Surface scanning for 3D dose calculation in intraoperative electron radiation therapy

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BioMed Central Ltd. Part of Springer Nature.
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Background: Dose calculations in intraoperative electron radiation therapy (IOERT) rely on the conventional assumption of water-equivalent tissues at the applicator end, which defines a flat irradiation surface. However, the shape of the irradiation surface modifies the dose distribution. Our study explores, for the first time, the use of surface scanning methods for three-dimensional dose calculation of IOERT. Methods: Two different three-dimensional scanning technologies were evaluated in a simulated IOERT scenario: a tracked conoscopic holography sensor (ConoProbe) and a structured-light three-dimensional scanner (Artec). Dose distributions obtained from computed tomography studies of the surgical field (gold standard) were compared with those calculated under the conventional assumption or from pseudo-computed tomography studies based on surfaces. Results: In the simulated IOERT scenario, the conventional assumption led to an average gamma pass rate of 39.9% for dose values greater than 10% (two configurations, with and without blood in the surgical field). Results improved when considering surfaces in the dose calculation (88.5% for ConoProbe and 92.9% for Artec). Conclusions: More accurate three-dimensional dose distributions were obtained when considering surfaces in the dose calculation of the simulated surgical field. The structured-light three-dimensional scanner provided the best results in terms of dose distributions. The findings obtained in this specific experimental setup warrant further research on surface scanning in the IOERT context owing to the clinical interest of improving the documentation of the actual IOERT scenario.
IOERT, Intraoperative radiotherapy, Surface scanning, Conoscopic holography, Structured-light 3D scanner, Dose distribution, In-vivo dosimetry, Conoscopic holography, Radiochromic films, Gafchromic films, Beam, Radiotherapy, Feasibility
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Radiation oncology, 13, Article ID 243, Dec. 2018, 12 pp.