k-Wave User Forum » Topic: 2D time-of-flight reconstructions

k-Wave User Forum » Topic: 2D time-of-flight reconstructions http://www.k-wave.org/forum/topic/2d-time-of-flight-reconstructions Support for the k-Wave MATLAB toolbox en-US Tue, 12 May 2026 23:10:33 +0000 http://bbpress.org/?v=1.0.2 <![CDATA[Search]]> q http://www.k-wave.org/forum/search.php Bradley Treeby on "2D time-of-flight reconstructions" http://www.k-wave.org/forum/topic/2d-time-of-flight-reconstructions#post-7279 Fri, 06 Mar 2020 14:08:26 +0000 Bradley Treeby 7279@http://www.k-wave.org/forum/ Hi Panos, If you use <code>makeMultiArc</code>, you can also return an indexed matrix, where the indices correspond to the physical elements. If you collapse this to 1D vector and remove zeros, e.g., <pre><code>mask = arcs_labelled(:); mask(mask == 0) = [];</code></pre> you will then have an index mask you can use to extract the appropriate elements of the <code>sensor_data</code>, and then average, e.g., <code>sensor_data_element_1 = sensor_data(mask == 1, :);</code> Brad Panos on "2D time-of-flight reconstructions" http://www.k-wave.org/forum/topic/2d-time-of-flight-reconstructions#post-7240 Tue, 25 Feb 2020 17:57:28 +0000 Panos 7240@http://www.k-wave.org/forum/ Hi there, First of all many congratulations on this very helpful toolbox. I am working on travel-time ultrasound tomography. I am using frequency domain kernels (banana-doughnut kernels) to reconstruct sound speed profiles. I am building 2D simulations here to validate my experimental data and eventually to optimize them. My target is to create simulations data which could provide better reconstructions than the experimental ones (using the same system matrix - in a back-projection concept). At first, I used point-sensors but the results weren't that good. Now I am trying to give dimensions to my sensors (defining every sensor as a number of points(nodes of the grid)). The problem that I am facing is that I cannot completely understand in which order the sensor_data structure is created. As I am using all the massive data (coming from the point-sensors) to average them in order to create an averaged signal for every actual sensor. For instance, in the attached example the amount of sensor_data is 368, while I have to link them to 32 actual sensors. Can you help me with? I put my code here: %----% clear all; close all; clc; %% GRID - Centre Frequency figure(); % computational grid Nx = 256; % number of grid points in the x (row) direction Ny = 256; % number of grid points in the y (column) direction spacing=370/Nx; dx = spacing*0.001; % grid point spacing in the x direction [m] dy = spacing*0.001; % grid point spacing in the y direction [m] pxl_mm=dx*1000; kgrid = kWaveGrid(Nx, dx, Ny, dy); %% Define a centered circular sensor sensor_radius = (329/2)*0.001; % [m] num_sensor_points = 32; sensor.mask = makeCartCircle(sensor_radius, num_sensor_points); % Sensors (grid) a=(sensor.mask./dx)+(Nx/2); %% Medium Porperties % DEFINE MEDIUM % WATER medium.sound_speed = 1480 * ones(Nx, Ny); % [m/s] medium.density = 9980 * ones(Nx, Ny); % [kg/m^3] % INCLUSION % Small_Inclusion inc_centre_x=min(min(a))+(225/pxl_mm); inc_centre_y=Nx-(min(min(a))+(275/pxl_mm)); radius=(14/pxl_mm); % plot(inc_centre_x,inc_centre_y,'*black');hold on; [columnsInImage rowsInImage] = meshgrid(1:Nx, 1:Nx); circlePixels = (rowsInImage - inc_centre_y).^2 ... + (columnsInImage - inc_centre_x).^2 <= radius.^2; [posx,posy]=find((circlePixels)~=0); % % Air bottle for i=1:length(posx) medium.sound_speed(posx(i), posy(i)) =343; %343; medium.density(posx(i), posy(i)) =18; %1.2; end %% Sources mask=zeros(Nx); for i=1:32 masks{i}=zeros(Nx); end mask3=zeros(Nx); for i=1:32 source_x(i)=sensor.mask(2,i); source_y(i)=sensor.mask(1,i); % disc_x_pos =(floor(source_x(i)/dx)+(Nx/2)); % [grid points] disc_y_pos =(floor(source_y(i)/dy)+(Nx/2)); % [grid points] % masks{i}(disc_x_pos,disc_y_pos)=1; % arc_pos = [disc_x_pos, disc_y_pos]; % [grid points] radius = 25; % [grid points] diameter = 13; % [grid points] focus_pos = [Nx/2, Nx/2]; % [grid points] % source.p_mask = makeArc([Nx, Ny], arc_pos, radius, diameter, focus_pos); masks{i} = makeArc([Nx, Ny], arc_pos, radius, diameter, focus_pos); % sensor_node_num{i}=sum(masks{i}(:) == 1); % mask3=mask3+masks{i}; mask1=mask3; mask1(mask1==1)=-1200; end %% Display mask1=mask1+medium.sound_speed; mask2=mask1; mask2(mask2==280)=1480; sensor1.mask=mask3; medium.sound_speed=mask2; %% Displays imagesc(mask2);colorbar; view(0,-90); title('Domain'); figure; imagesc(mask1); hold on; plot(a(1,:),a(2,:),'*r'); title('Geometry'); figure; imagesc(mask3); hold on; plot(a(1,:),a(2,:),'*r'); title('Sensors'); %% pause; figure(); % --- SIMULATION --- % Running the Tomographic scenario for i=1:length(sensor.mask) % Sources source_x(i)=sensor.mask(2,i); source_y(i)=sensor.mask(1,i); source.p_mask=masks{i}; % Source_Excitation_ToneBurst tone_burst_freq=480e3; sampling_freq=40 *tone_burst_freq; tone_burst_cycles=3; k = toneBurst(sampling_freq,tone_burst_freq,tone_burst_cycles,'Plot', true); source.p=30*k; figure(); % source.p_mode='dirichlet'; % run the simulation kgrid.t_array = makeTime(kgrid, 1480, 0.3, 0.00042 ); %recording time 0.5 msec (0.5e-3) - auto t_end=0.005 (increasing t_end one decreases the recording time) sensor_data{i} = kspaceFirstOrder2D(kgrid, medium, source, sensor1,'PMLInside',true); % % plot the simulated sensor data figure; imagesc(sensor_data{i}, [-1, 1]); colormap(getColorMap); ylabel('Sensor Position'); xlabel('Time Step'); colorbar; end %----%