Backscatter difference measurements may be used to detect changes in bone caused by osteoporosis. The backscatter difference technique measures the power difference between two portions of an ultrasonic backscatter signal. The goal of this study is to evaluate the feasibility of using an ultrasonic imaging system to perform backscatter difference measurements of bone. Ultrasonic images and backscatter signals were acquired from 24 specimens of human cancellous bone. The signals were analyzed in the frequency domain to determine the normalized mean backscatter difference (nMBD) and in the time domain to determine the normalized backscatter amplitude ratio (nBAR). The images were analyzed to determine the normalized pixel value difference (nPVD), which measures the difference in average pixel brightness between regions of interest placed at two different depths in the image. All three parameters were found to increase with bone mineral density. The signal-based parameters, nMBD and nBAR, correlated well with bone mineral density, yielding linear correlation coefficients that ranged from 0.74 to 0.87. The image based parameter, nPVD, performed somewhat less well, yielding correlation coefficients that ranged from 0.42 to 0.81. These results suggest that ultrasonic imaging systems may be used to perform backscatter difference measurements for the purpose of ultrasonic bone assessment.

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A new bis(indenyl)zirconocene bearing a pendant Si-Cl anchor has been grafted onto MCM-41, SBA-15, MCM-48 and a disordered mesoporous silica for the polymerisation of ethylene and propylene, producing polymers with very high molecular weights, low polydispersities and, in the case of polypropylene, higher levels of isotacticity than obtainable with analogous homogeneous systems.

Introduction: Osteoporosis is a degenerative bone disease that affects millions of Americans. Osteoporosis causes normally porous bone tissue, called cancellous bone, to become more porous and weak. It is possible that ultrasonic imaging systems may be used to detect changes in bone density (porosity) caused by osteoporosis. Methods: Ultrasonic images were acquired from 25 cube shaped specimens of cancellous bone in a water tank using a Terason 2000 + ultrasonic imaging system with a 5 MHz linear array transducer. Images were analyzed using an image processing program called ImageJ. Pixel brightness values were plotted as a function of depth in the images of each bone specimen. Pixel value gradient (PVG) was defined as the slope of the resulting graph. Results: PVG was negative for all specimens, and was found to decrease (become more negative) with bone density. PVG demonstrated a moderate but highly significant (p < 0.001) linear correlation with bone density (R = -0.79). Conclusion: Ultrasonic images of bone may be analyzed in ways that yield quantitative information about bone density.

Background: Backscatter difference techniques are being developed to detect changes in bone caused by osteoporosis. Backscatter difference techniques compare the power in one portion of an ultrasonic backscatter signal to the power in a different portion of the same signal. Goal: Evaluate the feasibility of using an ultrasonic imaging system to perform backscatter difference measurements of bone. Procedure: Ultrasonic signals and images were acquired from 24 specimens of bone using an ultrasonic imaging system (Terason) with a 5 MHz linear array transducer. The signals were analyzed to determine the normalized mean backscatter difference (nMBD) between two gated portions of each signal. The images were analyzed to determine the normalized pixel value difference (nPVD) between regions of interest (ROIs) positioned at two different depths. Results: nMBD demonstrated strong linear correlations with bone mineral density that ranged from 0.83 to 0.87. nPVD performed less well, yielding correlations that ranged from 0.42 to 0.81, depending on ROI separation. Conclusions: It is feasible to apply the backscatter difference technique to ultrasonic imaging systems for the purpose of bone assessment. Better results are obtained by analyzing the signals rather than the images.