title: A new optimization method using a compressed sensing inspired solver for real-time LDR-brachytherapy treatment planning.
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.
authors: Department of Experimental Radiation Oncology, Medical Faculty of Mannheim, Heidelberg University, 68167 Mannheim, Germany.
authors: C
authors: C
authors: Guthier
authors: K P
authors: KP
authors: Aschenbrenner
authors: D
authors: D
authors: Buergy
authors: M
authors: M
authors: Ehmann
authors: F
authors: F
authors: Wenz
authors: J W
authors: JW
authors: Hesser
mesh_terms: D000465:Algorithms; D001918:Brachytherapy / Q000379:methods*; D006801:Humans; D008297:Male; D011471:Prostatic Neoplasms / Q000532:radiotherapy; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*