were evaluated. First, two readers manually labeled spectra using spatial information to identify the localization of spectra and neighborhood information, establishing the reference set of this study. Then, spectra were labeled again manually in a blinded and randomized manner and evaluated automatically using software that applied spectral line fitting as well as pattern recognition routines. Statistical analysis of the results of the different approaches was performed. RESULTS: Altogether, 1018 spectra were evaluable by all methods. Numbers of evaluable spectra differed significantly depending on patient and evaluation method. Compared to automated analysis, the readers made rather binary decisions, using information from neighboring spectra in ambiguous cases, when evaluating MRSI data as a whole. Differences between anatomically blinded and unblinded evaluation were larger than differences between evaluations using blinded data and automated techniques. CONCLUSIONS: An automated approach, which evaluates each spectrum individually, can be as good as an anatomy-blinded human reader. Spatial information is routinely used by human experts to support their final decisions. Automated procedures that consider anatomic information for spectral evaluation will enhance the diagnostic impact of MRSI of the human prostate.
    Type of Publication: Journal article published
    PubMed ID: 22578226
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  • 10
    Keywords: OPTIMIZATION ; DISTRIBUTIONS ; RADIATION-THERAPY ; PENALTY
    Abstract: Common problems in inverse radiotherapy planning are localized dose insufficiencies like hot spots in organs at risk or cold spots inside targets. These are hard to correct since the optimization is based on global evaluations like maximum/minimum doses, equivalent uniform doses or dose-volume constraints for whole structures. In this work, we present a new approach to locally correct the dose of any given treatment plan. Once a treatment plan has been found that is acceptable in general but requires local corrections, these areas are marked by the planner. Then the system generates new plans that fulfil the local dose goals. Consequently, it is possible to interactively explore all plans between the locally corrected plans and the original treatment plan, allowing one to exactly adjust the degree of local correction and how the plan changes overall. Both the amount (in Gy) and the size of the local dose change can be navigated. The method is introduced formally as a new mathematical optimization setting, and is evaluated using a clinical example of a meningioma at the base of the skull. It was possible to eliminate a hot spot outside the target volume while controlling the dose changes to all other parts of the treatment plan. The proposed method has the potential to become the final standard step of inverse treatment planning.
    Type of Publication: Journal article published
    PubMed ID: 23442519
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