Computation of a STAI dataset with Field II using parameters of an L11-4v 128 element Verasonics Transducer and beamforming with USTB
This example shows how to load the data from a Field II simulation into USTB objects, and then beamform it with the USTB routines. This example uses the L11-4v 128 element Verasonics Transducer The Field II simulation program (field-ii.dk) should be in MATLAB's path.
This tutorial assumes familiarity with the contents of the 'CPWC simulation with the USTB built-in Fresnel simulator' tutorial. Please feel free to refer back to that for more details.
authors: Alfonso Rodriguez-Molares alfonso.r.molares@ntnu.no Ole Marius Hoel Rindal olemarius@olemarius.net
Last updated: 07.08.2017
Contents
Clear old workspace and close old plots
close all;
Basic Constants
Our first step is to define some basic constants for our imaging scenario - below, we set the speed of sound in the tissue, sampling frequency and sampling step size in time.
c0=1540; % Speed of sound [m/s] fs=100e6; % Sampling frequency [Hz] dt=1/fs; % Sampling step [s]
field II initialisation
Next, we initialize the field II toolbox. Again, this only works if the Field II simulation program (field-ii.dk) is in MATLAB's path. We also pass our set constants to it.
field_init(0); set_field('c',c0); % Speed of sound [m/s] set_field('fs',fs); % Sampling frequency [Hz] set_field('use_rectangles',1); % use rectangular elements
*------------------------------------------------------------*
* *
* F I E L D I I *
* *
* Simulator for ultrasound systems *
* *
* Copyright by Joergen Arendt Jensen *
* Version 3.30, April 5, 2021 (Matlab 2021a version) *
* Web-site: field-ii.dk *
* *
* This is citationware. Note the terms and conditions *
* for use on the web-site at: *
* field-ii.dk/?copyright.html *
* It is illegal to use this program, if the rules in the *
* copyright statement is not followed. *
*------------------------------------------------------------*
Warning: Remember to set all pulses in apertures for the new sampling frequency
Transducer definition L11-4v, 128-element linear array transducer
Our next step is to define the ultrasound transducer array we are using. For this experiment, we shall use the L11-4v 128 element Verasonics Transducer and set our parameters to match it.
probe = uff.linear_array(); f0 = 5.1333e+06; % Transducer center frequency [Hz] lambda = c0/f0; % Wavelength [m] probe.element_height = 5e-3; % Height of element [m] probe.pitch = 0.300e-3; % probe.pitch [m] kerf = 0.03e-03; % gap between elements [m] probe.element_width = probe.pitch-kerf;% Width of element [m] lens_el = 20e-3; % position of the elevation focus probe.N = 128; % Number of elements pulse_duration = 2.5; % pulse duration [cycles]
Pulse definition
We then define the pulse-echo signal which is done here using the fresnel simulator's pulse structure. We could also use 'Field II' for a more accurate model.
pulse = uff.pulse(); puse.center_frequency = f0; pulse.fractional_bandwidth = 0.65; % probe bandwidth [1] t0 = (-1/pulse.fractional_bandwidth/f0): dt : (1/pulse.fractional_bandwidth/f0); impulse_response = gauspuls(t0, f0, pulse.fractional_bandwidth); impulse_response = impulse_response-mean(impulse_response); % To get rid of DC te = (-pulse_duration/2/f0): dt : (pulse_duration/2/f0); excitation = square(2*pi*f0*te+pi/2); one_way_ir = conv(impulse_response,excitation); two_way_ir = conv(one_way_ir,impulse_response); lag = length(two_way_ir)/2+1; % We display the pulse to check that the lag estimation is on place % (and that the pulse is symmetric) figure; plot((0:(length(two_way_ir)-1))*dt -lag*dt,two_way_ir); hold on; grid on; axis tight plot((0:(length(two_way_ir)-1))*dt -lag*dt,abs(hilbert(two_way_ir)),'r') plot([0 0],[min(two_way_ir) max(two_way_ir)],'g'); legend('2-ways pulse','Envelope','Estimated lag'); title('2-ways impulse response Field II');
Aperture Objects
Next, we define the the mesh geometry with the help of Field II's xdc_linear_array function.
