Document Detail


Personalising population-based respiratory motion models of the heart using neighbourhood approximation based on learnt anatomical features.
MedLine Citation:
PMID:  24972379     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Respiratory motion models have been proposed for the estimation and compensation of respiratory motion during image acquisition and image-guided interventions on organs in the chest and abdomen. However, such techniques are not commonly used in the clinic. Subject-specific motion models require a dynamic calibration scan that interrupts the clinical workflow and is often impractical to acquire, while population-based motion models are not as accurate as subject-specific motion models. To address this lack of accuracy, we propose a novel personalisation framework for population-based respiratory motion models and demonstrate its application to respiratory motion of the heart. The proposed method selects a subset of the population sample which is more likely to represent the cardiac respiratory motion of an unseen subject, thus providing a more accurate motion model. The selection is based only on anatomical features of the heart extracted from a static image. The features used are learnt using a neighbourhood approximation technique from a set of training datasets for which respiratory motion estimates are available. Results on a population sample of 28 adult healthy volunteers show average improvements in estimation accuracy of 20% compared to a standard population-based motion model, with an average value for the 50th and 95th quantiles of the estimation error of 1.6mm and 4.7mm respectively. Furthermore, the anatomical features of the heart most strongly correlated to respiratory motion are investigated for the first time, showing the features on the apex in proximity to the diaphragm and the rib cage, on the left ventricle and interventricular septum to be good predictors of the similarity in cardiac respiratory motion.
Authors:
Devis Peressutti; Graeme P Penney; Christoph Kolbitsch; Andrew P King
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-6-6
Journal Detail:
Title:  Medical image analysis     Volume:  18     ISSN:  1361-8423     ISO Abbreviation:  Med Image Anal     Publication Date:  2014 Jun 
Date Detail:
Created Date:  2014-6-27     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9713490     Medline TA:  Med Image Anal     Country:  -    
Other Details:
Languages:  ENG     Pagination:  1015-1025     Citation Subset:  -    
Copyright Information:
Copyright © 2014 Elsevier B.V. All rights reserved.
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