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  • ALGORITHM  (8)
  • 1
    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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  • 2
    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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  • 3
    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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  • 4
    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
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  • 5
    Keywords: CANCER ; radiotherapy ; tumor ; COMBINATION ; Germany ; LUNG ; PROSTATE ; ALGORITHM ; CT ; imaging ; INFORMATION ; lung cancer ; LUNG-CANCER ; MASK ; TISSUE ; TIME ; PATIENT ; COMPLEX ; COMPLEXES ; CONTRAST ; treatment ; TARGET ; ACQUISITION ; EXPERIENCE ; VECTOR ; NUMBER ; prostate cancer ; PROSTATE-CANCER ; REGISTRATION ; BEAM ; DELIVERY ; HEAD ; CANCER-PATIENTS ; MULTILEAF COLLIMATOR ; treatment planning ; BODY ; CANCER PATIENTS ; LINEAR-ACCELERATOR ; RECONSTRUCTION ; IMRT ; PATIENT FIXATION ; IMPLEMENTATION ; INCREASE ; chordoma ; LEVEL ; methods ; fractionated stereotactic radiotherapy ; technique ; MUTUAL INFORMATION ; cancer research ; cone beam CT ; LANDMARK ; INCREASES ; CLINICAL IMPLEMENTATION ; ACCELERATOR ; WORKLOAD
    Abstract: ABSTRACT: BACKGROUND: The purpose of the study was the clinical implementation of a kV cone beam CT (CBCT) for setup correction in radiotherapy. PATIENTS AND METHODS: For evaluation of the setup correction workflow, six tumor patients (lung cancer, sacral chordoma, head-and-neck and paraspinal tumor, and two prostate cancer patients) were selected. All patients were treated with fractionated stereotactic radiotherapy, five of them with intensity modulated radiotherapy (IMRT). For patient fixation, a scotch cast body frame or a vacuum pillow, each in combination with a scotch cast head mask, were used. The imaging equipment, consisting of an x-ray tube and a flat panel imager (FPI), was attached to a Siemens linear accelerator according to the in-line approach, i.e. with the imaging beam mounted opposite to the treatment beam sharing the same isocenter. For dose delivery, the treatment beam has to traverse the FPI which is mounted in the accessory tray below the multi-leaf collimator. For each patient, a predefined number of imaging projections over a range of at least 200 degrees were acquired. The fast reconstruction of the 3D-CBCT dataset was done with an implementation of the Feldkamp-David-Kress (FDK) algorithm. For the registration of the treatment planning CT with the acquired CBCT, an automatic mutual information matcher and manual matching was used. RESULTS AND DISCUSSION: Bony landmarks were easily detected and the table shifts for correction of setup deviations could be automatically calculated in all cases. The image quality was sufficient for a visual comparison of the desired target point with the isocenter visible on the CBCT. Soft tissue contrast was problematic for the prostate of an obese patient, but good in the lung tumor case. The detected maximum setup deviation was 3 mm for patients fixated with the body frame, and 6 mm for patients positioned in the vacuum pillow. Using an action level of 2 mm translational error, a target point correction was carried out in 4 cases. The additional workload of the described workflow compared to a normal treatment fraction led to an extra time of about 10-12 minutes, which can be further reduced by streamlining the different steps. CONCLUSION: The cone beam CT attached to a LINAC allows the acquisition of a CT scan of the patient in treatment position directly before treatment. Its image quality is sufficient for determining target point correction vectors. With the presented workflow, a target point correction within a clinically reasonable time frame is possible. This increases the treatment precision, and potentially the complex patient fixation techniques will become dispensable
    Type of Publication: Journal article published
    PubMed ID: 16723023
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  • 6
    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
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  • 7
    Keywords: radiotherapy ; Germany ; THERAPY ; ALGORITHM ; CT ; DIAGNOSIS ; IMAGES ; TOOL ; RISK ; RESOLUTION ; validation ; radiation ; TIME ; PATIENT ; IMPACT ; DOMAIN ; treatment ; FREQUENCY ; FREQUENCIES ; RADIATION-THERAPY ; REGISTRATION ; treatment planning ; THIN-PLATE SPLINES ; rectum ; DEFORMATIONS ; elastic registration ; correlation ; MUTUAL INFORMATION ; NONRIGID REGISTRATION ; SET ; COEFFICIENTS ; LANDMARK ; elastic image registration ; adaptive radiotherapy ; BRAIN SHIFT ; CORRELATION-COEFFICIENT ; DEFORMABLE IMAGE REGISTRATION ; MAXIMIZATION
