@article{HoegeleDoblerKoelbletal., author = {H{\"o}gele, Wolfgang and Dobler, Barbara and K{\"o}lbl, Oliver and Beard, Clair and Zygmanski, Piotr and L{\"o}schel, Rainer}, title = {Stochastic triangulation for prostate positioning during radiotherapy using short CBCT arcs}, series = {Radiotherapy and Oncology}, volume = {106}, journal = {Radiotherapy and Oncology}, number = {2}, publisher = {Elsevier}, doi = {10.1016/j.radonc.2013.01.005}, pages = {241 -- 249}, abstract = {Background and purpose: Fast and reliable tumor localization is an important part of today's radiotherapy utilizing new delivery techniques. This proof-of-principle study demonstrates the use of a method called herein 'stochastic triangulation' for this purpose. Stochastic triangulation uses very short imaging arcs and a few projections. Materials and methods: A stochastic Maximum A Posteriori (MAP) estimator is proposed based on an uncertainty-driven model of the acquisition geometry and inter-/intra-fractional deformable anatomy. The application of this method was designed to use the available linac-mounted cone-beam computed tomography (CBCT) and/or electronic portal imaging devices (EPID) for the patient setup based on short imaging arcs. For the proof-of-principle clinical demonstration, the MAP estimator was applied to 5 CBCT scans of a prostate cancer patient with 2 implanted gold markers. Estimation was performed for several (18) very short imaging arcs of 5° with 10 projections resulting in 90 estimations. Results: Short-arc stochastic triangulation led to residual radial errors compared to manual inspection with a mean value of 1.4mm and a standard deviation of 0.9 mm (median 1.2mm, maximum 3.8mm) averaged over imaging directions all around the patient. Furthermore, abrupt intra-fractional motion of up to 10mm resulted in radial errors with a mean value of 1.8mm and a standard deviation of 1.1mm (median 1.5mm, maximum 5.6mm). Slow periodic intra-fractional motions in the range of 12 mm resulted in radial errors with a mean value of 1.8mm and a standard deviation of 1.1mm (median 1.6mm, maximum 4.7 mm). Conclusion: Based on this study, the proposed stochastic method is fast, robust and can be used for inter- as well as intra-fractional target localization using current CBCT units.}, language = {en} } @article{HoegeleZygmanskiDobleretal., author = {H{\"o}gele, Wolfgang and Zygmanski, Piotr and Dobler, Barbara and Kroiss, M. and K{\"o}lbl, Oliver and L{\"o}schel, Rainer}, title = {Localization of deformable tumors from short-arc projections using Bayesian estimation}, series = {Medical Physics}, volume = {39}, journal = {Medical Physics}, number = {12}, publisher = {Wiley}, doi = {10.1118/1.4764483}, pages = {7205 -- 7214}, abstract = {Purpose: The authors present a stochastic framework for radiotherapy patient positioning directly utilizing radiographic projections. This framework is developed to be robust against anatomical nonrigid deformations and to cope with challenging imaging scenarios, involving only a few cone beam CT projections from short arcs. Methods: Specifically, a Bayesian estimator (BE) is explicitly derived for the given scanning geometry. This estimator is compared to reference methods such as chamfer matching (CM) and the minimization of the median absolute error adapted as tools of robust image processing and statistics. In order to show the performance of the stochastic short-arc patient positioning method, a CIRS IMRT thorax phantom study is presented with movable markers and the utilization of an Elekta Synergy® XVI system. Furthermore, a clinical prostate CBCT scan of a Varian® On-Board Imager® system is utilized to investigate the robustness of the method for large variations of image quality (anterior-posterior vs lateral views). Results: The results show that the BE shifts reduce the initial setup error of up to 3 cm down to 3 mm at maximum for an imaging arc as short as 10° while CM achieves residual errors of 7 mm at maximum only for arcs longer than 40°. Furthermore, the BE can compensate robustly for low image qualities using several low quality projections simultaneously. Conclusions: In conclusion, an estimation method for marker-based patient positioning for short imaging arcs is presented and shown to be robust and accurate for deformable anatomies.}, language = {en} } @article{HoegeleLoeschelDobleretal., author = {H{\"o}gele, Wolfgang and L{\"o}schel, Rainer and Dobler, Barbara and K{\"o}lbl, Oliver and Zygmanski, Piotr}, title = {Bayesian estimation applied to stochastic localization with constraints due to interfaces and boundaries}, series = {Mathematical Problems in Engineering}, volume = {213}, journal = {Mathematical Problems in Engineering}, publisher = {HIndawi}, issn = {1563-5147}, doi = {10.1155/2013/960421}, pages = {17}, abstract = {Purpose We present a systematic Bayesian formulation of the stochastic localization/triangulation problem close to constraining interfaces. Methods For this purpose, the terminology of Bayesian estimation is summarized suitably for applied researchers including the presentation of Maximum Likelihood (ML), Maximum A Posteriori (MAP), and Minimum Mean Square Error (MMSE) estimation. Explicit estimators for triangulation are presented for the linear 2D parallel beam and the nonlinear 3D cone beam model. The priors in MAP and MMSE optionally incorporate (A) the hard constraints about the interface and (B) knowledge about the probability of the object with respect to the interface. All presented estimators are compared in several simulation studies for live acquisition scenarios with 10,000 samples each. Results First, the presented application shows that MAP and MMSE perform considerably better, leading to lower Root Mean Square Errors (RMSEs) in the simulation studies compared to the ML approach by typically introducing a bias. Second, utilizing priors including (A) and (B) is very beneficial compared to just including (A). Third, typically MMSE leads to better results than MAP, by the cost of significantly higher computational effort. Conclusion Depending on the specific application and prior knowledge, MAP and MMSE estimators strongly increase the estimation accuracy for localization close to interfaces.