Automatic Measurement of Venous Pressure Using B-Mode Ultrasound

Abstract

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 the 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 paper. The proposed standalone 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 the 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 of expert manual assessments.

Speed-of-Sound Imaging Based on Reflector Delineation

“…This approach reduces timing ambiguities and also provides a continuous τ surface in the transceiver-time space of A. Dynamic Programming (DP) was proposed for the delineation of bones [40] and vessel walls [41] in ultrasound. Herein we introduce an algorithm using DP for detecting reflector time-delay surface across multiple RF lines.…”

Robust motion tracking in liver from 2D ultrasound images using supporters

“…For each frame I f , first the center is transformed by the affine transform determined by motion tracking, see above, and then the center and radii are re-estimated as in [21] using the Star Edge detection, dynamic programming, model fitting, and binary template matching. The center of the resulting ellipse is then used as the estimated target position at frame I f .…”

Evaluation of 2D and 3D ultrasound tracking algorithms and impact on ultrasound‐guided liver radiotherapy margins

“…For each I ( t ), first the ellipse is translated by the previous motion estimate. Then, its center and radii are re‐estimated as in using the Star edge detection, dynamic programming, model fitting, and binary templates. The resulting ellipse center is taken as the sought landmark.…”

“…Makhinya & Goksel have extended an algorithm for identifying and tracking superficial veins in the forearm by integrating several tracking recovery strategies to take advantage of the repetitive nature of respiration. Lucas‐Kanade‐based tracking was applied on regularly‐spaced grid points around each landmark, and used for reference tracking ( I (1) to I ( t )), when the local appearance of I (1) and I ( t ) is similar.…”

“…For each I(t), first the ellipse is translated by the previous motion estimate. Then, its center and radii are re-estimated as in 56 58 but with a dense scale invariant feature transform (SIFT) 59 as similarity measure. 60,61 Each landmark P j was tracked independently and sequentially using image registration around a region of interest (W j ).…”