SP-0373 Do we have enough data to feed automated dose planning algorithms in brachytherapy?

2021 ◽  
Vol 161 ◽  
pp. S273
Author(s):  
B. Pieters
2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Anna Concas ◽  
Lothar Reichel ◽  
Giuseppe Rodriguez ◽  
Yunzi Zhang

AbstractThis paper introduces the notions of chained and semi-chained graphs. The chain of a graph, when existent, refines the notion of bipartivity and conveys important structural information. Also the notion of a center vertex $$v_c$$ v c is introduced. It is a vertex, whose sum of p powers of distances to all other vertices in the graph is minimal, where the distance between a pair of vertices $$\{v_c,v\}$$ { v c , v } is measured by the minimal number of edges that have to be traversed to go from $$v_c$$ v c to v. This concept extends the definition of closeness centrality. Applications in which the center node is important include information transmission and city planning. Algorithms for the identification of approximate central nodes are provided and computed examples are presented.


Author(s):  
Dean Wilkinson ◽  
Kelly Mackie ◽  
Dean Novy ◽  
Frances Beaven ◽  
Joanne McNamara ◽  
...  

Abstract Introduction: The Pinnacle3 Auto-Planning (AP) package is an automated inverse planning tool employing a multi-sequence optimisation algorithm. The nature of the optimisation aims to improve the overall quality of radiotherapy plans but at the same time may produce higher modulation, increasing plan complexity and challenging linear accelerator delivery capability. Methods and materials: Thirty patients previously treated with intensity-modulated radiotherapy (IMRT) to the prostate with or without pelvic lymph node irradiation were replanned with locally developed AP techniques for step-and-shoot IMRT (AP-IMRT) and volumetric-modulated arc therapy (AP-VMAT). Each case was also planned with VMAT using conventional inverse planning. The patient cohort was separated into two groups, those with a single primary target volume (PTV) and those with dual PTVs of differing prescription dose levels. Plan complexity was assessed using the modulation complexity score. Results: Plans produced with AP provided equivalent or better dose coverage to target volumes whilst effectively reducing organ at risk (OAR) doses. For IMRT plans, the use of AP resulted in a mean reduction in bladder V50Gy by 4·2 and 4·7 % (p ≤ 0·01) and V40Gy by 4·8 and 11·3 % (p < 0·01) in the single and dual dose level cohorts, respectively. For the rectum, V70Gy, V60Gy and V40Gy were all reduced in the dual dose level AP-VMAT plans by an average of 2·0, 2·7 and 7·3 % (p < 0·01), respectively. A small increase in plan complexity was observed only in dual dose level AP plans. Findings: The automated nature of AP led to high quality treatment plans with improvement in OAR sparing and minimised the variation in achievable dose planning metrics when compared to the conventional inverse planning approach.


2018 ◽  
Vol 06 (02) ◽  
pp. 95-118 ◽  
Author(s):  
Mohammadreza Radmanesh ◽  
Manish Kumar ◽  
Paul H. Guentert ◽  
Mohammad Sarim

Unmanned aerial vehicles (UAVs) have recently attracted the attention of researchers due to their numerous potential civilian applications. However, current robot navigation technologies need further development for efficient application to various scenarios. One key issue is the “Sense and Avoid” capability, currently of immense interest to researchers. Such a capability is required for safe operation of UAVs in civilian domain. For autonomous decision making and control of UAVs, several path-planning and navigation algorithms have been proposed. This is a challenging task to be carried out in a 3D environment, especially while accounting for sensor noise, uncertainties in operating conditions, and real-time applicability. Heuristic and non-heuristic or exact techniques are the two solution methodologies that categorize path-planning algorithms. The aim of this paper is to carry out a comprehensive and comparative study of existing UAV path-planning algorithms for both methods. Three different obstacle scenarios test the performance of each algorithm. We have compared the computational time and solution optimality, and tested each algorithm with variations in the availability of global and local obstacle information.


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