noSubAz=round(probe.element_width/(lambda/8)); % number of subelements in the azimuth direction noSubEl=round(probe.element_height/(lambda/8)); % number of subelements in the elevation direction Th = xdc_linear_array (probe.N, probe.element_width, probe.element_height, kerf, noSubAz, noSubEl, [0 0 Inf]); Rh = xdc_linear_array (probe.N, probe.element_width, probe.element_height, kerf, noSubAz, noSubEl, [0 0 Inf]); % We also set the excitation, impulse response and baffle as below: xdc_excitation (Th, excitation); xdc_impulse (Th, impulse_response); xdc_baffle(Th, 0); xdc_center_focus(Th,[0 0 0]); xdc_impulse (Rh, impulse_response); xdc_baffle(Rh, 0); xdc_center_focus(Rh,[0 0 0]);
Phantom
In our next step, we define our phantom. Here, our phantom is a single point scatterer.
g2=[[0.1e-3 0.2e-3 0.5e-3 1e-3 2e-3].' zeros(5,1) linspace(22e-3,18e-3,5).']; g1=[zeros(5,1) zeros(5,1) linspace(18e-3,22e-3,5).']; % list with the scatterers coordinates [m] sca=[g1; g2]; amp=ones(10,1); % list with the scatterers amplitudes
Output data
We define the variables to store our output data
cropat=round(1.1*2*sqrt((max(sca(:,1))-min(probe.x))^2+max(sca(:,3))^2)/c0/dt); % maximum time sample, samples after this will be dumped STA=zeros(cropat,probe.N,probe.N); % impulse response channel data
Compute STA signals
Now, we finally reach the stage where we generate a STA (Synthetic Transmit Aperture) dataset with the help of Field II.
disp('Field II: Computing STA dataset'); wb = waitbar(0, 'Field II: Computing STA dataset'); for n=1:probe.N waitbar(n/probe.N, wb); % transmit aperture xdc_apodization(Th, 0, [zeros(1,n-1) 1 zeros(1,probe.N-n)]); xdc_focus_times(Th, 0, zeros(1,probe.N)); % receive aperture xdc_apodization(Rh, 0, ones(1,probe.N)); xdc_focus_times(Rh, 0, zeros(1,probe.N)); % do calculation [v,t]=calc_scat_multi(Th, Rh, sca, amp); % build the dataset STA(1:size(v,1),:,n)=v; % Sequence generation seq(n)=uff.wave(); seq(n).probe=probe; seq(n).source.xyz=[probe.x(n) probe.y(n) probe.z(n)]; seq(n).sound_speed=c0; seq(n).delay = probe.r(n)/c0-lag*dt+t; % t0 and center of pulse compensation end close(wb);
Index exceeds the number of array elements (1).
Error in STAI_L11_resolution_phantom (line 127)
disp('Field II: Computing STA dataset');
Channel Data
In this part of the code, we creat a uff data structure to specifically store the captured ultrasound channel data.
channel_data = uff.channel_data(); channel_data.sampling_frequency = fs; channel_data.sound_speed = c0; channel_data.initial_time = 0; channel_data.pulse = pulse; channel_data.probe = probe; channel_data.sequence = seq; channel_data.data = STA./max(STA(:));
Scan
The scan area is defines as a collection of pixels spanning our region of interest. For our example here, we use the linear_scan structure, which is defined with two components: the lateral range and the depth range. scan too has a useful plot method it can call.
SCAN
sca=uff.linear_scan('x_axis',linspace(-2e-3,4e-3,256).', 'z_axis', linspace(17e-3,23e-3,256).');
Pipeline
With channel_data and a scan we have all we need to produce an ultrasound image. We now use a USTB structure pipeline, that takes an apodization structure in addition to the channel_data and scan.
pipe=pipeline(); pipe.channel_data=channel_data; pipe.scan=sca; pipe.receive_apodization.window=uff.window.boxcar; pipe.receive_apodization.f_number=1.7; pipe.transmit_apodization.window=uff.window.boxcar; pipe.transmit_apodization.f_number=1.7;
The pipeline structure allows you to implement different beamformers by combination of multiple built-in processes. By changing the process chain other beamforming sequences can be implemented. It returns yet another UFF structure: beamformed_data.
To achieve the goal of this example, we use delay-and-sum (implemented in the das_mex() process) as well as coherent compounding.
b_data=pipe.go({midprocess.das() postprocess.coherent_compounding()});
% Display images
b_data.plot()
Save UFF dataset
Finally, we save the data into a UFF file.
%channel_data.write([ustb_path(),'/data/FieldII_PSF_simulation_v2.uff'],'channel_data');