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A fast multitarget inverse treatment planning strategy optimizing dosimetric measures for high‐dose‐rate (HDR) brachytherapy
Author(s) -
Guthier Christian V.,
Damato Antonio L.,
Viswanathan Akila N.,
Hesser Juergen W.,
Cormack Robert A.
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12410
Subject(s) - brachytherapy , radiation treatment planning , histogram , dwell time , dosimetry , computer science , optimization problem , inverse , mathematical optimization , mathematics , nuclear medicine , algorithm , medical physics , medicine , radiation therapy , artificial intelligence , radiology , image (mathematics) , clinical psychology , geometry
Purpose In this study, we introduce a novel, fast, inverse treatment planning strategy for interstitial high‐dose‐rate ( HDR ) brachytherapy with multiple regions of interest solely based on dose‐volume‐histogram‐related dosimetric measures ( DM s). Methods We present a new problem formulation of the objective function that approximates the indicator variables of the standard DM optimization problem with a smooth logistic function. This problem is optimized by standard gradient‐based methods. The proposed approach is then compared against state‐of‐the‐art optimization strategies. Results All generated plans fulfilled prescribed DM s for all organs at risk. Compared to clinical practice, a statistically significant improvement ( p = 0.01 ) in coverage of target structures was achieved. Simultaneously, DM s representing high‐dose regions were significantly reduced ( p = 0.01 ) . The novel optimization strategies run‐time was (0.8 ± 0.3) s and thus outperformed the best competing strategies of the state of the art. In addition, the novel DM ‐based approach was associated with a statistically significant ( p = 0.01 ) increase in the number of active dwell positions and a decrease in the maximum dwell time. Conclusions The generated plans showed a clinically significant increase in target coverage with fewer hot spots, with an optimization time approximately three orders of magnitude shorter than manual optimization currently used in clinical practice. As optimization is solely based on DM s, intuitive, interactive, real‐time treatment planning, which motivated the adoption of manual optimization in our clinic, is possible.