Center for Devices and Radiological Health

About: Center for Devices and Radiological Health is a healthcare organization based out in Rockville, Maryland, United States. It is known for research contribution in the topics: Imaging phantom & Population. The organization has 1834 authors who have published 2642 publications receiving 63717 citations.

Statistics of Speckle in Ultrasound B-Scans

Abstract: the of the magnitude, i.e., intensity, of the field.) It is shown that Rayleigh statistics govern the fist-order behavior of the magnitude; and the autocorrelation of the resulting image speckle is obtained by the methodof Middleton. The corresponding power spectrum follows immediately by Fourier transformation. Theoretical and experimentally determined autocorrelation functions and power spectra derived from B-scans of a scattering phantom containing many scatterers per resolution cell are presented. These functions lead naturally to the definition of the average speckle spot or cell sue, and this inturn is comparable to the resolution cell. Each independent speckle servesas a degreeof freedom that determines the number of samples of tissue available over a target.As the speckle cell size decreases this number increases in a manner predictable from the physical parameters of the cell size. However, it is found that the speckle cellis broadened, the degrees of freedom diminished, when the object structureis correlated. This yields the possibilityof deducing information about the object structure from the second-order statistics of the speckle texture, in addition to that obtainable from the fiistorder statistics.

The Virtual Family—development of surface-based anatomical models of two adults and two children for dosimetric simulations

Abstract: The objective of this study was to develop anatomically correct whole body human models of an adult male (34 years old), an adult female (26 years old) and two children (an 11-year-old girl and a six-year-old boy) for the optimized evaluation of electromagnetic exposure. These four models are referred to as the Virtual Family. They are based on high resolution magnetic resonance (MR) images of healthy volunteers. More than 80 different tissue types were distinguished during the segmentation. To improve the accuracy and the effectiveness of the segmentation, a novel semi-automated tool was used to analyze and segment the data. All tissues and organs were reconstructed as three-dimensional (3D) unstructured triangulated surface objects, yielding high precision images of individual features of the body. This greatly enhances the meshing flexibility and the accuracy with respect to thin tissue layers and small organs in comparison with the traditional voxel-based representation of anatomical models. Conformal computational techniques were also applied. The techniques and tools developed in this study can be used to more effectively develop future models and further improve the accuracy of the models for various applications. For research purposes, the four models are provided for free to the scientific community.

Kidney injury molecule-1 outperforms traditional biomarkers of kidney injury in preclinical biomarker qualification studies

Abstract: Kidney toxicity accounts both for the failure of many drug candidates as well as considerable patient morbidity. Whereas histopathology remains the gold standard for nephrotoxicity in animal systems, serum creatinine (SCr) and blood urea nitrogen (BUN) are the primary options for monitoring kidney dysfunction in humans. The transmembrane tubular protein kidney injury molecule-1 (Kim-1) was previously reported to be markedly induced in response to renal injury. Owing to the poor sensitivity and specificity of SCr and BUN, we used rat toxicology studies to compare the diagnostic performance of urinary Kim-1 to BUN, SCr and urinary N-acetyl-beta-D-glucosaminidase (NAG) as predictors of kidney tubular damage scored by histopathology. Kim-1 outperforms SCr, BUN and urinary NAG in multiple rat models of kidney injury. Urinary Kim-1 measurements may facilitate sensitive, specific and accurate prediction of human nephrotoxicity in preclinical drug screens. This should enable early identification and elimination of compounds that are potentially nephrotoxic.