}, language = {en} } @article{HoegeleLoeschelDobleretal., author = {Hoegele, W. and L{\"o}schel, Rainer and Dobler, Barbara and Hesser, J. and K{\"o}lbl, Oliver and Zygmanski, Piotr}, title = {Stochastic formulation of patient positioning using linac-mounted cone beam imaging with prior knowledge}, series = {Medical physics}, volume = {38}, journal = {Medical physics}, number = {2}, publisher = {American Association of Physicists in Medicine}, doi = {10.1118/1.3532959}, pages = {668 -- 681}, abstract = {PURPOSE In this work, a novel stochastic framework for patient positioning based on linac-mounted CB projections is introduced. Based on this formulation, the most probable shifts and rotations of the patient are estimated, incorporating interfractional deformations of patient anatomy and other uncertainties associated with patient setup. METHODS The target position is assumed to be defined by and is stochastically determined from positions of various features such as anatomical landmarks or markers in CB projections, i.e., radiographs acquired with a CB-CT system. The patient positioning problem of finding the target location from CB projections is posed as an inverse problem with prior knowledge and is solved using a Bayesian maximum a posteriori (MAP) approach. The prior knowledge is three-fold and includes the accuracy of an initial patient setup (such as in-room laser and skin marks), the plasticity of the body (relative shifts between target and features), and the feature detection error in CB projections (which may vary depending on specific detection algorithm and feature type). For this purpose, MAP estimators are derived and a procedure of using them in clinical practice is outlined. Furthermore, a rule of thumb is theoretically derived, relating basic parameters of the prior knowledge (initial setup accuracy, plasticity of the body, and number of features) and the parameters of CB data acquisition (number of projections and accuracy of feature detection) to the expected estimation accuracy. RESULTS MAP estimation can be applied to arbitrary features and detection algorithms. However, to experimentally demonstrate its applicability and to perform the validation of the algorithm, a water-equivalent, deformable phantom with features represented by six 1 mm chrome balls were utilized. These features were detected in the cone beam projections (XVI, Elekta Synergy) by a local threshold method for demonstration purposes only. The accuracy of estimation (strongly varying for different plasticity parameters of the body) agreed with the rule of thumb formula. Moreover, based on this rule of thumb formula, about 20 projections for 6 detectable features seem to be sufficient for a target estimation accuracy of 0.2 cm, even for relatively large feature detection errors with standard deviation of 0.5 cm and spatial displacements of the features with standard deviation of 0.5 cm. CONCLUSIONS The authors have introduced a general MAP-based patient setup algorithm accounting for different sources of uncertainties, which are utilized as the prior knowledge in a transparent way. This new framework can be further utilized for different clinical sites, as well as theoretical developments in the field of patient positioning for radiotherapy.}, language = {en} } @article{HoegeleLoeschelMerkleetal., author = {Hoegele, W. and L{\"o}schel, Rainer and Merkle, N. and Zygmanski, Piotr}, title = {An efficient inverse radiotherapy planning method for VMAT using quadratic programming optimization}, series = {Medical physics}, volume = {39}, journal = {Medical physics}, number = {1}, publisher = {American Association of Physicists in Medicine}, doi = {10.1118/1.3671922}, pages = {444 -- 454}, abstract = {PURPOSE The purpose of this study is to investigate the feasibility of an inverse planning optimization approach for the Volumetric Modulated Arc Therapy (VMAT) based on quadratic programming and the projection method. The performance of this method is evaluated against a reference commercial planning system (eclipse(TM) for rapidarc(TM)) for clinically relevant cases. METHODS The inverse problem is posed in terms of a linear combination of basis functions representing arclet dose contributions and their respective linear coefficients as degrees of freedom. MLC motion is decomposed into basic motion patterns in an intuitive manner leading to a system of equations with a relatively small number of equations and unknowns. These equations are solved using quadratic programming under certain limiting physical conditions for the solution, such as the avoidance of negative dose during optimization and Monitor Unit reduction. The modeling by the projection method assures a unique treatment plan with beneficial properties, such as the explicit relation between organ weightings and the final dose distribution. Clinical cases studied include prostate and spine treatments. The optimized plans are evaluated by comparing isodose lines, DVH profiles for target and normal organs, and Monitor Units to those obtained by the clinical treatment planning system eclipse(TM). RESULTS The resulting dose distributions for a prostate (with rectum and bladder as organs at risk), and for a spine case (with kidneys, liver, lung and heart as organs at risk) are presented. Overall, the results indicate that similar plan qualities for quadratic programming (QP) and rapidarc(TM) could be achieved at significantly more efficient computational and planning effort using QP. Additionally, results for the quasimodo phantom [Bohsung et al., "IMRT treatment planning: A comparative inter-system and inter-centre planning exercise of the estro quasimodo group," Radiother. Oncol. 