Stochastic formulation of patient positioning using linac-mounted cone beam imaging with prior knowledge
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 unc...
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| Hauptverfasser: | , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
10 January 2011
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| In: |
Medical physics
Year: 2011, Jahrgang: 38, Heft: 2, Pages: 668-681 |
| ISSN: | 2473-4209 |
| DOI: | 10.1118/1.3532959 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1118/1.3532959 Verlag, lizenzpflichtig, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1118/1.3532959 
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| Verfasserangaben: | W. Hoegele, R. Loeschel, B. Dobler, J. Hesser, O. Koelbl, P. Zygmanski |
MARC
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| 100 | 1 | |a Högele, Wolfgang |d 1984- |e VerfasserIn |0 (DE-588)1041247168 |0 (DE-627)766696316 |0 (DE-576)392935317 |4 aut | |
| 245 | 1 | 0 | |a Stochastic formulation of patient positioning using linac-mounted cone beam imaging with prior knowledge |c W. Hoegele, R. Loeschel, B. Dobler, J. Hesser, O. Koelbl, P. Zygmanski |
| 264 | 1 | |c 10 January 2011 | |
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| 520 | |a 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 maximuma 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. | ||
| 650 | 4 | |a Anatomy | |
| 650 | 4 | |a and statistics | |
| 650 | 4 | |a Bayes methods | |
| 650 | 4 | |a biomechanics | |
| 650 | 4 | |a Cancer | |
| 650 | 4 | |a Computed tomography | |
| 650 | 4 | |a computerised tomography | |
| 650 | 4 | |a Conformal radiation treatment | |
| 650 | 4 | |a deformation | |
| 650 | 4 | |a diagnostic radiography | |
| 650 | 4 | |a estimation | |
| 650 | 4 | |a feature extraction | |
| 650 | 4 | |a IGRT | |
| 650 | 4 | |a Image registration | |
| 650 | 4 | |a inverse problems | |
| 650 | 4 | |a Inverse problems | |
| 650 | 4 | |a maximum a posteriori | |
| 650 | 4 | |a maximum likelihood estimation | |
| 650 | 4 | |a Mechanical and electrical properties of tissues and organs | |
| 650 | 4 | |a medical image processing | |
| 650 | 4 | |a Medical image reconstruction | |
| 650 | 4 | |a Medical imaging | |
| 650 | 4 | |a patient positioning | |
| 650 | 4 | |a phantoms | |
| 650 | 4 | |a plasticity | |
| 650 | 4 | |a Plasticity | |
| 650 | 4 | |a Probability theory | |
| 650 | 4 | |a radiation therapy | |
| 650 | 4 | |a Radiography | |
| 650 | 4 | |a setup error | |
| 650 | 4 | |a Stochastic analysis | |
| 650 | 4 | |a stochastic processes | |
| 700 | 1 | |a Loeschel, R. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Dobler, B. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Hesser, Jürgen |d 1964- |e VerfasserIn |0 (DE-588)1020647353 |0 (DE-627)691291071 |0 (DE-576)361513739 |4 aut | |
| 700 | 1 | |a Koelbl, O. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Zygmanski, P. |e VerfasserIn |4 aut | |
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