Three-dimensional Vocal Tract Morphology Based on Multiple Magnetic Resonance Images Is Highly Reproducible During Sustained Phonation.

OBJECTIVES: The extraction of a three-dimensional (3D) morphology of the human vocal tract (VT) from magnetic resonance imaging (MRI) during sustained phonation can be used for various analyses like numerical simulations or creating physical models. The precision of visualizing techniques nowadays allows for very targeted acoustical simulation evaluating the influence of subsections of the VT for the transfer function. The aim of the study was to assess the accuracy of the 3D geometry based on MRI data in repetitive trials. STUDY DESIGN: This is a prospective study. METHODS: Four experienced singers underwent an MRI while repeating a specific vocal task 20 times consecutively. Audio recordings were made by means of an optical microphone. Images were restacked and subsections of the VT were segmented on multi-image-based cross sections using a semiautomatic algorithm. Different volume and area measures were evaluated. RESULTS: A high reproducibility of the morphologic data based on multiple images by means of the applied segmentation method could be shown with an overall variation of around 8%. CONCLUSIONS: 3D modeling of the VT during sustained phonation involves a complex experimental setting and elaborate image processing techniques. Functional comparative analysis or acoustical simulations based on such data should take the found variability into account.

Numbers can be worth a thousand pictures: individual differences in understanding graphical and numerical representations of health-related information.

OBJECTIVE: Informed medical decision making requires comprehending statistical information. We aimed to improve the understanding of conveying health-related statistical information with graphical representations compared with numerical representations. First, we investigated whether the iconicity of representations (i.e., their abstractness vs. concreteness) affected comprehension and recall of statistical information. Second, we investigated whether graph literacy helps to identify individuals who comprehend graphical representations better than numerical representations. METHOD: Participants (N = 275) were randomly assigned to receive different representations of health-related statistical information, ranging from very low iconicity (numbers) to very high iconicity (icon arrays including photographs). Comprehension and recall of the information were assessed. Additionally, participants rated the accessibility of the information and the attractiveness of the representation. Graph literacy was assessed by means of a recently developed scale. RESULTS: The only difference between representations that affected comprehension and recall was the difference between graphics and numbers; the actual level of iconicity of graphics did not matter. Individuals with high graph literacy had better comprehension and recall when presented with graphics instead of numbers, and they rated graphical information as more accessible than numerical information, whereas the reverse was true for individuals with low graph literacy, F(4, 185) = 2.60, p = .04, η(p)(²) = .05, and F(4, 245) = 2.71, p = .03, η(p)(2) = .04, respectively. Both groups judged graphical representations as more attractive than numerical representations. CONCLUSION: An assessment of graph literacy distinguished individuals who are best informed with graphical representations of statistical information from those who are better informed with numerical representations.