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k-Wave User Forum » User Favorites: daga_pankaj daga_pankaj Support for the k-Wave MATLAB toolbox en-US Fri, 06 Mar 2026 02:42:30 +0000 http://bbpress.org/?v=1.0.2 <![CDATA[Search]]> q http://www.k-wave.org/forum/search.php Bradley Treeby on "Error in mode linear transducer example" http://www.k-wave.org/forum/topic/error-in-mode-linear-transducer-example#post-9060 Wed, 20 Mar 2024 19:36:43 +0000 Bradley Treeby 9060@http://www.k-wave.org/forum/ Thanks for reporting back. We haven’t played with Octave for a long time, so I’m not sure what the compatibility is like I’m afraid. daga_pankaj on "Error in mode linear transducer example" http://www.k-wave.org/forum/topic/error-in-mode-linear-transducer-example#post-9031 Wed, 31 Jan 2024 15:46:15 +0000 daga_pankaj 9031@http://www.k-wave.org/forum/ Ok, some more details. This seems to be octave related. I got my hands on a Matlab licence and the code works fine. Also, the code in Matlab is about 7x times faster than octave. Matlab version: 2021b Octave: 8.4.0 System: Apple M2 Pro 32GB OS: Sonoma 14.3 daga_pankaj on "Error in mode linear transducer example" http://www.k-wave.org/forum/topic/error-in-mode-linear-transducer-example#post-9029 Tue, 30 Jan 2024 18:23:27 +0000 daga_pankaj 9029@http://www.k-wave.org/forum/ The error seems to be coming from this <code>isequal</code> function comparing whether the source is used as a sensor as well... <code>if ~(strcmp(class(sensor), 'kWaveTransducer') && isequal(sensor, source))</code> Also, if I remove this line in the example script: <code>transducer.elevation_focus_distance = 19e-3; % focus distance in the elevation plane [m]</code> Then it seems to not get this error. I am just getting started on this. Can someone explain what impact this might have on the simulation? daga_pankaj on "Error in mode linear transducer example" http://www.k-wave.org/forum/topic/error-in-mode-linear-transducer-example#post-9024 Mon, 29 Jan 2024 18:55:43 +0000 daga_pankaj 9024@http://www.k-wave.org/forum/ I am trying to run the BMode linear transducer example (in octave) and run into the following error: <pre><code>Computing scan line 1 of 96 Running k-Wave simulation... start time: 29-Jan-2024 19:47:05 reference sound speed: 1600.5115m/s prepending transducer.input_signal with 17 leading zeros appending transducer.input_signal with 17 trailing zeros dt: 36.075ns, t_end: 57.1429us, time steps: 1585 input grid size: 216 by 108 by 108 grid points (40 by 20 by 20mm) maximum supported frequency: 3.78MHz expanding computational grid... error: elevation_delay_times(0): subscripts must be either integers 1 to (2^63)-1 or logicals error: called from delay_mask at line 1000 column 45 get.input_signal at line 578 column 28 isequal at line 86 column 7 kspaceFirstOrder_expandGridMatrices at line 190 column 12 kspaceFirstOrder_inputChecking at line 1558 column 5 kspaceFirstOrder3D at line 574 column 1 example_us_bmode_linear_transducer at line 242 column 21</code></pre> Seems like an indexing issue. I can run most of the other examples in Octave, so this seems like some script error somewhere. Bradley Treeby on "Artifacts in Plane Wave Simulations using kWaveArray" http://www.k-wave.org/forum/topic/artifacts-in-plane-wave-simulations-using-kwavearray#post-9011 Wed, 24 Jan 2024 10:17:15 +0000 Bradley Treeby 9011@http://www.k-wave.org/forum/ I haven't run your simulation, but the effects you're describing is what I would expect to happen given the way sources are modelled in k-Wave. Depending on the transducer design, you also see this in experimental measurements. One simple way to remove the transmitted wave from the receive data would be to run two simulations, one in a homogeneous medium, and then one with the medium properties you are trying to image, and subtract the RF data of the first from the second. nikunj101 on "Artifacts in Plane Wave Simulations using kWaveArray" http://www.k-wave.org/forum/topic/artifacts-in-plane-wave-simulations-using-kwavearray#post-8987 Wed, 13 Dec 2023 18:10:16 +0000 nikunj101 8987@http://www.k-wave.org/forum/ Hi, I am currently working on simulating RFData generated by plane wave emissions from a linear array transducer. In this process, I am encountering several artifacts in my simulations, particularly when recombining sensor data using kWaveArray. The primary issue involves the inadequate cancellation of signals from adjacent elements, especially noticeable when the transmit angle deviates from zero. This results in unwanted signals appearing in the simulated RFData. Additionally, I observe an edge effect. The edges of my simulated array produce spherical wave patterns that propagate across the array, creating an X-like artifact in the RFData. While similar phenomena occur in practical scenarios with linear arrays, the inherent directionality of actual transducer elements usually reduces the intensity of this artifact. In my simulation using kWave, even though the spherical sources are non-directional, I expect the line elements implemented via kWaveArray to exhibit