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 0.9mm (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 12mm resulted in radial errors with a mean value of 1.8mm and a standard deviation of 1.1mm (median 1.6mm, maximum 4.7mm).
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.
Written by:
Hoegele W, Loeschel R, Dobler B, Koelbl O, Beard C, Zygmanski P. Are you the author?
Regensburg University Medical Center, Department of Radiation Oncology, Regensburg, Germany; Regensburg University of Applied Sciences, Department of Computer Science and Mathematics, Regensburg, Germany.
Reference: Radiother Oncol. 2013 Feb 7. pii: S0167-8140(13)00009-1.
doi: 10.1016/j.radonc.2013.01.005
PubMed Abstract
PMID: 23395068
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