Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • OPTIMIZATION  (10)
Collection
Keywords
Publisher
  • 1
    Keywords: radiation ; OPTIMIZATION ; treatment ; TECHNOLOGY ; inverse planning ; ONCOLOGY ; RADIATION ONCOLOGY
    Type of Publication: Book chapter
    Signatur Availability
    BibTip Others were also interested in ...
  • 2
    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
    Signatur Availability
    BibTip Others were also interested in ...
  • 3
    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
    Signatur Availability
    BibTip Others were also interested in ...
  • 4
    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
    Signatur Availability
    BibTip Others were also interested in ...
  • 5
    Keywords: OPTIMIZATION ; radiotherapy ; tumor ; ALGORITHM ; ALGORITHMS ; INFORMATION ; SYSTEM ; SYSTEMS ; RISK ; treatment ; ASSOCIATION ; FORM ; TARGET ; NO ; DISTRIBUTIONS ; EQUIVALENT ; RADIATION-THERAPY ; HEAD ; NECK ; head and neck ; CONVEX-SETS ; equivalent uniform dose ; inverse planning ; optimization constraints ; projection onto convex sets ; PROJECTIONS
    Abstract: Optimization algorithms in inverse radiotherapy planning need information about the desired dose distribution. Usually the planner defines physical dose constraints for each structure of the treatment plan, either in form of minimum and maximum doses or as dose-volume constraints. The concept of equivalent uniform dose (EUD) was designed to describe dose distributions with a higher clinical relevance. In this paper, we present a method to consider the EUD as an optimization constraint by using the method of projections onto convex sets (POCS). In each iteration of the optimization loop, for the actual dose distribution of an organ that violates an EUD constraint a new dose distribution is calculated that satisfies the EUD constraint, leading to voxel-based physical dose constraints. The new dose distribution is found by projecting the current one onto the convex set of all dose distributions fulfilling the EUD constraint. The algorithm is easy to integrate into existing inverse planning systems, and it allows the planner to choose between physical and EUD constraints separately for each structure. A clinical case of a head and neck tumor is optimized using three different sets of constraints: physical constraints for all structures, physical constraints for the target and EUD constraints for the organs at risk, and EUD constraints for all structures. The results show that the POCS method converges stable and given EUD constraints are reached closely. (C) 2003 American Association of Physicists in Medicine
    Type of Publication: Journal article published
    PubMed ID: 14528955
    Signatur Availability
    BibTip Others were also interested in ...
  • 6
    Keywords: OPTIMIZATION ; radiotherapy ; RISK
    Abstract: In the multi-criteria optimization approach to IMRT planning, a given dose distribution is evaluated by a number of convex objective functions that measure tumor coverage and sparing of the different organs at risk. Within this context optimizing the intensity profiles for any fixed set of beams yields a convex Pareto set in the objective space. However, if the number of beam directions and irradiation angles are included as free parameters in the formulation of the optimization problem, the resulting Pareto set becomes more intricate. In this work, a method is presented that allows for the comparison of two convex Pareto sets emerging from two distinct beam configuration choices. For the two competing beam settings, the non-dominated and the dominated points of the corresponding Pareto sets are identified and the distance between the two sets in the objective space is calculated and subsequently plotted. The obtained information enables the planner to decide if, for a given compromise, the current beam setup is optimal. Hemay then re-adjust his choice accordingly during navigation. The method is applied to an artificial case and two clinical head neck cases. In all cases no configuration is dominating its competitor over the whole Pareto set. For example, in one of the head neck cases a seven-beam configuration turns out to be superior to a nine-beam configuration if the highest priority is the sparing of the spinal cord. The presented method of comparing Pareto sets is not restricted to comparing different beam angle configurations, but will allow for more comprehensive comparisons of competing treatment techniques (e. g. photons versus protons) than with the classical method of comparing single treatment plans
    Type of Publication: Journal article published
    PubMed ID: 21610294
    Signatur Availability
    BibTip Others were also interested in ...
