دانلود رایگان مقاله تصویربرداری موج پالس با سونوگرافی معمولی در آئورت سگ و عروق طبیعی انسان

Abstract

The propagation behavior of the arterial pulse wave may provide valuable diagnostic information for cardiovascular pathology. pulse wave imaging (PWI) is a noninvasive, ultrasound imaging-based technique capable of mapping multiple wall motion waveforms along a short arterial segment over a single cardiac cycle, allowing for the regional pulse wave velocity (PWV) and propagation uniformity to be evaluated. The purpose of this study was to improve the clinical utility of PWI using a conventional ultrasound system. The tradeoff between PWI spatial and temporal resolution was evaluated using an ex vivo canine aorta (n Z 2) setup to assess the effects of varying image acquisition and signal processing parameters on the measurement of the PWV and the pulse wave propagation uniformity r2 . PWI was also performed on the carotid arteries and abdominal aortas of 10 healthy volunteers (24.8
3.3 y.o.) to determine the waveform tracking feature that would yield the most precise PWV measurements and highest r2 values in vivo. The ex vivo results indicated that the highest precision for measuring PWVs w2.5e3.5 m/s was achieved using 24e48 scan lines within a 38 mm image plane width (i.e. 0.63e1.26 lines/mm). The in vivo results indicated that tracking the 50% upstroke of the waveform would consistently yield the most precise PWV measurements and minimize the error in the propagation uniformity measurement. Such findings may help establish the optimal image acquisition and signal processing parameters that may improve the reliability of PWI as a clinical measurement tool.

Conclusions

The conclusions from this paper serve to improve the clinical utility of PWI for more accurate diagnosis and characterization of cardiovascular disease based on pulse wave analysis. In cases of disease such as aneurysm and atherosclerosis, the altered geometry and mechanical properties of the vessel results in non-uniform pulse wave propagation patterns, and hence the PWV may vary within the imaged segment. By eliminating the imaging and signal processing factors that may contribute to imprecise PWV and low r2 measurements, we may be able to more reliably detect variations in disease cases.