@article{GroegerHautmannLoescheletal., author = {Gr{\"o}ger, Christian and Hautmann, Matthias G. and L{\"o}schel, Rainer and Repp, Natalia and K{\"o}lbl, Oliver and Dobler, Barbara}, title = {Re-irradiation of spinal column metastases by IMRT: Impact of setup errors on the dose distribution}, series = {Radiation Oncology}, volume = {8}, journal = {Radiation Oncology}, number = {269}, publisher = {BMC, Part of Springer Nature}, doi = {10.1186/1748-717X-8-269}, abstract = {Background This study investigates the impact of an automated image guided patient setup correction on the dose distribution for ten patients with in-field IMRT re-irradiation of vertebral metastases. Methods 10 patients with spinal column metastases who had previously been treated with 3D-conformal radiotherapy (3D-CRT) were simulated to have an in-field recurrence. IMRT plans were generated for treatment of the vertebrae sparing the spinal cord. The dose distributions were compared for a patient setup based on skin marks only and a Cone Beam CT (CBCT) based setup with translational and rotational couch corrections using an automatic robotic image guided couch top (Elekta - HexaPOD™ IGuide® - system). The biological equivalent dose (BED) was calculated to evaluate and rank the effects of the automatic setup correction for the dose distribution of CTV and spinal cord. Results The mean absolute value (± standard deviation) over all patients and fractions of the translational error is 6.1 mm (±4 mm) and 2.7° (±1.1 mm) for the rotational error. The dose coverage of the 95\% isodose for the CTV is considerable decreased for the uncorrected table setup. This is associated with an increasing of the spinal cord dose above the tolerance dose. Conclusions An automatic image guided table correction ensures the delivery of accurate dose distribution and reduces the risk of radiation induced myelopathy.}, language = {en} } @article{HaertlLoeschelReppetal., author = {Haertl, Petra Maria and L{\"o}schel, Rainer and Repp, Natalia and Pohl, Fabian and K{\"o}lbl, Oliver and Dobler, Barbara}, title = {Frameless fractionated stereotactic radiation therapy of intracranial lesions: Impact of cone beam CT based setup correction on dose distribution}, series = {Radiation Oncology}, volume = {8}, journal = {Radiation Oncology}, number = {1}, publisher = {BioMed Central}, address = {London}, doi = {10.1186/1748-717X-8-153}, abstract = {Background The purpose of this study was to evaluate the impact of Cone Beam CT (CBCT) based setup correction on total dose distributions in fractionated frameless stereotactic radiation therapy of intracranial lesions. Methods Ten patients with intracranial lesions treated with 30 Gy in 6 fractions were included in this study. Treatment planning was performed with Oncentra® for a SynergyS® (Elekta Ltd, Crawley, UK) linear accelerator with XVI® Cone Beam CT, and HexaPOD™ couch top. Patients were immobilized by thermoplastic masks (BrainLab, Reuther). After initial patient setup with respect to lasers, a CBCT study was acquired and registered to the planning CT (PL-CT) study. Patient positioning was corrected according to the correction values (translational, rotational) calculated by the XVI® system. Afterwards a second CBCT study was acquired and registered to the PL-CT to confirm the accuracy of the corrections. An in-house developed software was used for rigid transformation of the PL-CT to the CBCT geometry, and dose calculations for each fraction were performed on the transformed CT. The total dose distribution was achieved by back-transformation and summation of the dose distributions of each fraction. Dose distributions based on PL-CT, CBCT (laser set-up), and final CBCT were compared to assess the influence of setup inaccuracies. Results The mean displacement vector, calculated over all treatments, was reduced from (4.3 ± 1.3) mm for laser based setup to (0.5 ± 0.2) mm if CBCT corrections were applied. The mean rotational errors around the medial-lateral, superior-inferior, anterior-posterior axis were reduced from (-0.1 ± 1.4)°, (0.1 ± 1.2)° and (-0.2 ± 1.0)°, to (0.04 ± 0.4)°, (0.01 ± 0.4)° and (0.02 ± 0.3)°. As a consequence the mean deviation between planned and delivered dose in the planning target volume (PTV) could be reduced from 12.3\% to 0.4\% for D95 and from 5.9\% to 0.1\% for Dav. Maximum deviation was reduced from 31.8\% to 0.8\% for D95, and from 20.4\% to 0.1\% for Dav. Conclusion Real dose distributions differ substantially from planned dose distributions, if setup is performed according to lasers only. Thermoplasic masks combined with a daily CBCT enabled a sufficient accuracy in dose distribution.}, language = {en} } @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{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} }