A new optimization method using a compressed sensing inspired solver for real-time LDR-brachytherapy treatment planning

• Verfasst von: Guthier, Christian Vinzenz [VerfasserIn]
• Verfasst von: Aschenbrenner, Katharina P. [VerfasserIn]
• Verfasst von: Bürgy, Daniel [VerfasserIn]
• Verfasst von: Ehmann, Michael [VerfasserIn]
• Verfasst von: Wenz, Frederik [VerfasserIn]
• Verfasst von: Hesser, Jürgen [VerfasserIn]
• Titel: A new optimization method using a compressed sensing inspired solver for real-time LDR-brachytherapy treatment planning
• Verf.angabe: C. Guthier, K.P. Aschenbrenner, D. Buergy, M. Ehmann, F. Wenz and J.W. Hesser
• E-Jahr: 2015
• Jahr: 16 February 2015
• Umfang: 16 S.
• Fussnoten: Gesehen am 04.12.2018
• Titel Quelle: Enthalten in: Physics in medicine and biology
• Ort Quelle: Bristol : IOP Publ., 1956
• Jahr Quelle: 2015
• Band/Heft Quelle: 60(2015), 6, Seite 2179-2194
• ISSN Quelle: 1361-6560
• DOI: doi:10.1088/0031-9155/60/6/2179
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 158478931X

Abstract

This work discusses a novel strategy for inverse planning in low dose rate brachytherapy. It applies the idea of compressed sensing to the problem of inverse treatment planning and a new solver for this formulation is developed. An inverse planning algorithm was developed incorporating brachytherapy dose calculation methods as recommended by AAPM TG-43. For optimization of the functional a new variant of a matching pursuit type solver is presented. The results are compared with current state-of-the-art inverse treatment planning algorithms by means of real prostate cancer patient data. The novel strategy outperforms the best state-of-the-art methods in speed, while achieving comparable quality. It is able to find solutions with comparable values for the objective function and it achieves these results within a few microseconds, being up to 542 times faster than competing state-of-the-art strategies, allowing real-time treatment planning. The sparse solution of inverse brachytherapy planning achieved with methods from compressed sensing is a new paradigm for optimization in medical physics. Through the sparsity of required needles and seeds identified by this method, the cost of intervention may be reduced.