Acoustic Radiation Force Imaging from UFF file recorded with the Verasonics ARFI_L7 example

In this example we demonstrate the speckle tracking of displacement created by a shear wave induced with an acoustic "push" pulse. Also known as acoustic radiation force imaging, or share wave elastography. You will need an internet connection to download the data. Otherwise, you can run the ARFI_L7.m Verasonics example and create your own .uff file.

Ole Marius Hoel Rindal olemarius@olemarius.net 15.08.2017

Contents

Reading the channel data from the UFF file

close all;

% data location
url='http://ustb.no/datasets/';            % if not found data will be downloaded from here
local_path = [data_path(), filesep];  % location of example data on this computer

filename='ARFI_dataset.uff';

% check if the file is available in the local path & downloads otherwise
tools.download(filename, url, local_path);

Reading channel data from the UFF file and print out information about the dataset.

channel_data = uff.read_object([local_path filename],'/channel_data');
channel_data.print_authorship
UFF: reading channel_data [uff.channel_data]
Name: 		 CPWC dataset from a CIRS Elasticity QA Spherical  
		 Phantom. The dataset consists of 4 Verasonics  
		 _superframes_ with 50 super high framerate plane  
		 wave images after an acoustical radiation force  
		 push creating share waves. The image has a harder  
		 sphere at about x = 10 mm and z = 15 mm 
Reference: 	 www.ultrasoundtoolbox.com 
Author(s): 	 Ole Marius Hoel Rindal <olemarius@olemarius.net> 
		 Fabrice Prieur <fabrice@ifi.uio.no> 
Version: 	 1.0.0 

Beamform images

First, we need to beamform the images from the channel data. We'll do the usual drill of defining the scan and the beamformer object.

% SCAN
sca=uff.linear_scan();
sca.x_axis = linspace(channel_data.probe.x(1),channel_data.probe.x(end),256).';
sca.z_axis = linspace(0,30e-3,768).';

% Define processing pipeline
pipe=pipeline();
pipe.channel_data=channel_data;
pipe.scan=sca;

pipe.receive_apodization.window=uff.window.tukey50;
pipe.receive_apodization.f_number=1.7;

pipe.transmit_apodization.window=uff.window.tukey50;
pipe.transmit_apodization.f_number=1.7;

% Start the processing pipeline
b_data=pipe.go({midprocess.das postprocess.coherent_compounding});
USTB General beamformer MEX v1.1.2 .............done!

Show beamformed images

The b-mode images is really not that interesting. The displacement created by the shear waves are to small to be seen. Notice that we barely see a circular structure at about x = 10 and z = 15 mm. This is a sphere in the phantom which is somewhat harder than the surrounding structure.

b_data.plot(1,['B-mode'],[30]);

Estimate displacement

To actually see the shear wave propagation we need to estimate the displacement. This is done using a USTB process called autocorrelation_displacement_estimation. Have a look at its references to see the details.

disp = postprocess.autocorrelation_displacement_estimation();
disp.channel_data = channel_data;
disp.input = b_data;
disp.z_gate = 4;        % Nbr of samples to average estimate in depth / z
disp.x_gate = 2;        % Nbr of samples to average estimate in lateral / x
disp.packet_size = 6;   % How many frames to use in the estimate
displacement_estimation = disp.go();
disp.print_reference    % This is the references
disp.print_implemented_by    % Credits to the people who implemented it ;)
Reference:	 Barber, W. D., Eberhard, J. W., & Karr, S. G. (198 
		 5). A new time domain technique for velocity measu 
		 rements using Doppler ultrasound. IEEE Transaction 
		  on Biomedical Engineering, 32(3)Kasai, C., Nameka 
		 wa, K., Koyano, A., & Omoto, R. (1985). Real-Time  
		 Two-Dimensional Blood Flow Imaging Using an Autoco 
		 rrelation Technique. IEEE Transactions on Sonics a 
		 nd Ultrasonics, 32(3).Angelsen, B.A.J., & Kristoff 
		 ersen, K. (1983). Discrete time estimation of the  
		 mean Doppler frequency in ultrasonic blood velocit 
		 y measurements. IEEE Transaction on Biomedical Eng 
		 ineering, (4). 
Implemented by:	 Ole Marius Hoel Rindal <olemarius@olemarius.net>,  
		 Thomas B?rstad <thomas.borstad@gmail.com> 

We also have an alternative implementation also estimating the center frequency. This is the modified_autocorrelation_displacement_estimation. Please see the individiual implementations and references for more details.