  • 7
    Keywords: OPTIMIZATION ; radiotherapy ; evaluation ; Germany ; human ; PROSTATE ; ALGORITHM ; imaging ; SYSTEM ; VISUALIZATION ; meningioma ; NUCLEAR-MEDICINE ; TIME ; PATIENT ; QUALITY ; REDUCTION ; treatment ; FORM ; ELEMENT ; NO ; TRIAL ; RADIATION-THERAPY ; DATABASE ; BEAM ; INTENSITY-MODULATED RADIOTHERAPY ; sensitivity ; nuclear medicine ; IMRT ; radiology ; ONCOLOGY ; PROGRAM ; sensitivity analysis ; RE ; REAL-TIME ; interaction ; intensity modulated radiotherapy ; analysis ; methods ; NUCLEAR ; navigation ; BRACHYTHERAPY ; MEDICINE ; NOV ; user interface ; ERROR ; clinical evaluation ; DOSE OPTIMIZATION ; interactive ; inverse planning system ; multiobjective ; mutticriteria
    Abstract: Background and purpose: Currently, inverse planning for intensity-modulated radiotherapy (IMRT) can be a timeconsuming trial and error process. This is because many planning objectives are inherently contradictory and cannot reach their individual optimum all at the same time. Therefore in clinical practice the potential of IMRT cannot be fully exploited for all patients. Multicriteria (multiobjective) optimization combined with interactive plan navigation is a promising approach to overcome these problems. Patients and methods: We developed a new inverse planning system called "Multicriteria Interactive Radiotherapy Assistant (MIRA)". The optimization result is a database of patient specific, Pareto-optimal plan proposals. The database is explored with an intuitive user interface that utilizes both a new interactive element for plan navigation and familiar dose visualizations in form of DVH and isoclose projections. Two clinical test cases, one paraspinal meningioma case and one prostate case, were optimized using MIRA and compared with the clinically approved planning program KonRad. Results: Generating the databases required no user interaction and took approx. 2-3 h per case. The interactive exploration required only a few minutes until the best plan was identified, resulting in a significant reduction of human planning time. The achievable plan quality was comparable to KonRad with the additional benefit of having plan alternatives at hand to perform a sensitivity analysis or to decide for a different clinical compromise. Conclusions: The MIRA system provides a complete database and interactive exploration of the solution space in real time. Hence, it is ideally suited for the inherently multicriterial problem of inverse IMRT treatment planning. (c) 2007 Elsevier Ireland Ltd. All rights reserved
    Type of Publication: Journal article published
    PubMed ID: 17892901
    Signatur Availability
    BibTip Others were also interested in ...
  • 8
    Keywords: OPTIMIZATION ; Germany ; imaging ; VISUALIZATION ; NUCLEAR-MEDICINE ; TIME ; MOUSE ; IDENTIFICATION ; RADIATION-THERAPY ; INTENSITY-MODULATED RADIOTHERAPY ; treatment planning ; SELECTION ; nuclear medicine ; IMRT ; radiology ; INCREASE ; methods ; NUCLEAR ; ENGLAND ; navigation ; EXCLUSION ; INCREASES ; MEDICINE ; VALUES ; interactions ; OPERATIONS ; interactive ; EFFICIENT SET
    Abstract: Inherently, IMRT treatment planning involves compromising between different planning goals. Multi- criteria IMRT planning directly addresses this compromising and thus makes it more systematic. Usually, several plans are computed from which the planner selects the most promising following a certain procedure. Applying Pareto navigation for this selection step simultaneously increases the variety of planning options and eases the identification of the most promising plan. Pareto navigation is an interactive multi- criteria optimizationmethod that consists of the two navigationmechanisms 'selection' and 'restriction'. The former allows the formulation of wishes whereas the latter allows the exclusion of unwanted plans. They are realized as optimization problems on the so- called plan bundle - a set constructed from pre- computed plans. They can be approximately reformulated so that their solution time is a small fraction of a second. Thus, the user can be provided with immediate feedback regarding his or her decisions. Pareto navigation was implemented in the MIRA navigator software and allows real- time manipulation of the current plan and the set of considered plans. The changes are triggered by simple mouse operations on the so- called navigation star and lead to real- time updates of the navigation star and the dose visualizations. Since any Pareto- optimal plan in the plan bundle can be found with just a few navigation operations the MIRA navigator allows a fast and directed plan determination. Besides, the concept allows for a refinement of the plan bundle, thus offering a middle course between single plan computation and multi- criteria optimization. Pareto navigation offers so far unmatched real- time interactions, ease of use and plan variety, setting it apart from the multi- criteria IMRT planning methods proposed so far
    Type of Publication: Journal article published
    PubMed ID: 18263953
    Signatur Availability
    BibTip Others were also interested in ...
