IMRT Beam Angle Optimization Using Dynamically Dimensioned Search

Dias, Joana; Rocha, Humberto; Ferreira, B.; Lopes, Maria do Carmo

doi:10.1007/978-3-319-03005-0_1

Cited by 6 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The challenge is greater with the additional depth dimension in IMPT optimization. While methods for IMXT BOO have been developed using heuristic and stochastic algorithms to overcome the mathematical challenge, [9][10][11][12][13][14][15] the BOO problem is rarely touched in IMPT. To our knowledge, the only work that attempts to explicitly attack the IMPT BOO problem was by Cao et al, 16 in which the coplanar BOO problem was treated as a combinatorial problem.…”

Section: Introductionmentioning

confidence: 99%

Integrated beam orientation and scanning‐spot optimization in intensity‐modulated proton therapy for brain and unilateral head and neck tumors

et al. 2018

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Integrated beam orientation and scanning‐spot optimization in intensity‐modulated proton therapy for brain and unilateral head and neck tumors

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The Intensity Modulated Photon Therapy (IMXT) BOO problem has been extensively researched using stochastic and analytical methods, [6][7][8][9][10][11][12] but there have been limited IMPT BOO studies. Cao et al 13 applied a local neighborhood search (LNS) algorithm to the IMPT BOO problem and implemented it on prostate cancer to improve beam arrangement.…”

Section: Introductionmentioning

confidence: 99%

Robust beam orientation optimization for intensity‐modulated proton therapy

Neph

Ruan

et al. 2019

Medical Physics

View full text Add to dashboard Cite

Purpose Dose conformality and robustness are equally important in intensity modulated proton therapy (IMPT). Despite the obvious implication of beam orientation on both dosimetry and robustness, an automated, robust beam orientation optimization algorithm has not been incorporated due to the problem complexity and paramount computational challenge. In this study, we developed a novel IMPT framework that integrates robust beam orientation optimization (BOO) and robust fluence map optimization (FMO) in a unified framework. Methods The unified framework is formulated to include a dose fidelity term, a heterogeneity‐weighted group sparsity term, and a sensitivity regularization term. The L2, 1/2‐norm group sparsity is used to reduce the number of active beams from the initial 1162 evenly distributed noncoplanar candidate beams, to between two and four. A heterogeneity index, which evaluates the lateral tissue heterogeneity of a beam, is used to weigh the group sparsity term. With this index, beams more resilient to setup uncertainties are encouraged. There is a symbiotic relationship between the heterogeneity index and the sensitivity regularization; the integrated optimization framework further improves beam robustness against both range and setup uncertainties. This Sensitivity regularization and Heterogeneity weighting based BOO and FMO framework (SHBOO‐FMO) was tested on two skull‐base tumor (SBT) patients and two bilateral head‐and‐neck (H&N) patients. The conventional CTV‐based optimized plans (Conv) with SHBOO‐FMO beams (SHBOO‐Conv) and manual beams (MAN‐Conv) were compared to investigate the beam robustness of the proposed method. The dosimetry and robustness of SHBOO‐FMO plan were compared against the manual beam plan with CTV‐based voxel‐wise worst‐case scenario approach (MAN‐WC). Results With SHBOO‐FMO method, the beams with superior range robustness over manual beams were selected while the setup robustness was maintained or improved. On average, the lowest [D95%, V95%, V100%] of CTV were increased from [93.85%, 91.06%, 70.64%] in MAN‐Conv plans, to [98.62%, 98.61%, 96.17%] in SHBOO‐Conv plans with range uncertainties. With setup uncertainties, the average lowest [D98%, D95%, V95%, V100%] of CTV were increased from [92.06%, 94.83%, 94.31%, 78.93%] in MAN‐Conv plans, to [93.54%, 96.61%, 97.01%, 91.98%] in SHBOO‐Conv plans. Compared with the MAN‐WC plans, the final SHBOO‐FMO plans achieved comparable plan robustness and better OAR sparing, with an average reduction of [Dmean, Dmax] of [6.31, 6.55] GyRBE for the SBT cases and [1.89, 5.08] GyRBE for the H&N cases from the MAN‐WC plans. Conclusion We developed a novel method to integrate robust BOO and robust FMO into IMPT optimization for a unified solution of both BOO and FMO, generating plans with superior dosimetry and good robustness.

show abstract

“…BAO problem is known to be a challenging problem, highly non-convex and with many local minima. Several approaches have been proposed to tackle this problem [1][2][3][4][5][6][7][8]. SA is a well known metaheuristic that has proven to be able of calculating good quality solutions for challenging problems.…”

Section: Introductionmentioning

confidence: 99%

IMRT Inverse Planning Using Simulated Annealing

et al. 2015

View full text Add to dashboard Cite

Abstract-Radiation therapy is an important type of cancer treatment, being delivered to more than half of all cancer patients. Intensity Modulated Radiation Therapy (IMRT) is one type of radiation therapy where it is possible not only to conform the dose distribution to the shape of the volumes to treat, but also to have different radiation intensity profiles that will allow a better coverage of the volumes to treat and a better sparing of all other structures. Planning an IMRT treatment is a time-demanding task, based on a trial and error procedure. We propose an inverse planning approach to this problem based on simulated annealing (SA) and a dynamic neighborhood concept. The planning of an IMRT treatment can be interpreted as having several stages, from choosing proper incidence directions until deciding on the movement of the multiple collimator leaves that will produce the desired intensity profiles. We consider here mainly the first of these stages: the problem of choosing the best intensity directions. This problem is known to be highly non-convex and with many local minima. SA is a well known metaheuristic that has proven to be capable of escaping local minima. Some preliminary computational results using ten clinical examples of already treated patient cases of head-and-neck tumors at the Portuguese Institute of Oncology of Coimbra (IPOC) will be described.

show abstract

IMRT Beam Angle Optimization Using Dynamically Dimensioned Search

Cited by 6 publications

References 10 publications

Integrated beam orientation and scanning‐spot optimization in intensity‐modulated proton therapy for brain and unilateral head and neck tumors

Integrated beam orientation and scanning‐spot optimization in intensity‐modulated proton therapy for brain and unilateral head and neck tumors

Robust beam orientation optimization for intensity‐modulated proton therapy

IMRT Inverse Planning Using Simulated Annealing

Contact Info

Product

Resources

About