Background: Myocardial tissue characterization by MR T1 and extracellular volume (ECV) mapping has demonstratedclinical value. The modified Look–Locker inversion recovery (MOLLI) sequence is a... Show moreBackground: Myocardial tissue characterization by MR T1 and extracellular volume (ECV) mapping has demonstratedclinical value. The modified Look–Locker inversion recovery (MOLLI) sequence is a standard mapping technique, but itsquality can be negatively affected by motion.Purpose: To develop a robust motion correction method for T1 and ECV mapping.Study Type: Retrospective analysis of clinical data.Population: Fifty patients who were referred to cardiac MR exam for T 1 mapping.Field Strength/Sequence: 3.0T cardiac MRI with precontrast and postcontrast MOLLI acquisition of the left ventricle(LV).Assessment: A groupwise registration method based on principle component analysis (PCA) was developed to registerall MOLLI frames simultaneously. The resulting T 1 and ECV maps were compared to those from the original andmotion-corrected MOLLI with pairwise registration, in terms of standard deviation (SD) error.Statistical Test: Paired variables were compared using the Wilcoxon signed-rank test.Results: The groupwise registration method demonstrated improved registration performance compared to pairwiseregistration, with the T 1 SD error reduced from 31 6 20 msec to 26 6 15 msec (P < 0.05), and ECV SD error reducedfrom 4.1 6 3.6% to 2.8 6 2.0% (P < 0.05). In LV segmental analysis, the performance was particularly improved in lat-eral segments, which are most affected by motion. The running time of groupwise registration was significantly shorterthan that of the pairwise registration, 17.5 6 3.0 seconds compared to 43.5 6 2.2 seconds (P < 0.05).Data Conclusion: We developed an automatic, robust motion correction method for myocardial T 1 and ECV mappingbased on a new groupwise registration scheme. The method led to lower mapping error compared to the conventionalpairwise registration method in reduced execution time. Show less
In the social and behavioral sciences, data sets often do not meet the assumptions of traditional analysis methods. Therefore, nonlinear alternatives to traditional methods have been developed.... Show moreIn the social and behavioral sciences, data sets often do not meet the assumptions of traditional analysis methods. Therefore, nonlinear alternatives to traditional methods have been developed. This thesis starts with a didactic discussion of nonlinear principal components analysis (NLPCA), illustrated by an application considering caregiver-child interactions in day-care. Traditional PCA explores data structures, summarizing the observed information in underlying variables, called principal components. The method only gives a sensible solution if the variables are numeric, and linearly related to each other. NLPCA is developed for situations in which these assumptions do not apply. It incorporates different types of variables (nominal, ordinal, and numeric) and discovers and handles nonlinear relationships. As PCA does not make assumptions about variable distributions, it does not seem theoretically sensible to apply standard (asymptotic) formulas for statistical inference. Therefore, this thesis shows easily applicable ways of assessing stability and statistical significance of the elements of the NLPCA solution (eigenvalues, component loadings, component scores, category quantifications) without making prior assumptions about the data (i.e., nonparametrically), using the bootstrap and permutation tests, respectively. By providing relatively simple inferential measures for NLPCA, a wider use of this method in the psychological and educational context may be promoted. Show less
