3-D volume rendering maxillofacial analysis of angular measurements by multislice CT

Objective. This study was designed to determine the precision and accuracy of angular measurements using three-dimensional computed tomography (3D-CT) volume rendering by computer systems. Study design. The study population consisted of 28 dried skulls that were scanned with a 64-row multislice CT, and 3D-CT images were generated. Angular measurements, (n = 6) based upon conventional craniometric anatomical landmarks (n = 9), were identified independently in 3D-CT images by 2 radiologists, twice each, and were then performed by 3D-CT imaging. Subsequently, physical measurements were made by a third examiner using a Beyond Crysta-C9168 series 900 device. Results. The results demonstrated no statistically significant difference between interexaminer and intraexaminer analysis. The mean difference between the physical and 3-D-based angular measurements was -1.18% and -0.89%, respectively, for both examiners, demonstrating high accuracy. Conclusion. Maxillofacial analysis of angular measurements using 3D-CT volume rendering by 64-row multislice CT is established and can be used for orthodontic and dentofacial orthopedic applications.

The exponentiated generalized inverse Gaussian distribution

The modeling and analysis of lifetime data is an important aspect of statistical work in a wide variety of scientific and technological fields. Good (1953) introduced a probability distribution which is commonly used in the analysis of lifetime data. For the first time, based on this distribution, we propose the so-called exponentiated generalized inverse Gaussian distribution, which extends the exponentiated standard gamma distribution (Nadarajah and Kotz, 2006). Various structural properties of the new distribution are derived, including expansions for its moments, moment generating function, moments of the order statistics, and so forth. We discuss maximum likelihood estimation of the model parameters. The usefulness of the new model is illustrated by means of a real data set. (c) 2010 Elsevier B.V. All rights reserved.