Efficient schemes for robust IMRT treatment planning

Ólafsson, Arinbjörn; Wright, Stephen J.

doi:10.1088/0031-9155/51/21/016

Cited by 43 publications

(32 citation statements)

References 27 publications

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“…through manual ''robustification'' of the treatment plans. Computerised robust optimisation techniques have recently been developed [60][61][62]. These robust optimisation techniques are becoming essential in advanced intensity-modulated particle therapy [63], and especially in biologically-guided treatment planning with additional uncertainties in the underlying biological models.…”

Section: Implications For Treatment Planning: Robust Optimisation Andmentioning

confidence: 99%

The physical basis and future of radiation therapy

Bortfeld

Jeraj²

2011

BJR

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ABSTRACT. The remarkable progress in radiation therapy over the last century has been largely due to our ability to more effectively focus and deliver radiation to the tumour target volume. Physics discoveries and technology inventions have been an important driving force behind this progress. However, there is still plenty of room left for future improvements through physics, for example image guidance and four-dimensional motion management and particle therapy, as well as increased efficiency of more compact and cheaper technologies. Bigger challenges lie ahead of physicists in radiation therapy beyond the dose localisation problem, for example in the areas of biological target definition, improved modelling for normal tissues and tumours, advanced multicriteria and robust optimisation, and continuous incorporation of advanced technologies such as molecular imaging. The success of physics in radiation therapy has been based on the continued ''fuelling'' of the field with new discoveries and inventions from physics research. A key to the success has been the application of the rigorous scientific method. In spite of the importance of physics research for radiation therapy, too few physicists are currently involved in cutting-edge research. The increased emphasis on more ''professionalism'' in medical physics will tip the situation even more off balance. To prevent this from happening, we argue that medical physics needs more research positions, and more and better academic programmes. Only with more emphasis on medical physics research will the future of radiation therapy and other physics-related medical specialties look as bright as the past, and medical physics will maintain a status as one of the most exciting fields of applied physics.

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Section: Implications For Treatment Planning: Robust Optimisation Andmentioning

confidence: 99%

The physical basis and future of radiation therapy

Bortfeld

Jeraj²

2011

BJR

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“…For P/C, we have tested both with default Cplex settings, which lead to using the quadratic simplex for solving the relaxations during the B&C, as well as with forcing Cplex to use its IP algorithm throughout all the search. For CP, we have tested both with default Cplex settings and with miqcpstrat = 2, which implements a linearizationbased method for the solution of QCQPs (new to Cplex 11) akin to [11,12,17]. In the Table, columns "nds" and "time" report the number of nodes in the B&C tree and the total running time (in seconds) required by each approach, while column "gap" reports, only for those cases where not all the instances could be solved to optimality within the allotted time limit, the attained gap (in percentage) at termination.…”

Section: Markowitz Mean-variance Modelmentioning

confidence: 99%

A computational comparison of reformulations of the perspective relaxation: SOCP vs. cutting planes

Frangioni

Gentile

2009

Operations Research Letters

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“…Combining the two, we get a position uncertainty which can be represented by a stochastic influence matrix K(ω). Olafsson and Wright [42] and Chu et al [13], assume that the doses are stochastic, and use probabilistic constraints to control the dose levels in the target and in the organs at risk. Chan et al [12] use a motion probability mass function, and assumes that the probability itself is uncertain.…”

Section: Traffic Network Designmentioning

confidence: 99%

On the Robustness of Global Optima and Stationary Solutions to Stochastic Mathematical Programs with Equilibrium Constraints, Part 2: Applications

Cromvik

Patriksson

2009

J Optim Theory Appl

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We consider a stochastic mathematical program with equilibrium constraints (SMPEC), and show that under certain assumptions, global optima and stationary solutions are robust with respect to changes in the underlying probability distribution. In particular, the discretization scheme Sample Average Approximation, which is convergent for both global optima and stationary solutions, can be combined with the robustness result to motivate the use of SMPECs in practice.We also consider SMPECs with multiple objectives, and establish robustness of weakly Pareto optimal and stationary solutions.Two applications are presented, both principally and numerically, in order to exemplify the use of SMPECs: a classic traffic network design problem where travel costs are uncertain, and the optimization of a treatment plan in intensity modulated radiation therapy, where radiobiological parameters are uncertain.

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Efficient schemes for robust IMRT treatment planning

Cited by 43 publications

References 27 publications

The physical basis and future of radiation therapy

The physical basis and future of radiation therapy

A computational comparison of reformulations of the perspective relaxation: SOCP vs. cutting planes

On the Robustness of Global Optima and Stationary Solutions to Stochastic Mathematical Programs with Equilibrium Constraints, Part 2: Applications

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