Contrast of Delay Multiply And Sum on FI data from an UFF file

by Ole Marius Hoel Rindal olemarius@olemarius.net 28.05.2017

Setting up file path
Reading channel data from UFF file
Define Scan
Set up delay part of beamforming
Create the DMAS image using the delay_multiply_and_sum process
Beamform DAS image
Plot both images in same plot
Measure contrast

Setting up file path

To read data from a UFF file the first we need is, you guessed it, a UFF file. We check if it is on the current path and download it from the USTB websever.

clear all; close all;

% data location
url='http://ustb.no/datasets/';      % if not found downloaded from here
local_path = [ustb_path(),'/data/']; % location of example data
addpath(local_path);

% Choose dataset
filename='Alpinion_L3-8_FI_hypoechoic.uff';

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

Reading channel data from UFF file

uff_file=uff(filename)
channel_data=uff_file.read('/channel_data');

uff_file = 

  uff with properties:

    filename: 'Alpinion_L3-8_FI_hypoechoic.uff'
     version: 'v1.0.1'
        mode: 'append'
     verbose: 1

UFF: reading channel_data [uff.channel_data]
UFF: reading sequence [uff.wave]
Processed 256/256

%Print info about the dataset
channel_data.print_info

Name: 		 FI dataset of hypoechic cyst recorded on an  
		 Alpinion scanner with a L3-8 Probe from a CIRC  
		 General Purpose Ultrasound Phantom 
Reference: 	 www.ultrasoundtoolbox.com 
Author(s): 	 Ole Marius Hoel Rindal <olemarius@olemarius.net> 
		 Muyinatu Lediju Bell <mledijubell@jhu.edu> 
Version: 	 1.0.2

Define Scan

Define the image coordinates we want to beamform in the scan object. Notice that we need to use quite a lot of samples in the z-direction. This is because the DMAS creates an "artificial" second harmonic signal, so we need high enough sampling frequency in the image to get a second harmonic signal.

z_axis=linspace(34e-3,48e-3,768).';
sca=uff.linear_scan();
idx = 1;
for n=1:numel(channel_data.sequence)
    sca(n)=uff.linear_scan(channel_data.sequence(n).source.x,z_axis);
end

Set up delay part of beamforming

setting up and running the delay part of the beamforming

bmf=beamformer();
bmf.channel_data=channel_data;
bmf.scan=sca;

bmf.receive_apodization.window=uff.window.boxcar;
bmf.receive_apodization.f_number=1.7;
bmf.receive_apodization.apex.distance=Inf;

bmf.transmit_apodization.window=uff.window.none;
b_data=bmf.go({process.delay_matlab_light process.stack});

Create the DMAS image using the delay_multiply_and_sum process

dmas = process.delay_multiply_and_sum();
dmas.dimension = dimension.receive();
dmas.receive_apodization = bmf.receive_apodization;
dmas.transmit_apodization = bmf.transmit_apodization;
dmas.beamformed_data = b_data;
dmas.channel_data = channel_data;
dmas.scan = sca
b_data_dmas = dmas.go();        % Launch beamformer
b_data_dmas.plot(100,'DMAS');   % Display image

dmas = 

  delay_multiply_and_sum with properties:

               dimension: receive
            channel_data: [1×1 uff.channel_data]
     receive_apodization: [1×1 uff.apodization]
    transmit_apodization: [1×1 uff.apodization]
                    scan: [1×256 uff.linear_scan]
         beamformed_data: [1×1 uff.beamformed_data]
                    name: 'Delay Multiply and Sum'
               reference: 'Matrone, G., Savoia, A. S., & Magenes, G. (2015...'
          implemented_by: {'Ole Marius Hoel Rindal <olemarius@olemarius.net>'}
                 version: 'v1.0.2'

Warning: If the result looks funky, you might need to tune the filter paramters
of DMAS. Use the plot to check that everything is OK.

Beamform DAS image

Notice that I redefine the beamformer to use Hamming apodization.

bmf.receive_apodization.window=uff.window.hamming;
bmf.receive_apodization.f_number=1.7;
bmf.receive_apodization.apex.distance=Inf;

bmf.transmit_apodization.window=uff.window.none;

b_data_das=bmf.go({process.das_mex process.stack});
b_data_das.plot(2,'DAS');

Plot both images in same plot

Plot both in same plot with connected axes, try to zoom!

f3 = figure(3);clf
set(f3,'Position',[200,200,600,350])
b_data_dmas.plot(subplot(2,3,[1 2]),'DMAS'); % Display image
ax(1) = gca;
b_data_das.plot(subplot(2,3,[4 5]),'DAS'); % Display image
ax(2) = gca;
linkaxes(ax);

Measure contrast

Lets measure the contrast using the "contrast ratio" as our metric.

% First we need to put our images in a different data struct that the
% measure contrast function expects
images.all{1} = b_data_dmas.get_image();
images.all{2} = b_data_das.get_image();

% Define the coordinates of the regions used to measure contrast
xc_nonecho = -9.5;      % Center of cyst in X
zc_nonecho = 40.8;      % Center of cyst in Z
r_nonecho = 2.8;        % Radi of the circle in the cyst
r_speckle_inner = 4.5;  % Radi of the inner circle defining speckle region
r_speckle_outer = 7;    % Radi of the outer circle defining speckle region

% Call the "tool" to measure the contrast
[CR] = tools.measure_contrast_ratio(b_data_das,images,xc_nonecho,...
                    zc_nonecho,r_nonecho,r_speckle_inner,r_speckle_outer);

% Plot the contrast as a bar graph together with the two images
figure(3);hold on
subplot(2,3,[3 6]);
bar(CR)
set(gca,'XTickLabel',{'DMAS','DAS'})
title('Measured Contrast');
ylabel('CR');