  • 9
    Keywords: OPTIMIZATION ; radiotherapy ; evaluation ; Germany ; imaging ; TOOL ; RISK ; NUCLEAR-MEDICINE ; TARGET ; PARAMETERS ; PREDICTION ; INTENSITY-MODULATED RADIOTHERAPY ; sensitivity ; nuclear medicine ; IMRT ; radiology ; sensitivity analysis ; analysis ; NUCLEAR ; ELASTICITY ; TOOLS ; ENGLAND ; PROGRAMS ; PREDICT ; MEDICINE ; modeling ; modification ; VALUES
    Abstract: The formulation of optimization problems in intensity-modulated radiotherapy (IMRT) planning comprises the choice of various values such as function-specific parameters or constraint bounds. In current inverse planning programs that yield a single treatment plan for each optimization, it is often unclear how strongly these modeling parameters affect the resulting plan. This work investigates the mathematical concepts of elasticity and sensitivity to deal with this problem. An artificial planning case with a horse-shoe formed target with different opening angles surrounding a circular risk structure is studied. As evaluation functions the generalized equivalent uniform dose (EUD) and the average underdosage below and average overdosage beyond certain dose thresholds are used. A single IMRT plan is calculated for an exemplary parameter configuration. The elasticity and sensitivity of each parameter are then calculated without re-optimization, and the results are numerically verified. The results show the following. ( 1) elasticity can quantify the influence of a modeling parameter on the optimization result in terms of how strongly the objective function value varies under modifications of the parameter value. It also can describe how strongly the geometry of the involved planning structures affects the optimization result. ( 2) Based on the current parameter settings and corresponding treatment plan, sensitivity analysis can predict the optimization result for modified parameter values without re-optimization, and it can estimate the value intervals in which such predictions are valid. In conclusion, elasticity and sensitivity can provide helpful tools in inverse IMRT planning to identify the most critical parameters of an individual planning problem and to modify their values in an appropriate way
    Type of Publication: Journal article published
    PubMed ID: 18711248
    Signatur Availability
    BibTip Others were also interested in ...
  • 10
    Keywords: OPTIMIZATION ; tumor ; Germany ; ALGORITHMS ; imaging ; TOOL ; TIME ; treatment ; SURFACE ; INTENSITY-MODULATED RADIOTHERAPY ; IMRT ; radiology ; PHASE ; TIMES ; RANGE
    Abstract: One approach to multi-criteria IMRT planning is to automatically calculate a data set of Pareto-optimal plans for a given planning problem in a first phase, and then interactively explore the solution space and decide on the clinically best treatment plan in a second phase. The challenge of computing the plan data set is to ensure that all clinically meaningful plans are covered and that as many clinically irrelevant plans as possible are excluded to keep computation times within reasonable limits. In this work, we focus on the approximation of the clinically relevant part of the Pareto surface, the process that constitutes the first phase. It is possible that two plans on the Pareto surface have a small, clinically insignificant difference in one criterion and a significant difference in another criterion. For such cases, only the plan that is clinically clearly superior should be included into the data set. To achieve this during the Pareto surface approximation, we propose to introduce bounds that restrict the relative quality between plans, the so-called trade-off bounds. We show how to integrate these trade-off bounds into the approximation scheme and study their effects. The proposed scheme is applied to two artificial cases and one clinical case of a paraspinal tumor. For all cases, the quality of the Pareto surface approximation is measured with respect to the number of computed plans, and the range of values occurring in the approximation for different criteria is compared. Through enforcing trade-off bounds, the scheme disregards clinically irrelevant plans during the approximation. Thereby, the number of plans necessary to achieve a good approximation quality can be significantly reduced. Thus, trade-off bounds are an effective tool to focus the planning and to reduce computation time
    Type of Publication: Journal article published
    PubMed ID: 19809122
    Signatur Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...