Generation of RF-data containing blood and tissue motion
Due to breathing, heart beat and vessel wall pulsation the tissue surrounding
a vessel will experience motion. By simulating this motion along with the
motion of the blood, very realistic RF-data can be computed. In this example
RF-data for the carotid artery including tissue motion due to breathing
and pulsation is generated. The blood velocity profile is modeled using
Womersley´s pulsatile flow model.
The phantom generates 3,500 files with positions of scatterers at the
corresponding time step. The change in position of the scatterers at each
time step is determined by the blood velocity and the tissue motion models.
These files are then used for generating the RF-lines along one direction
for the number time steps - equal to 3,500. The transducer is modeled as
a convex, elevation focused array with 60 elements with a Hanning apodization in
transmit and receive. The element height was 15 mm, the width 0.37 mm,
and the kerf 0.03 mm. The transmit pulse consist of 8 periods. A single
transmit focus was placed at 40 mm, and receive focusing was done at
40 mm intervals.
The resulting signals can then be used in a standard autocorrelation
estimator for finding the velocity image.
Plots of a simulated (top) and measured (bottom) RF-signal are given
below.
The m-files can be found at:
examples/ftp_files/tissue_motion
The routine field.m initializes the field system, and should
be modified to point to the directory holding the Field II code and m-files.
The routine make_sct.m is then called to make the file for the scatterers
in the phantom. The script sim_motion.m is then called. Here the
field simulation is performed and the data is stored in RF-files; one for
each RF-line done. A data file called velocity.mat contains a matrix
with the blood velocities with respect to time and relative distance
to center of vessel.
References:
M. Schlaikjer, S.Torp-Pedersen, J.A. Jensen and P.F. Stetson:
Tissue motion in blood velocity estimation and its simulation,
IEEE Ultrasonics Symposium Proceeding, pp. 1495-1499, Volume 2, IEEE, 1998.
and
M. Schlaikjer, S.Torp-Pedersen and J.A. Jensen:
Simulation of RF data with tissue motion for optimizing stationary echo canceling filters,
Ultrasonics, vol: 41(6), p. 415-419 (2003)
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