Contrast of Delay Multiply And Sum on FI data from an UFF file
by Ole Marius Hoel Rindal olemarius@olemarius.net
Last updated 07.08.2017
Contents
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.
close all; % data location url='http://ustb.no/datasets/'; % if not found downloaded from here local_path = [ustb_path(),'/data/']; % location of example data % 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
channel_data=uff.read_object([local_path filename],'/channel_data'); % Check that the user have the correct version of the dataset if(strcmp(channel_data.version{1},'1.0.2')~=1) error(['Wrong version of the dataset. Please delete ',local_path,... filename,' and rerun script.']); end
UFF: reading channel_data [uff.channel_data] UFF: reading sequence [uff.wave] [====================] 100%
%Print info about the dataset
channel_data.print_authorship
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-3,750).'; x_axis=zeros(channel_data.N_waves,1); for n=1:channel_data.N_waves x_axis(n) = channel_data.sequence(n).source.x; end scan=uff.linear_scan('x_axis',x_axis,'z_axis',z_axis);
Set up the processing pipeline
pipe=pipeline(); pipe.channel_data=channel_data; pipe.scan=scan; pipe.transmit_apodization.window=uff.window.scanline; pipe.receive_apodization.window=uff.window.none; pipe.receive_apodization.f_number=1.7;
Define the DAS beamformer
das = midprocess.das();
%Sum only on transmit, so that we can do DMAS on receice
das.dimension = dimension.transmit();
Create the DMAS image using the delay_multiply_and_sum postprocess
dmas = postprocess.delay_multiply_and_sum();
dmas.dimension = dimension.receive;
dmas.channel_data = channel_data;
dmas.receive_apodization = pipe.receive_apodization;
b_data_dmas=pipe.go({das dmas});
% beamforming
b_data_dmas.plot(100,'DMAS');
USTB General beamformer MEX v1.1.2 .............done!
uff.apodization: Inputs and outputs are unchanged. Skipping process.
f_stop =
single
10779358
Beamform DAS image
Notice that I redefine the beamformer to use Hamming apodization and summing on both transmit and receive.
das.dimension = dimension.both();
das.receive_apodization.window=uff.window.hamming;
das.receive_apodization.f_number=1.7;
b_data_das=pipe.go({das});
b_data_das.plot([],'DAS');
uff.apodization: Inputs and outputs are unchanged. Skipping process. USTB General beamformer MEX v1.1.2 .............done!
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');
