title: Comparison of different grid of tags detection methods in tagged cardiac MR imaging.
abstract: OBJECTIVE
  Non-invasive imaging assessment of cardiac function is important in cardiovascular disease diagnosis, especially for evaluation of local cardiac motion. Tagged cardiac MRI has been developed for this purpose, but evaluation of the results requires quantification and automation.
  
  
  METHODS
  Two methods utilizing active contour modeling for wall motion extraction based on tagged cardiac MRI scans were evaluated based on properties of tracking methods in the image domain and frequency domain. Three criteria were used: accuracy, inter-subject and intra-subject sensitivity. The tracking results were evaluated by a medical expert. The evaluation methodology and its possible generalization to other diagnostic methods were considered.
  
  
  RESULTS
  Image domain and frequency domain analysis of tagged cardiac MRI data sets were evaluated demonstrating that the image domain method provides better results. The image domain method method is much more resistant to changes in the data, this time, due to a different subject being scanned. The frequency domain approach is not suitable for clinical applications, as the global error is significantly increased (more than 20%).
  
  
  CONCLUSIONS
  The image domain method was found most effective, and it can generate a set of clearly identified parameters. The evaluation approach can be an interesting alternative to classical psychovisual studies which are time-consuming and often fastidious for clinicians.
authors.affiliation: ETIS UMR CNRS 8051, ENSEA-University of Cergy-Pontoise, 6 av. du Ponceau, 95000, Cergy-Pontoise Cedex, France. aymeric.histace@u-cergy.fr
authors.forename: Aymeric
authors.initials: A
authors.lastname: Histace
authors.forename: Christophe
authors.initials: C
authors.lastname: Portefaix
authors.forename: Bogdan
authors.initials: B
authors.lastname: Matuszewski
mesh_terms: D000328:Adult; D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated; D012016:Reference Values; D012680:Sensitivity and Specificity