Comparative behaviour of the Dynamically Penalized Likelihood algorithm in inverse radiation therapy planning

University of California, Los Angeles, Los Ángeles, California, United States
Physics in Medicine and Biology (Impact Factor: 2.76). 11/2001; 46(10):2637-63. DOI: 10.1088/0031-9155/46/10/309
Source: PubMed


This paper presents a description of tests carried out to compare the behaviour of five algorithms in inverse radiation therapy planning: (1) The Dynamically Penalized Likelihood (DPL), an algorithm based on statistical estimation theory; (2) an accelerated version of the same algorithm: (3) a new fast adaptive simulated annealing (ASA) algorithm; (4) a conjugate gradient method; and (5) a Newton gradient method. A three-dimensional mathematical phantom and two clinical cases have been studied in detail. The phantom consisted of a U-shaped tumour with a partially enclosed 'spinal cord'. The clinical examples were a cavernous sinus meningioma and a prostate case. The algorithms have been tested in carefully selected and controlled conditions so as to ensure fairness in the assessment of results. It has been found that all five methods can yield relatively similar optimizations, except when a very demanding optimization is carried out. For the easier cases. the differences are principally in robustness, ease of use and optimization speed. In the more demanding case, there are significant differences in the resulting dose distributions. The accelerated DPL emerges as possibly the algorithm of choice for clinical practice. An appendix describes the differences in behaviour between the new ASA method and the one based on a patent by the Nomos Corporation.

