http://www.k-wave.org/forum/topic/3d-ultrasound-delay-and-sum-das-and-image-reconstruction Support for the k-Wave MATLAB toolbox en-US Wed, 13 May 2026 00:23:58 +0000 http://bbpress.org/?v=1.0.2 q http://www.k-wave.org/forum/search.php http://www.k-wave.org/forum/topic/3d-ultrasound-delay-and-sum-das-and-image-reconstruction#post-6899 Sat, 22 Jun 2019 20:38:02 +0000 Bradley Treeby 6899@http://www.k-wave.org/forum/ <p>Have you looked at the <a href="https://www.ustb.no">Ultrasound Toolbox</a>? That might have something useful (I haven't used it myself). </p> http://www.k-wave.org/forum/topic/3d-ultrasound-delay-and-sum-das-and-image-reconstruction#post-6861 Mon, 27 May 2019 12:13:41 +0000 souradiprockz 6861@http://www.k-wave.org/forum/ <p>Okay thanks for the beamforming part. What about image reconstruction?<br /> how can we reconstruct the image using sensor data ('delay and sum algorithm') in K wave toolbox? </p> http://www.k-wave.org/forum/topic/3d-ultrasound-delay-and-sum-das-and-image-reconstruction#post-6563 Thu, 16 Aug 2018 23:58:22 +0000 bencox 6563@http://www.k-wave.org/forum/ <p>Hi nitai, </p> <p>Here's a simple 2D example demonstrating beamforming, in case it's helpful. The principles are identical in 3D. </p> <p>Best wishes,<br /> Ben</p> <pre><code>%-------------------------------------------------------------------------- % 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 = 0.05e-3; % grid point spacing in the x direction [m] dy = 0.05e-3; % 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 = {&#39;PMLInside&#39;, false, &#39;PMLSize&#39;, PML_size, &#39;Smooth&#39;, false, ... &#39;PlotPML&#39;, false, &#39;PlotLayout&#39;, true, &#39;PlotScale&#39;, [-0.25 0.25]}; % run the simulation sensor_data = kspaceFirstOrder2D(kgrid, medium, source, sensor, input_args{:}); %-------------------------------------------------------------------------- % Form the A-lines %-------------------------------------------------------------------------- % Reconstruction grid. Define the points for the A-line % (ie. the distance from the central element to each pixel on the A-line) pixel_distances = (0:0.25:Nx-1)*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 A-line, we need to choose one time point % for each element. Uses Pythagoras&#39; 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 A-lines 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, A-line formed as sum, no time delays A_line_sum = sum(sensor_data); % NO FOCUSSING, A-line 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,&#39;XLim&#39;,[min(kgrid.y_vec) max(kgrid.y_vec)]) title(&#39;sources on the x=0 axis&#39;) grid on subplot(3,1,2) plot(medium.sound_speed*kgrid.t_array + min(kgrid.y_vec), A_line_unfocussed) set(gca,&#39;XLim&#39;,[min(kgrid.y_vec) max(kgrid.y_vec)]) title(&#39;A-line from single unfocussed element&#39;) grid on subplot(3,1,3) plot(medium.sound_speed*kgrid.t_array + min(kgrid.y_vec), A_line_sum,&#39;k&#39;) hold on plot(pixel_distances + min(kgrid.y_vec), A_line_onefocus,&#39;r-&#39;) plot(pixel_distances + min(kgrid.y_vec), A_line_focussed,&#39;b--&#39;) set(gca,&#39;XLim&#39;,[min(kgrid.y_vec) max(kgrid.y_vec)]) title(&#39;A-lines with focussing&#39;) legend(&#39;simple sum&#39;,&#39;one focus&#39;,&#39;multi-focus&#39;) xlabel(&#39;x [m]&#39;) grid on</code></pre> http://www.k-wave.org/forum/topic/3d-ultrasound-delay-and-sum-das-and-image-reconstruction#post-6548 Sun, 12 Aug 2018 07:20:56 +0000 nitai 6548@http://www.k-wave.org/forum/ <p>does anyone know anything about it?<br /> will be very helpful<br /> thanks </p> http://www.k-wave.org/forum/topic/3d-ultrasound-delay-and-sum-das-and-image-reconstruction#post-6532 Tue, 24 Jul 2018 08:33:37 +0000 nitai 6532@http://www.k-wave.org/forum/ <p>Hello<br /> We are trying to apply beam forming using delay and sum on 3d ultrasound data (which we simulated using k-wave). 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 </p>