    Abstract: Image registration has many medical applications in diagnosis, therapy planning and therapy. Especially for time-adaptive radiotherapy, an efficient and accurate elastic registration of images acquired for treatment planning, and at the time of the actual treatment, is highly desirable. Therefore, we developed a fully automatic and fast block matching algorithm which identifies a set of anatomical landmarks in a 3D CT dataset and relocates them in another CT dataset by maximization of local correlation coefficients in the frequency domain. To transform the complete dataset, a smooth interpolation between the landmarks is calculated by modified thin-plate splines with local impact. The concept of the algorithm allows separate processing of image discontinuities like temporally changing air cavities in the intestinal track or rectum. The result is a fully transformed 3D planning dataset (planning CT as well as delineations of tumour and organs at risk) to a verification CT, allowing evaluation and, if necessary, changes of the treatment plan based on the current patient anatomy without time-consuming manual re-contouring. Typically the total calculation time is less than 5min, which allows the use of the registration tool between acquiring the verification images and delivering the dose fraction for online corrections. We present verifications of the algorithm for five different patient datasets with different tumour locations ( prostate, paraspinal and head-and-neck) by comparing the results with manually selected landmarks, visual assessment and consistency testing. It turns out that the mean error of the registration is better than the voxel resolution (2 x 2 x 3 mm(3)). In conclusion, we present an algorithm for fully automatic elastic image registration that is precise and fast enough for online corrections in an adaptive fractionated radiation treatment course
    Type of Publication: Journal article published
    PubMed ID: 16985271
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  • 8
    Keywords: CANCER ; IRRADIATION ; radiotherapy ; COMBINATION ; Germany ; THERAPY ; ALGORITHM ; CT ; QUANTIFICATION ; VOLUME ; ACCURACY ; radiation ; PATIENT ; COMPLEX ; COMPLEXES ; treatment ; RADIATION-THERAPY ; PROSTATE-CANCER ; MODULATION ; DELIVERY ; HEAD ; NECK ; COMPUTED-TOMOGRAPHY ; treatment planning ; LINEAR-ACCELERATOR ; DEVICES ; PATIENT FIXATION ; technique ; UNIT ; cancer research ; EXTENT ; adaptation ; SCANS ; MOVEMENTS ; ACCELERATOR
    Abstract: Modern radiotherapy techniques such as intensity modulation are capable of generating complex dose distributions whose high dose areas tightly conform to the tumour target volume, sparing critical organs even when they are located in close proximity. This potential can only be exploited to its full extent when the accumulated dose actually delivered over the complete treatment course is sufficiently close to the dose computed on the initial CT scan used for treatment planning. Exact patient repositioning is mandatory, but also other sources of error, e.g. changes of the patient's anatomy under therapy, should be taken into account. At the German Cancer Research Center, we use a combination of a linear accelerator and a CT scanner installed in one room and sharing the same couch. it allows the quantification and correction of interfractional variations between planning and treatment delivery. in this paper, we describe treatments of prostate, paraspinal and head and neck tumours. All patients were immobilized by customized fixation devices and treated in a stereotactic setup For each patient, frequent CT scans were taken during the treatment course. Each scan was compared with the original planning CT using manual checks and automatic rigid matching algorithms. Depending on the individual case, the adaptation to variations was carried out offline after several fractions or in real-time between the CT scan and linac irradiation. We discuss the techniques for detecting and correcting interfractional errors and outline the procedural steps of a linac-CT scanner-supported radiation treatment course. (C) 2006 The British Institute of Radiology
    Type of Publication: Journal article published
    PubMed ID: 16980687
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