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3 February 2020 Automatic generation of three-dimensional dose reconstruction data for two-dimensional radiotherapy plans for historically treated patients
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Abstract

Performing large-scale three-dimensional radiation dose reconstruction for patients requires a large amount of manual work. We present an image processing-based pipeline to automatically reconstruct radiation dose. The pipeline was designed for childhood cancer survivors that received abdominal radiotherapy with anterior-to-posterior and posterior-to-anterior field set-up. First, anatomical landmarks are automatically identified on two-dimensional radiographs. Second, these landmarks are used to derive parameters to emulate the geometry of the plan on a surrogate computed tomography. Finally, the plan is emulated and used as input for dose calculation. For qualitative evaluation, 100 cases of automatic and manual plan emulations were assessed by two experienced radiation dosimetrists in a blinded comparison. The two radiation dosimetrists approved 100%/100% and 92%/91% of the automatic/manual plan emulations, respectively. Similar approval rates of 100% and 94% hold when the automatic pipeline is applied on another 50 cases. Further, quantitative comparisons resulted in on average <5  mm difference in plan isocenter/borders, and <0.9  Gy in organ mean dose (prescribed dose: 14.4 Gy) calculated from the automatic and manual plan emulations. No statistically significant difference in terms of dose reconstruction accuracy was found for most organs at risk. Ultimately, our automatic pipeline results are of sufficient quality to enable effortless scaling of dose reconstruction data generation.

© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2020/$28.00 © 2020 SPIE
Ziyuan Wang, Marco Virgolin, Peter A. N. Bosman, Koen F. Crama, Brian V. Balgobind, Arjan Bel, and Tanja Alderliesten "Automatic generation of three-dimensional dose reconstruction data for two-dimensional radiotherapy plans for historically treated patients," Journal of Medical Imaging 7(1), 015001 (3 February 2020). https://doi.org/10.1117/1.JMI.7.1.015001
Received: 8 October 2019; Accepted: 17 January 2020; Published: 3 February 2020
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