Hello
We are trying to apply beam forming using delay and sum on 3d ultrasound data (which we simulated using kwave). is there any code doing it? In addition, is there any prepared code which does an interpolation between the sampled data after beam forming) in order to reconstruct the image? thanks
kWave
A MATLAB toolbox for the timedomain
simulation of acoustic wave fields
3D UltraSound Delay and Sum (DAS) and image reconstruction
(5 posts) (4 voices)
Posted 11 months ago #

does anyone know anything about it?
will be very helpful
thanksPosted 10 months ago # 
Hi nitai,
Here's a simple 2D example demonstrating beamforming, in case it's helpful. The principles are identical in 3D.
Best wishes,
Ben% % Simulate time series from a linear detection array % % create the computational grid PML_size = 20; % size of the PML in grid points Nx = 128  2*PML_size; % number of grid points in the x (row) direction Ny = 128  2*PML_size; % number of grid points in the y (column) direction dx = 0.05e3; % grid point spacing in the x direction [m] dy = 0.05e3; % grid point spacing in the y direction [m] kgrid = kWaveGrid(Nx, dx, Ny, dy); % define the properties of the propagation medium medium.sound_speed = 1500; % [m/s] % create the time array [kgrid.t_array, dt] = makeTime(kgrid, medium.sound_speed); % define the sensor array N_elements = 16; % number of elements in the detector array element_spacing = 3; % spacing between elements [pixels] sensor.mask = zeros(Nx, Ny); sensor.mask((Nx/2 + 1  N_elements*element_spacing/2) : element_spacing :(Nx/2 + N_elements*element_spacing/2), 1) = 1; % create initial pressure distribution consisting of a few points source_spacing_x = 20; % x spacing between source points [pixels] source_spacing_y = 30; % y spacing between source points [pixels] source.p0 = zeros(Nx,Ny); source.p0(Nx/2+1 , Ny/2+1  source_spacing_y) = 1; source.p0(Nx/2+1 , Ny/2+1) = 1; source.p0(Nx/2+1 , Ny/2+1 + source_spacing_y) = 1; source.p0(Nx/2+1  source_spacing_x, Ny/2+1) = 1; source.p0(Nx/2+1 + source_spacing_x, Ny/2+1  source_spacing_y) = 1; % smooth the initial pressure distribution and restore the magnitude source.p0 = smooth(kgrid, source.p0, true); % set the input arguments: force the PML to be outside the computational % grid; switch off p0 smoothing within kspaceFirstOrder2D input_args = {'PMLInside', false, 'PMLSize', PML_size, 'Smooth', false, ... 'PlotPML', false, 'PlotLayout', true, 'PlotScale', [0.25 0.25]}; % run the simulation sensor_data = kspaceFirstOrder2D(kgrid, medium, source, sensor, input_args{:}); % % Form the Alines % % Reconstruction grid. Define the points for the Aline % (ie. the distance from the central element to each pixel on the Aline) pixel_distances = (0:0.25:Nx1)*dy; % [m] % find the distances of the elements from the element at x=0 element_distances = kgrid.x_vec(sensor.mask(:,1)==1); % [m] % distance of single focus from array focal_distance = (Ny/2+1)*dy; % [m] % Calculate which time points need to be summed together to synthesise % focussing. For each pixel in the Aline, we need to choose one time point % for each element. Uses Pythagoras' theorem. A_line_onefocus = zeros(size(pixel_distances)); A_line_focussed = zeros(size(pixel_distances)); for loop = 1:N_elements % time points which give just one focal point at focal_distance time_points1 = ( sqrt( focal_distance.^2 + element_distances(loop).^2 )  focal_distance + pixel_distances ) / medium.sound_speed; % time points which focus all along the line x = 0 time_points2 = sqrt( pixel_distances.^2 + element_distances(loop).^2 ) / medium.sound_speed; % convert time shifts to time indices (+1 as matlab indexing starts at 1 not 0) time_indices1 = round( time_points1 / dt ) + 1; time_indices2 = round( time_points2 / dt ) + 1; % sum the time series to make the Alines A_line_onefocus = sensor_data(loop,time_indices1) + A_line_onefocus; A_line_focussed = sensor_data(loop,time_indices2) + A_line_focussed; end % SIMPLE SUM, Aline formed as sum, no time delays A_line_sum = sum(sensor_data); % NO FOCUSSING, Aline from just the sensor at x=0 A_line_unfocussed = sensor_data(N_elements/2,:); figure subplot(3,1,1) plot(kgrid.y_vec,source.p0(Nx/2+1,:)) set(gca,'XLim',[min(kgrid.y_vec) max(kgrid.y_vec)]) title('sources on the x=0 axis') grid on subplot(3,1,2) plot(medium.sound_speed*kgrid.t_array + min(kgrid.y_vec), A_line_unfocussed) set(gca,'XLim',[min(kgrid.y_vec) max(kgrid.y_vec)]) title('Aline from single unfocussed element') grid on subplot(3,1,3) plot(medium.sound_speed*kgrid.t_array + min(kgrid.y_vec), A_line_sum,'k') hold on plot(pixel_distances + min(kgrid.y_vec), A_line_onefocus,'r') plot(pixel_distances + min(kgrid.y_vec), A_line_focussed,'b') set(gca,'XLim',[min(kgrid.y_vec) max(kgrid.y_vec)]) title('Alines with focussing') legend('simple sum','one focus','multifocus') xlabel('x [m]') grid on
Posted 10 months ago # 
Okay thanks for the beamforming part. What about image reconstruction?
how can we reconstruct the image using sensor data ('delay and sum algorithm') in K wave toolbox?Posted 4 weeks ago # 
Have you looked at the Ultrasound Toolbox? That might have something useful (I haven't used it myself).
Posted 2 days ago #
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