a simulation study on patient setup errors in external beam radiotherapy using an anthropomorphic 4d phantom
Clicks: 175
ID: 249553
2016
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
30.0
/100
175 views
23 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #68 of 81 articles by views in the international archives of the photogrammetry, remote sensing and spatial information sciences
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Introduction Patient set-up optimization is required in radiotherapy to fill the accuracy gap between personalized treatment planning and uncertainties in the irradiation set-up. In this study, we aimed to develop a new method based on neural network to estimate patient geometrical setup using 4-dimensional (4D) XCAT anthropomorphic phantom. Materials and Methods To access 4D modeling of motion of dynamic organs, a phantom employs non-uniform rational B-splines (NURBS)-based Cardiac-Torso method with spline-based model to generate 4D computed tomography (CT) images. First, to generate all the possible roto-translation positions, the 4D CT images were imported to Medical Image Data Examiner (AMIDE). Then, for automatic, real time verification of geometrical setup, an artificial neural network (ANN) was proposed to estimate patient displacement, using training sets. Moreover, three external motion markers were synchronized with a patient couch position as reference points. In addition, the technique was validated through simulated activities by using reference 4D CT data acquired from five patients. Results The results indicated that patient geometrical set-up is highly depended on the comprehensiveness of training set. By using ANN model, the average patient setup error in XCAT phantom was reduced from 17.26 mm to 0.50 mm. In addition, in the five real patients, these average errors were decreased from 18.26 mm to 1.48 mm various breathing phases ranging from inhalation to exhalation were taken into account for patient setup. Uncertainty error assessment and different setup errors were obtained from each respiration phase. Conclusion This study proposed a new method for alignment of patient setup error using ANN model. Additionally, our correlation model (ANN) could estimate true patient position with less error.
| Reference Key |
miandoab2016iraniana
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Payam Samadi Miandoab;Ahmad Esmaili Torshabi;Saber Nankali;Mohammad Reza Rezaie |
| Journal | the international archives of the photogrammetry, remote sensing and spatial information sciences |
| Year | 2016 |
| DOI |
10.22038/ijmp.2017.8320
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.