76(3), 354-361 (2005)] are presented as an example for an extreme concave case. CONCLUSION Quadratic programming is an alternative approach for inverse planning which generates clinically satisfying plans in comparison to the clinical system and constitutes an efficient optimization process characterized by uniqueness and reproducibility of the solution.}, language = {en} } @article{HoegeleLoeschelZygmanski, author = {Hoegele, W. and L{\"o}schel, Rainer and Zygmanski, Piotr}, title = {An alternative VMAT with prior knowledge about the type of leaf motion utilizing projection method for concave targets}, series = {Medical physics}, volume = {36}, journal = {Medical physics}, number = {8}, publisher = {American Association of Physicists in Medicine}, doi = {10.1118/1.3173815}, pages = {3764 -- 3774}, abstract = {The authors present an alternative approach to inverse planning optimization and apply it to volumetric modulated are therapy (VMAT) in one rotation with a prior knowledge about the type of leaf motions. The optimization is based on the projection theorem in inner product spaces. MLC motion is directly considered in the optimization, thus avoiding leaf segmentation characteristic of IMRT optimization. In this work they realize the method for concave irregular targets encompassing an organ at risk leading to a repetitive MLC motion pattern. Applying the projection theorem leads to a noniterative optimization method and reduces to solving few systems of linear equations with small numbers of dimensions. The solution of the inverse problem is unique, and false minima are naturally excluded. They divided the full rotation into about 50 short arc segments and for each segment decomposed dose into separate contributions related to stages of MLC motion. This results generally in an inverse problem with just four free parameters per arc segment. Practically three degrees of freedom will be used for the purpose of a constant angular speed of the gantry. Therefore the total number of degrees of freedom for a 3D problem is about 3 x 50 x number of collimator leaf pairs for irradiating the whole target volume in one rotation. Two 2D and one 3D concave target volumes are applied for a slice by slice optimization. A 6 MV photon beam model is used, including realistic scattering and attenuation, and a maximal leaf velocity of 3 cm/s is regarded. The resulting dose distributions cover the PTVs very well and have maxima at about 108\% of dose in the PTVs. The OAR is spared very strong in all cases. As a result of optimization, the MLC apertures are repetitively opening and closing and can be interpreted in an intuitive way. Applying the projection method for this knowledge-based VMAT delivery scheme for concave target volumes is an alternative technique for dose optimization. There are several properties, such as uniqueness of MLC motions and their continuous dependence on geometry and prescribed dose, that make this approach interesting to inverse planning. This method is still in an investigational stage, but promising results are presented. In future work it will be extended directly (without conceptual changes) in several directions to be more clinically applicable.}, language = {en} } @article{ZygmanskiHoegeleCormacketal., author = {Zygmanski, Piotr and H{\"o}gele, Wolfgang and Cormack, Robert and Chin, Lee and L{\"o}schel, Rainer}, title = {A volumetric-modulated arc therapy using sub-conformal dynamic arc with a monotonic dynamic multileaf collimator modulation}, series = {Physics in medicine and biology}, volume = {53}, journal = {Physics in medicine and biology}, number = {22}, publisher = {IOP Publishing}, doi = {10.1088/0031-9155/53/22/009}, pages = {6395 -- 6417}, abstract = {We present a new dose delivery scheme utilizing sub-conformal dynamic arc (sub-CD-ARC) with a dynamic multileaf collimator (dMLC) modulation in a single rotation. In sub-conformal delivery, a MLC aperture conforms not to the whole target but to its portion, simultaneously completely avoiding organs at risk (OARs). In CD-ARC therapy, the dose deposition level depends on the target width and distance to the axis of rotation, and therefore the use of multiple arcs is necessary to achieve a uniform dose within the target in 3D. In our delivery scheme, the dose deposition variations symptomatic of non-modulating sub-CD-ARC are compensated for in 3D by a quasi-periodic monotonic dMLC modulation. For this reason, we call this scheme sub-conformal dynamic modulated arc (sub-CD-MARC) therapy. The advantage of using such a modulation is that MLC leaf motion has just a few control points, and that an inverse planning problem is reduced to a linear equation problem with a few unknown parameters, which have clear physical meaning. We show the general dosimetric properties of sub-CD-ARC and sub-CD-MARC for a specific geometry of the target and OARs (rotational symmetry with varying inner and outer radii along the axis of rotation). In addition, we present numerical results of sub-CD-MARC inverse planning optimization.}, language = {en} }