Data Entry: Please note that the research database will be replaced by UNIverse by the end of October 2023. Please enter your data into the system https://universe-intern.unibas.ch. Thanks

Login for users with Unibas email account...

Login for registered users without Unibas email account...

 
Model-guided respiratory organ motion prediction of the liver from 2D ultrasound
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
 
ID 2582133
Author(s) Preiswerk, Frank; De Luca, Valeria; Arnold, Patrik; Celicanin, Zarko; Petrusca, Lorena; Tanner, Christine; Bieri, Oliver; Salomir, Rares; Cattin, Philippe C
Author(s) at UniBasel Cattin, Philippe Claude
Year 2014
Year: comment 2014
Title Model-guided respiratory organ motion prediction of the liver from 2D ultrasound
Journal Medical image analysis
Volume 18
Number 5
Pages / Article-Number 740-51
Keywords Respiratory motion compensation, Statistical motion model, Spatio-temporal prediction, 4D-MRI, Ultrasound
Abstract With the availability of new and more accurate tumour treatment modalities such as high-intensity focused ultrasound or proton therapy, accurate target location prediction has become a key issue. Various approaches for diverse application scenarios have been proposed over the last decade. Whereas external surrogate markers such as a breathing belt work to some extent, knowledge about the internal motion of the organs inherently provides more accurate results. In this paper, we combine a population-based statistical motion model and information from 2d ultrasound sequences in order to predict the respiratory motion of the right liver lobe. For this, the motion model is fitted to a 3d exhalation breath-hold scan of the liver acquired before prediction. Anatomical landmarks tracked in the ultrasound images together with the model are then used to reconstruct the complete organ position over time. The prediction is both spatial and temporal, can be computed in real-time and is evaluated on ground truth over long time scales (5.5 min). The method is quantitatively validated on eight volunteers where the ultrasound images are synchronously acquired with 4D-MRI, which provides ground-truth motion. With an average spatial prediction accuracy of 2.4 mm, we can predict tumour locations within clinically acceptable margins. (C) 2014 Elsevier B.V. All rights reserved.
Publisher Elsevier
ISSN/ISBN 1361-8415
edoc-URL http://edoc.unibas.ch/dok/A6298831
Full Text on edoc No
Digital Object Identifier DOI 10.1016/j.media.2014.03.006
PubMed ID http://www.ncbi.nlm.nih.gov/pubmed/24835181
ISI-Number WOS:000337875700004
Document type (ISI) Journal Article
 
   

MCSS v5.8 PRO. 0.339 sec, queries - 0.000 sec ©Universität Basel  |  Impressum   |    
12/04/2024