Reproducibility and Validity of a Chinese Food Frequency Questionnaire

Objectives This study was design to develop a semi-quantitative Chinese Food Frequency Questionnaire （FFQ） and to conduct a validation study for the questionnaire. Methods Based on the survey experience in recent years, a new Chinese food frequency questionnaire （FFQ） with 149 items in 17 food categories was developed. A validation study on this new FFQ was conducted in Jiangsu and Beijing of China between 1999 and 2001. The period of study covered 1 year and the FFQ was validated by comparing with data obtained by a six repeated 24-hour recalls for 3 consecutive days, or a totally 18-day 24- hour recall throughout the year. A total of 271 healthy adult subjects were enrolled in the study. Food and nutrient intakes measured by the 18-day dietary recalls and food frequency questionnaires （FFQs） were computed in the National Institute for Nutrition and Food Safety, China CDC using the existing nutrition database. The average daily intake of foods and nutrients over the 18-day recall was used to compare with FFQ1 and FFQ2, which was conducted at the beginning and the end of the year, respectively. All statistical analyses were carried out using SAS software version 6.12.

Quantitative evaluation of perceived depth of transparently-visualized medical 3D data presented with a multi-view 3D display

Transparent visualization is used in many fields because it can visualize not only the frontal object but also other important objects behind it.Although in many situations,it would be very important for the 3D structures of the visualized transparent images to be perceived as they are simulated,little is known quantitatively as to how such transparent 3D structures are perceived.To address this question,in the present study,we conducted a psychophysical experiment in which the observers reported the perceived depth magnitude of a transparent object in medical images,presented with a multiview 3D display.For the visualization,we employed a stochastic point-based rendering(SPBR)method,which was developed recently as a technique for efficient transparentrendering.Perceived depth of the transparent object was smaller than the simulated depth.We found,however,that such depth underestimation can be alleviated to some extent by(1)applying luminance gradient inherent in the SPBR method,(2)employing high opacities,and(3)introducing binocular disparity and motion parallax produced by a multi-view 3D display.

PARTICLE-BASED TRANSPARENT FUSED VISUALIZATION APPLIED TO MEDICAL VOLUME DATA

This paper proposes a method to create 3D fusion images,such as volume–volume,volume–surface,and surface–surface fusion.Our method is based on the particle-based rendering,which uses tiny particles as rendering primitives.The method can create natural and comprehensible 3D fusion images simply by merging particles prepared for each element to be fused.Moreover,the method does not require particle sorting along the line of sight to realize right depth feel.We apply our method to realize comprehensible visualization of medical volume data.