some directionality, so I'm surprised I'm seeing as strong an artifact as I am. I have tried applying a roll-off apodization along the array edges to mitigate this issue, but I am seeking alternative solutions. Could you suggest other methods to reduce the edge effect artifact? Additionally, how might I address the interference artifact arising from neighboring elements? Here is a minimal working example of my code using a homogenous medium. You can adjust the transmit angle by changing the TXangle parameter in the first section: <code></code>` % Coherent Plane Wave Compounding Simulation Using K-Wave % % This script demonstrates how to setup a linear array transducer using the % kWaveArray class for coherent plane wave compounding in ultrasound imaging. % It involves creating a simulation grid, defining a medium with point targets, % initializing a transducer array, generating a focused source, simulating wave % propagation, and collecting sensor data. % % Author: Nikunj Khetan % Last Update: 21st November 2023 clearvars; % close all %% DEFINE LITERALS - Setting up parameters for the simulation % Selection of K-Wave code execution model model = 1; % Options: 1 - MATLAB CPU, 2 - MATLAB GPU, 3 - C++ code, 4 - CUDA code USE_STATISTICS = true; % set to true to compute the rms or peak beam patterns, set to false to compute the harmonic beam patterns % Medium parameters c0 = 1540; % Sound speed in the medium [m/s] rho0 = 1020; % Density of the medium [kg/m^3] % Source parameters source_f0 = (250/48)*1e6; % Frequency of the ultrasound source [Hz] source_amp = 1e6; % Amplitude of the ultrasound source [Pa] source_cycles = 3; % Number of cycles in the tone burst signal numEl = 128; % Number of elements in the transducer array element_width = 2.3e-4; % Width of each transducer element [m] element_pitch = 2.3e-4; % Pitch - distance between the centers of adjacent elements [m] RF_fs = source_f0*4; % Sampling Frequency of final RFData % Define transmission angles for plane wave compounding na = 1; % Number of angles for transmission TXangle = 10*pi/180; % Grid parameters grid_size_x = 40e-3; % Grid size in x-direction [m] grid_size_y = 10e-3; % Grid size in y-direction [m] % Computational parameters ppw = 4; % Points per wavelength t_end = round(grid_size_y/c0,6); % Simulation duration [s] cfl = 0.5; % Courant-Friedrichs-Lewy (CFL) number for stability %% GRID - Creating the computational grid % Calculate grid spacing based on PPW and source frequency dx = c0 / (ppw * source_f0); % Grid spacing [m] dy = dx; % Compute grid size Nx = roundEven(grid_size_x / dx); % Number of grid points in x-direction Ny = roundEven(grid_size_y / dy); % Number of grid points in y-direction % Create the computational grid kgrid = kWaveGrid(Nx, dx, Ny, dy); % Create the time array kgrid.makeTime(c0, cfl, t_end); dsFactor = (1/kgrid.dt)/RF_fs; %% SOURCE - Initializing the transducer array and source signal [karray, ElemPos] = initArray(kgrid, numEl, element_pitch, element_width); % Create source signal using a tone burst source_sig = source_amp .* toneBurst(1/kgrid.dt, source_f0, source_cycles); %% MEDIUM - Defining the medium properties medium.sound_speed = c0 * ones([Nx, Ny]); % sound speed [m/s] medium.density = rho0 * ones([Nx, Ny]); % density [kg/m3] %% SENSOR - Setting up the sensor mask and properties % Assign binary mask from karray to the sensor mask sensor.mask = karray.getArrayBinaryMask(kgrid); % set the record mode such that only the rms and peak values are stored sensor.record = {'p'}; % Define frequency response of the sensor sensor.frequency_response = [source_f0, 100]; %% SIMULATION - Running the simulation for different transmission angles % Preallocate arrays for time delays and RF data time_delays = zeros(na, numEl); % Simulation input options input_args = {'PMLSize', 'auto', 'PMLInside', false, 'PlotPML', false, 'DisplayMask', 'off'}; RFData = zeros(numEl, kgrid.Nt, na); % Loop over each angle for plane wave compounding for i = 1:na [source, time_delays(i, :)] = genSource(kgrid, source_f0, source_cycles, source_amp, TXangle(i), karray, ElemPos, c0); sensor_data = runSim(kgrid, medium, source, sensor, input_args, model,source_amp); RFData(:, :, i) = karray.combineSensorData(kgrid, sensor_data.p); end % Rearrange RF data dimensions for further processing RFData = downsample(flip(permute(RFData, [2, 1, 3]),2),dsFactor); figure colormap gray imagesc(log10(abs(RFData(:,:,1)))) %% HELPER FUNCTIONS function [karray, ElemPos] = initArray(kgrid, element_num, element_pitch, element_width) % Initializes the transducer array. % Args: % kgrid: The k-Wave grid object. % element_num: Number of elements in the array. % element_pitch: Distance between the centers of adjacent elements. % element_width: Width of each element. % Returns: % karray: The k-Wave array object. % ElemPos: The positions of the elements in the array. % Create empty kWaveArray object with specified BLI tolerance and upsampling rate karray = kWaveArray('BLITolerance', 0.05, 'UpsamplingRate', 10); % Calculate the center position for the first element L = element_num * element_pitch / 2; ElemPos = -(L - element_pitch / 2) + (0:element_num - 1) * element_pitch; % Add rectangular elements to the array for ind = 1:element_num % Set element position x_pos = ElemPos(ind); % Define start and end points of the element start_point = [x_pos - element_width / 2, kgrid.y_vec(1)]; end_point = [x_pos + element_width / 2, kgrid.y_vec(1)]; % Add line element to the array karray.addLineElement(start_point, end_point); end end function [source, time_delays] = genSource(kgrid, source_f0, source_cycles, source_amp, theta, karray, ElemPos, c0) % Generates the source signal with time delays for each transducer element. % Args: % kgrid: The k-Wave grid object. % source_f0: Frequency of the source. % source_cycles: Number of cycles in the tone burst signal. % source_amp: Amplitude of the source. % theta: Steering angle of the plane wave. % karray: The k-Wave array object. % ElemPos: The positions of the elements in the array. % c0: Speed of sound. % Returns: % source: The source object containing the mask and signals. % time_delays: Time delays applied to each element for focusing. % Calculate time delays for each element based on steering angle time_delays = ElemPos * sin(theta) / c0; time_delays = time_delays - min(time_delays); % Create time-varying source signals for each physical element source_sig = source_amp .* toneBurst(1/kgrid.dt, source_f0, source_cycles, 'SignalOffset', round(time_delays / kgrid.dt)); % Assign binary mask for the source source.p_mask = karray.getArrayBinaryMask(kgrid); % Assign source signals to the source object source.p = karray.getDistributedSourceSignal(kgrid, source_sig); end function [sensor_data] = runSim(kgrid, medium, source, sensor, input_args, model, source_amp) % Runs the simulation based on the selected model (CPU or GPU). % Args: % kgrid: The k-Wave grid object. % medium: The medium in which waves propagate. % source: The source object containing the ultrasound signal. % sensor: The sensor object to record the pressure. % input_args: Additional input arguments for the simulation. % model: The selected model for running the simulation. % Returns: % sensor_data: The recorded sensor data from the simulation. % Run the simulation based on the chosen model switch model case 1 % MATLAB CPU sensor_data = kspaceFirstOrder2D(kgrid, medium, source, sensor, ... input_args{:}, ... 'DataCast', 'single', ... 'PlotScale', [-1, 1] * source_amp); case 2 % MATLAB GPU sensor_data = kspaceFirstOrder2D(kgrid, medium, source, sensor, ... input_args{:}, ... 'DataCast', 'gpuArray-single', ... 'PlotScale', [-1, 1] * source_amp); case 3 % C++ code sensor_data = kspaceFirstOrder2DC(kgrid, medium, source, sensor, input_args{:}); case 4 % C++/CUDA GPU sensor_data = kspaceFirstOrder2DG(kgrid, medium, source, sensor, input_args{:}); end end <code></code>` Bradley Treeby on "Understanding Absorption Parameters medium.alpha_coeff and medium.alpha_power" http://www.k-wave.org/forum/topic/understanding-absorption-parameters-mediumalpha_coeff-and-mediumalpha_power#post-7857 Tue, 29 Sep 2020 08:37:00 +0000 Bradley Treeby 7857@http://www.k-wave.org/forum/ Hi emmal, By default, a perfectly matched layer (PML) is used at the walls, so the simulation will be the same as in an anechoic environment (you can read more about this in the examples and user manual). You can find some details about the absorption in air in <a href="http://www.k-wave.org/forum/topic/absorbtion-coefficents#post-522">this post</a>. Brad emmal on "Understanding Absorption Parameters medium.alpha_coeff and medium.alpha_power" http://www.k-wave.org/forum/topic/understanding-absorption-parameters-mediumalpha_coeff-and-mediumalpha_power#post-7856 Mon, 28 Sep 2020 21:34:27 +0000 emmal 7856@http://www.k-wave.org/forum/ I'm new to K-wave and confused about what the absorption parameters, defined by the alpha parameters (medium.alpha_coeff and medium.alpha_power), should be set to in the code or how to go about understanding what one would want to set them to. A project I'm working on is simulating a low-frequency (120-230 Hz), linear phased array with four point sources representing transducers. Also, my group wants the sound waves to be absorbed completely at the walls and the medium to behave like atmospheric air in an anechoic environment. Attached is a section of our code showing these parameters. The alpha coefficient and alpha power values were set based on a sample simulation. % ================= % SIMULATION % ================= % create the computational grid Nx = 250; % number of grid points in the x (row) direction Ny = Nx; % number of grid points in the y (column) direction dx = .01; % grid point spacing in the x direction [m] dy = dx; % grid point spacing in the y direction [m] kgrid = kWaveGrid(Nx, dx, Ny, dy); % define the properties of the propagation medium -- need to understand % these a bit better. 0.97 was selected based upon anechoic chambers at low % frequencies, but this determines properties of the PML medium.sound_speed = 330; % [m/s] medium.alpha_coeff = 1; % [dB/(MHz^y cm)] medium.alpha_power = 1.5;