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A novel optimization framework for VMAT with dynamic gantry couch rotation.

ABSTRACT: Existing volumetric modulated arc therapy (VMAT) optimization using coplanar arcs is highly efficient but usually dosimetrically inferior to intensity modulated radiation therapy (IMRT) with optimized non-coplanar beams. To achieve both dosimetric quality and delivery efficiency, we proposed in this study, a novel integrated optimization method for non-coplanar VMAT (4?VMAT). 4?VMAT with direct aperture optimization (DAO) was achieved by utilizing a least square dose fidelity objective, along with an anisotropic total variation term for regularizing the fluence smoothness, a single segment term for imposing simple apertures, and a group sparsity term for selecting beam angles. Continuous gantry/couch angle trajectories were selected using the Dijkstra's algorithm, where the edge and node costs were determined based on the maximal gantry rotation speed and the estimated fluence map at the current iteration, respectively. The couch-gantry-patient collision space was calculated based on actual machine geometry and a human subject 3D surface. Beams leading to collision are excluded from the DAO and beam trajectory selection (BTS). An alternating optimization strategy was implemented to solve the integrated DAO and BTS problem. The feasibility of 4?VMAT using one full-arc or two full-arcs was tested on nine patients with brain, lung, or prostate cancer. The plan was compared against a coplanar VMAT (2?VMAT) plan using one additional arc and collimator rotation. Compared to 2?VMAT, 4?VMAT reduced the average maximum and mean organs-at-risk dose by 9.63% and 3.08% of the prescription dose with the same target coverage. R50 was reduced by 23.0%. Maximum doses to the dose limiting organs, such as the brainstem, the major vessels, and the proximal bronchus, were reduced by 8.1 Gy (64.8%), 16.3 Gy (41.5%), and 19.83 Gy (55.5%), respectively. The novel 4?VMAT approach affords efficient delivery of non-coplanar arc trajectories that lead to dosimetric improvements compared with coplanar VMAT using more arcs.

SUBMITTER: Lyu Q

PROVIDER: S-EPMC6075870 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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A novel optimization framework for VMAT with dynamic gantry couch rotation.

Lyu Qihui Q Yu Victoria Y VY Ruan Dan D Neph Ryan R O'Connor Daniel D Sheng Ke K

Physics in medicine and biology 20180613 12

Existing volumetric modulated arc therapy (VMAT) optimization using coplanar arcs is highly efficient but usually dosimetrically inferior to intensity modulated radiation therapy (IMRT) with optimized non-coplanar beams. To achieve both dosimetric quality and delivery efficiency, we proposed in this study, a novel integrated optimization method for non-coplanar VMAT (4πVMAT). 4πVMAT with direct aperture optimization (DAO) was achieved by utilizing a least square dose fidelity objective, along wi ...[more]

PMID: 29786614

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Project description:The purpose of this article is to present the first imaging experiments to demonstrate the functional equivalence between a conventional rotational gantry and a fixed-beam imaging geometry, and the feasibility of an iterative image-reconstruction technique under gravitational deformation.Experiments were performed using an Elekta Axesse with Agility MLC and XVI, a custom-built rotating phantom stage, a Catphan QA phantom, and a porcine heart. For the imaging equivalence, a conventional cone beam computed tomography (CBCT) of the Catphan was acquired, as well as a set of 660 x-ray projections with a static gantry and rotating Catphan. Both datasets were reconstructed with the Feldkamp-Davis-Kress (FDK) algorithm, and the resultant volumetric images were compared using standard metrics. For imaging under gravitational deformation, a conventional CBCT of the Catphan and a set of 660 x-ray projections with a static gantry and rotating Catphan were also acquired with a porcine heart. The conventional CBCT was reconstructed using FDK. The projections that were acquired with the heart rotating were sorted into angular bins and reconstructed with prior image constrained compressed sensing using a deformation-blurred FDK prior. Deformation was quantified with B-spline transformation-based deformable image registration.For imaging equivalence, the difference between the two Catphan images was consistent with Poisson noise. For imaging under gravitational deformation, the conventional CBCT porcine heart image (ground truth at 0 degrees) matched the static gantry, rotating heart reconstruction with a mean magnitude of <3 mm and maximum magnitude of <5 mm of the deformation vector field. The mean deformation of the rotating heart was 3.0 to 8.9 mm, up to 16.1 mm maximum deformation. Deformation was mainly observed in the direction of gravity.We have demonstrated imaging equivalence in cone beam CT reconstructions between rigid phantom images acquired with a conventional rotating gantry and with a fixed-gantry and rotating phantom. We have presented a method for image reconstruction under a fixed-beam imaging geometry using a deformable phantom.

Project description:The reformations of the electrical power sector have resulted in very dynamic and competitive market that has changed many elements of the power industry. Excessive demand of energy, depleting the fossil fuel reserves of planet and releasing the toxic air pollutant, has been causing harm to earth habitats. In this new situation, insufficiency of energy supplies, rising power generating costs, high capital cost of renewable energy equipment, environmental concerns of wind power turbines, and ever-increasing demand for electrical energy need efficient economic dispatch. The objective function in practical economic dispatch (ED) problem is nonlinear and non-convex, with restricted equality and inequality constraints, and traditional optimization methods are incapable of resolving such non-convex problems. Over the recent decade, meta-heuristic optimization approaches have acquired enormous reputation for obtaining a solution strategy for such types of ED issues. In this paper, a novel soft computing optimization technique is proposed for solving the dynamic economic dispatch problem (DEDP) of complex non-convex machines with several constraints. Our premeditated framework employs the genetic algorithm (GA) as an initial optimizer and sequential quadratic programming (SQP) for the fine tuning of the pre-optimized run of GA. The simulation analysis of GA-SQP performs well by acquiring less computational cost and finite time of execution, while providing optimal generation of powers according to the targeted power demand and load, whereas subject to valve point loading effect (VPLE) and multiple fueling option (MFO) constraints. The adequacy of the presented strategy concerning accuracy, convergence as well as reliability is verified by employing it on ten benchmark case studies, including non-convex IEEE bus system at the same time also considering VPLE of thermal power plants. The potency of designed optimization seems more robust with fast convergence rate while evaluating the hard bounded DEDP. Our suggested hybrid method GA-SQP converges to achieve the best optimal solution in a confined environment in a limited number of simulations. The simulation results demonstrate applicability and adequacy of the given hybrid schemes over conventional methods.

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A novel optimization framework for VMAT with dynamic gantry couch rotation.

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A novel optimization framework for VMAT with dynamic gantry couch rotation.

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