Optimizing Nozzle Travel Time in Proton Therapy

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Spezialetti, M (Author) Other; Filippo, RD (Author) Other; De Lorenzo, RG (Author) Other; Gravina, GL (Author) Other; Placidi, G (Author) Other; Proietti, G (Author) Other; Rossi, F (Author) Other; Smriglio, S (Author) Other; João Manuel R. S. Tavares (Author) FEUP; Vittorini, F (Author) Other; Mignosi, F (Author) Other

Conference proceedings International

Title: 2022 IEEE 35TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS)

Pages: 441-446

35th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

ELECTR NETWORK, JUL 21-23, 2022

Indexing

Scopus

Other information

Authenticus ID: P-00X-6ZM

DOI: 10.1109/cbms55023.2022.00085

Abstract (EN): Proton therapy is a cancer therapy that is more expensive than classical radiotherapy but that is considered the gold standard in several situations. Since there is also a limited amount of delivering facilities for this techniques, it is fundamental to increase the number of treated patients over time. The objective of this work is to offer an insight on the problem of the optimization of the part of the delivery time of a treatment plan that relates to the movements of the system. We denote it as the Nozzle Travel Time Problem (NTTP), in analogy with the Leaf Travel Time Problem (LTTP) in classical radiotherapy. In particular this work: (i) describes a mathematical model for the delivery system and formalize the optimization problem for finding the optimal sequence of movements of the system (nozzle and bed) that satisfies the covering of the prescribed irradiation directions; (ii) provides an optimization pipeline that solves the problem for instances with an amount of irradiation directions much greater than those usually employed in the clinical practice; (iii) reports preliminary results about the effects of employing two different resolution strategies within the aforementioned pipeline, that rely on an exact Traveling Salesman Problem (TSP) solver, Concorde, and an efficient Vehicle Routing Problem (VRP) heuristic, VROOM. © 2022 IEEE.

Language: English

Type (Professor's evaluation): Scientific