disp_mod = postprocess.modified_autocorrelation_displacement_estimation();
disp_mod.channel_data = channel_data;
disp_mod.input = b_data;
disp_mod.z_gate = 4;        % Nbr of samples to average estimate in depth / z
disp_mod.x_gate = 2;        % Nbr of samples to average estimate in lateral / x
disp_mod.packet_size = 6;   % How many frames to use in the estimate
displacement_estimation_modified = disp_mod.go();
disp_mod.print_reference    % This is the references
disp_mod.print_implemented_by    % Credits to the people who implemented it ;)
Reference:	 Loupas, T., Powers, J. T., & Gill, R. W. (1995). A 
		 n Axial Velocity Estimator for Ultrasound Blood Fl 
		 ow Imaging, Based on a Full Evaluation of the Dopp 
		 ler Equation by Means of a Two-Dimensional Autocor 
		 relation Approach. IEEE Transactions on Ultrasonic 
		 s, Ferroelectrics and Frequency Control, 42(4).Bar 
		 ber, W. D., Eberhard, J. W., & Karr, S. G. (1985). 
		  A new time domain technique for velocity measurem 
		 ents using Doppler ultrasound. IEEE Transaction on 
		  Biomedical Engineering, 32(3)Kasai, C., Namekawa, 
		  K., Koyano, A., & Omoto, R. (1985). Real-Time Two 
		 -Dimensional Blood Flow Imaging Using an Autocorre 
		 lation Technique. IEEE Transactions on Sonics and  
		 Ultrasonics, 32(3).Angelsen, B.A.J., & Kristoffers 
		 en, K. (1983). Discrete time estimation of the mea 
		 n Doppler frequency in ultrasonic blood velocity m 
		 easurements. IEEE Transaction on Biomedical Engine 
		 ering, (4). 
Implemented by:	 Ole Marius Hoel Rindal <olemarius@olemarius.net>,  
		 Thomas B?rstad <thomas.borstad@gmail.com> 

Display the displacement estimate

Now, we can finally show the estimated displacement. This is nice to visualize as a movie.

In the movie we can clearly see the acoustic radiation force push that creates the share waves. The push is centered at x = 0 mm and z = 14 mm. Notice how the shear waves interacts with the harder spherical structure at x = 10 and z = 15 mm. The wavefront is moving faster through the harder tissue and some complex wavefronts are created.

Notice that we give the figure and the title as arguments to the plot function. The empty brackets [] is because we don't want to specify any dynamic range (well do that with the caxis function). And the 'none' is because we don't want to do any compression of the displacement data

f2 = figure(2);clf;
displacement_estimation.plot(f2,['Displacement'],[],'none');
caxis([-0.1*10^-6 0.2*10^-6]); % Updating the colorbar
colormap(gca(f2),'hot');       % Changing the colormap

f3 = figure(3);clf;
displacement_estimation_modified.plot(f3,['Displacement modified estimation'],[],'none');
caxis([-0.1*10^-6 0.2*10^-6]); % Updating the colorbar
colormap(gca(f3),'hot');       % Changing the colormap

Let's check the estimated center frequency and make sure that it is around 5 MHz as it should be. It is, but is the estimate any better?

figure(4);
imagesc(displacement_estimation_modified.scan.x_axis*1000,displacement_estimation_modified.scan.z_axis*1000,disp_mod.estimated_center_frequency(:,:,10));
xlabel('X [mm]');ylabel('Z [mm]');title('Estimated Center Frequency');
colorbar;

We can do it all in once!!

Since the autocorrelation_displacement_estimation is a process, we can trust the default paramters (which are the same as the ones used above) and do the beamforming and the displacement estimation all in one call!

And the pipe will check if some of the calculations allready have been done and skip them.

Isn't the USTB great?!

disp = postprocess.autocorrelation_displacement_estimation();
disp.channel_data = channel_data;

disp_img = pipe.go({midprocess.das postprocess.coherent_compounding ...
                                disp});
uff.apodization: Inputs and outputs are unchanged. Skipping process.
uff.apodization: Inputs and outputs are unchanged. Skipping process.
USTB General beamformer MEX v1.1.2 .............done!

Display the displacement Which gives us the same result as above.

f5 = figure(5);clf;
disp_img.plot(f5,['Displacement'],[],'none');
caxis([-0.1*10^-6 0.2*10^-6]); % Updating the colorbar
colormap(gca(f5),'hot');       % Changing the colormap