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Automatic Measurement of Venous Pressure Using B-Mode Ultrasound

Alessandro Crimi, Maxim Makhinya, Ulrich Baumann, Christoph Thalhammer, Gabor Székely, and Orcun Goksel
IEEE Trans Biomedical Engineering
Vol. 63, No. 2, July 2015


Central venous pressure (CVP) information is crucial in clinical situations such as cardiac failure, intravascular volume overload, and sepsis. The measurement of CVP, however, requires catheterization of vena cava through the subclavian or internal jugular veins, which is an impractical and costly procedure with related risk of complications. Peripheral venous pressure (PVP), which correlates with CVP under certain patient positioning, can be measured noninvasively using ultrasound via controlled compressions of a superficial vein. This paper presents an automatic system for acquiring such noninvasive measurements. Robust signal and image processing techniques developed for this purpose are introduced in this work. The proposed stand-alone, mobile platform collects images in real-time from the display output of any ultrasound machine, meanwhile measuring the pressure on the skin underneath the ultrasound transducer via a liquid-filled pouch. The image and pressure data are synchronized through an automated temporal calibration procedure. During forearm compressions, blood vessels are detected and tracked in the images using robust geometric (ellipse) models, the parameters of which are used further in model-based estimation of PVP. The proposed system was tested in 56 image sequences on 14 healthy volunteers, and was shown to achieve measurements with errors comparable to or lower than the interoperator variability between expert manual assessments.

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  author = {Alessandro Crimi and Maxim Makhinya and Ulrich Baumann and Christoph Thalhammer and Gabor Székely and and Orcun Goksel},
  title = {Automatic Measurement of Venous Pressure Using B-Mode Ultrasound},
  journal = {IEEE Trans Biomedical Engineering},
  year = {2015},
  month = {July},
  pages = {},
  volume = {63},
  number = {2},
  keywords = {Venous blood pressure, real-time tracking, vessel segmentation, Star-Kalman, dynamic programming}