Evidence map›Paper›PMID 29659018›Full record

ArticleMedical physics2018

VMAT optimization with dynamic collimator rotation.

Qihui Lyu, Daniel O'Connor, Dan Ruan, Victoria Yu, Dan Nguyen, Ke Sheng

Open access · greenAbstract readEvaluation Study
In one paragraph

Article in Medical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
3.4field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 24 citations in OpenAlex.

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  13. Many-isocenter optimization for robotic radiotherapy.Physics in medicine and biology · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Qihui LyuDepartment of Radiation Oncology, University of California Los Angeles, Los Angeles, California, 90095, USA.
Daniel O'ConnorDepartment of Radiation Oncology, University of California Los Angeles, Los Angeles, California, 90095, USA.
Dan RuanDepartment of Radiation Oncology, University of California Los Angeles, Los Angeles, California, 90095, USA.
Victoria YuDepartment of Radiation Oncology, University of California Los Angeles, Los Angeles, California, 90095, USA.
Dan NguyenDepartment of Radiation Oncology, University of California Los Angeles, Los Angeles, California, 90095, USA.
Ke ShengDepartment of Radiation Oncology, University of California Los Angeles, Los Angeles, California, 90095, USA.
University of California, Los Angeles · US

Funding

Development of a versatile robotic radiation therapy systemR44CA183390 · NCI · RADIABEAM TECHNOLOGIES, LLC · PI SHENG, KE, YONEMOTO, JAY · 2017 to 2024
$5.8M
Motion Management of Pancreatic Cancer in MRI-Guided RadiotherapyR01CA188300 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SHENG, KE · 2015 to 2019
$1.7M
(NCI) Developing an Intermediate Energy Linac for Robotic RadiotherapyR43CA183390 · NCI · RADIABEAM TECHNOLOGIES, LLC · PI BOUCHER, SALIME · 2015 to 2016
$265k
NCI NIH HHS R01 CA188300NCI NIH HHS R43 CA183390NCI NIH HHS R44 CA183390
6 · The paper itself

Abstract

purposeAlthough collimator rotation is an optimization variable that can be exploited for dosimetric advantages, existing Volumetric Modulated Arc Therapy (VMAT) optimization uses a fixed collimator angle in each arc and only rotates the collimator between arcs. In this study, we develop a novel integrated optimization method for VMAT, accounting for dynamic collimator angles during the arc motion.

methodsDirect Aperture Optimization (DAO) for Dynamic Collimator in VMAT (DC-VMAT) was achieved by adding to the existing dose fidelity objective an anisotropic total variation term for regulating the fluence smoothness, a binary variable for forming simple apertures, and a group sparsity term for controlling collimator rotation. The optimal collimator angle for each beam angle was selected using the Dijkstra's algorithm, where the node costs depend on the estimated fluence map at the current iteration and the edge costs account for the mechanical constraints of multi-leaf collimator (MLC). An alternating optimization strategy was implemented to solve the DAO and collimator angle selection (CAS). Feasibility of DC-VMAT using one full-arc with dynamic collimator rotation was tested on a phantom with two small spherical targets, a brain, a lung and a prostate cancer patient. The plan was compared against a static collimator VMAT (SC-VMAT) plan using three full arcs with 60 degrees of collimator angle separation in patient studies.

resultsWith the same target coverage, DC-VMAT achieved 20.3% reduction of R50 in the phantom study, and reduced the average max and mean OAR dose by 4.49% and 2.53% of the prescription dose in patient studies, as compared with SC-VMAT. The collimator rotation co-ordinated with the gantry rotation in DC-VMAT plans for deliverability. There were 13 beam angles in the single-arc DC-VMAT plan in patient studies that requires slower gantry rotation to accommodate multiple collimator angles.

conclusionsThe novel DC-VMAT approach utilizes the dynamic collimator rotation during arc delivery. In doing so, DC-VMAT affords more sophisticated intensity modulation, alleviating the limitation previously imposed by the square beamlet from the MLC leaf thickness and achieves higher effective modulation resolution. Consequently, DC-VMAT with a single arc manages to achieve superior dosimetry than SC-VMAT with three full arcs.

Indexed as

AlgorithmsBrain NeoplasmsHumansLung NeoplasmsMalePhantoms, ImagingProstatic NeoplasmsRadiotherapy DosageRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-AssistedRotationWit and Humor as Topicdynamic collimatoroptimizationVMAT

Identifiers

PMID29659018
PMCPMC5997563
OpenAlexW2800677998

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.