Title : 
Robust exemplar model of respiratory liver motion and individualization using an additional breath-hold image
         
        
            Author : 
Tanner, Christine ; Samei, Golnoosh ; Szekely, Gabor
         
        
            Author_Institution : 
Comput. Vision Lab., ETH Zurich, Zurich, Switzerland
         
        
        
        
        
        
            Abstract : 
This study investigates the benefits of observing an example of the subject-specific 3D liver motion for individualizing a statistical population model and the influence of tracking errors on the model predictions based on 4D-MRI data from 16 subjects, simulated erroneous 2D tracking surrogates and leave-one-subject-out tests. A robust formulation of the exemplar model was proposed, which lead to mean (95%) prediction improvements of 6% (6%) for tracking errors with 1mm standard deviation. Individualization by an example 3D motion field from the 4D-MRI provided additional improvements (mean: 7%, 95%: 13%). Similar benefits could be achieved from breath-hold images, after automatically detecting and excluding unsuitable motion examples.
         
        
            Keywords : 
biomedical MRI; image motion analysis; liver; medical image processing; pneumodynamics; statistical analysis; 4D-MRI data; additional breath-hold image; leave-one-subject-out tests; respiratory liver individualization; respiratory liver motion; robust exemplar model; simulated erroneous 2D tracking surrogates; standard deviation; statistical population model; subject-specific 3D liver motion; tracking errors; Liver; Predictive models; Principal component analysis; Robustness; Sociology; Three-dimensional displays; Respiratory motion; image-guided therapy; liver; statistical motion model;
         
        
        
        
            Conference_Titel : 
Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on
         
        
            Conference_Location : 
New York, NY
         
        
        
            DOI : 
10.1109/ISBI.2015.7164180