Download full-text


Available from: Claus Promberger
  • Source
    • "The two TPSs differ in fluence resolution which is, in one dimension, defined by leaf width. Furthermore, they differ in the optimization algorithm for IP and in leaf sequencing, i.e. the quadratic difference between desired and actual dose is used in Helax-TMS while the Dynamically Penalized Likelihood is applied in BrainSCAN [11]. Leaf sequencing is parameterized by mean (BrainSCAN) or maximum (Helax-TMS) segment numbers, and in the latter TPS also by a 'minimum segment area'. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The purpose of this study was to compare the dosimetric accuracy of IMRT plans for targets in lung with the accuracy of standard uniform-intensity conformal radiotherapy for different dose calculation algorithms. Tests were performed utilizing a special phantom manufactured from cork and polystyrene in order to quantify the uncertainty of two commercial TPS for IMRT in the lung. Ionization and film measurements were performed at various measuring points/planes. Additionally, single-beam and uniform-intensity multiple-beam tests were performed, in order to investigate deviations due to other characteristics of IMRT. Helax-TMS V6.1(A) was tested for 6, 10 and 25 MV and BrainSCAN 5.2 for 6 MV photon beams, respectively. Pencil beam (PB) with simple inhomogeneity correction and 'collapsed cone' (CC) algorithms were applied for dose calculations. However, the latter was not incorporated during optimization hence only post-optimization recalculation was tested. Two-dimensional dose distributions were evaluated applying the gamma index concept. Conformal plans showed the same accuracy as IMRT plans. Ionization chamber measurements detected deviations of up to 5% when a PB algorithm was used for IMRT dose calculations. Significant improvement (deviations approximately 2%) was observed when IMRT plans were recalculated with the CC algorithm, especially for the highest nominal energy. All gamma evaluations confirmed substantial improvement with the CC algorithm in 2D. While PB dose distributions showed most discrepancies in lower (<50%) and high (>90%) dose regions, the CC dose distributions deviated mainly in the high dose gradient (20-80%) region. The advantages of IMRT (conformity, intra-target dose control) should be counterbalanced with possible calculation inaccuracies for targets in the lung. Until no superior dose calculation algorithms are involved in the iterative optimization process it should be used with great care. When only PB algorithm with simple inhomogeneity correction is used, lower energy photon beams should be utilized.
    Preview · Article · Jan 2007 · Acta Oncologica
  • Source
    • "An important issue in inverse planning is how to formalize the clinical goals to objectively evaluate the figures of merit of different IMRT plans. Despite intense research effort in modelling the clinical decision-making strategies (Amols and Ling 2002, Deasy et al 2002, Earl et al 2003, Hou et al 2003, Lahanas et al 2003, Langer et al 1993, 1998, Lee et al 2000, Llacer et al 2001, Mohan et al 1994, Webb 2004, Xing et al 1999, Yan et al 2003), the appropriate form of the objective function remains illusive. Presently, two types of objective functions are widely used: dose or dose–volume histogram (DVH)-based (physical objective functions) (Chen et al 2002, Cho et al 1998, Holmes et al 1995, Hristov et al 2002, Michalski et al 2004, Starkschall et al 2001, Shepard et al 2002, Xing et al 1998) and dose–response-based objective functions (biological objective functions) (Brahme 2001, Kallman et al 1992, Miften et al 2004, Mohan et al 1992, Wang et al 1995, Webb and Nahum 1993). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Clinical IMRT treatment plans are currently made using dose-based optimization algorithms, which do not consider the nonlinear dose-volume effects for tumours and normal structures. The choice of structure specific importance factors represents an additional degree of freedom of the system and makes rigorous optimization intractable. The purpose of this work is to circumvent the two problems by developing a biologically more sensible yet clinically practical inverse planning framework. To implement this, the dose-volume status of a structure was characterized by using the effective volume in the voxel domain. A new objective function was constructed with the incorporation of the volumetric information of the system so that the figure of merit of a given IMRT plan depends not only on the dose deviation from the desired distribution but also the dose-volume status of the involved organs. The conventional importance factor of an organ was written into a product of two components: (i) a generic importance that parametrizes the relative importance of the organs in the ideal situation when the goals for all the organs are met; (ii) a dose-dependent factor that quantifies our level of clinical/dosimetric satisfaction for a given plan. The generic importance can be determined a priori, and in most circumstances, does not need adjustment, whereas the second one, which is responsible for the intractable behaviour of the trade-off seen in conventional inverse planning, was determined automatically. An inverse planning module based on the proposed formalism was implemented and applied to a prostate case and a head-neck case. A comparison with the conventional inverse planning technique indicated that, for the same target dose coverage, the critical structure sparing was substantially improved for both cases. The incorporation of clinical knowledge allows us to obtain better IMRT plans and makes it possible to auto-select the importance factors, greatly facilitating the inverse planning process. The new formalism proposed also reveals the relationship between different inverse planning schemes and gives important insight into the problem of therapeutic plan optimization. In particular, we show that the EUD-based optimization is a special case of the general inverse planning formalism described in this paper.
    Preview · Article · Dec 2004 · Physics in Medicine and Biology
  • Source
    • "Stochastic optimizations have also been carried out with the quadratic cost function of equation (1). The algorithm used is the adaptive simulated annealing (ASA) algorithm described in Llacer et al (2001). This algorithm was also used in a study of multiple local minima in Llacer et al (2003). "
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper attempts to provide an answer to some questions that remain either poorly understood, or not well documented in the literature, on basic issues related to intensity modulated radiation therapy (IMRT). The questions examined are: the relationship between degeneracy and frequency response of optimizations, effects of initial beamlet fluence assignment and stopping point, what does filtering of an optimized beamlet map actually do and how could image analysis help to obtain better optimizations? Two target functions are studied, a quadratic cost function and the log likelihood function of the dynamically penalized likelihood (DPL) algorithm. The algorithms used are the conjugate gradient, the stochastic adaptive simulated annealing and the DPL. One simple phantom is used to show the development of the analysis tools used and two clinical cases of medium and large dose matrix size (a meningioma and a prostate) are studied in detail. The conclusions reached are that the high number of iterations that is needed to avoid degeneracy is not warranted in clinical practice, as the quality of the optimizations, as judged by the DVHs and dose distributions obtained, does not improve significantly after a certain point. It is also shown that the optimum initial beamlet fluence assignment for analytical iterative algorithms is a uniform distribution, but such an assignment does not help a stochastic method of optimization. Stopping points for the studied algorithms are discussed and the deterioration of DVH characteristics with filtering is shown to be partially recoverable by the use of space-variant filtering techniques.
    Full-text · Article · Aug 2004 · Physics in Medicine and Biology
Show more