k-Wave Toolbox |
![]() ![]() |
On this page… |
---|
Defining a large element detector Defining a collection of point sources |
This example demonstrates how the sensitivity of a large single element detector varies with the angular position of a point-like source. It is a 3D version of the Modelling Sensor Directivity in 2D example.
A large area detector is defined as a rectangle and
stored in sensor.mask
.
% define a large area detector sensor.mask = zeros(Nx, Ny, Nz); sensor.mask(Nx/2+1, (Ny/2-9):(Ny/2+11),(Nz/2-9):(Nz/2+11)) = 1;
Equi-spaced point sources are defined at a fixed distance from the centre
of the detector face. To do this, the Cartesian coordinates of the points are
calculated using makeCartCircle
and then a binary source mask corresponding to these Cartesian points is calculated using cart2grid
.
The indices of the matrix elements for which the binary mask is equal to 1
(the source points) are found using find
.
% define equally spaced point sources lying on a circle centred at the % centre of the detector face Nangles = 11; circle = makeCartCircle(25*dx, Nangles, [0,0], pi); circle = [circle; zeros(1,Nangles)]; % find the binary sensor mask most closely corresponding to the cartesian % points coordinates from makeCartCircle circle3D = cart2grid(kgrid,circle); % find the indices of the sources in the binary source mask source_positions = find(circle3D == 1);
A time varying pressure source is defined to drive the point sources.
% define a time varying sinusoidal source source_freq = 0.25e6; source_mag = 1; source.p = source_mag*sin(2*pi*source_freq*kgrid.t_array); % smooth the source source.p = filterTimeSeries(kgrid, medium, source.p);
For each point source, a binary source mask is set to one at the point corresponding to that source, and the simulation is run. When each simulation has finished, the calculated time series (one for each grid poing of the detector) are summed together to mimic the fact that in this example the grid points together define one single large measuring element. (This is in contrast to when each grid point is treated as a element of an array, see Steering A Linear Array Example.)
for source_loop = 1:length(source_positions) % select a point source source.p_mask = zeros(Nx,Ny,Nz); source.p_mask(source_positions(source_loop)) = 1; % create a display mask to display the transducer display_mask = source.p_mask + sensor.mask; % run the simulation input_args = {'PMLSize', 10, 'DisplayMask', display_mask, 'PlotScale', [-0.2 0.2], 'PlotFreq', 50, 'DataCast', 'single'}; sensor_data = kspaceFirstOrder3D(kgrid, medium, source, sensor, input_args{:}); % average the data recorded for each pixel to simulate the measured signal % from a large aperture, single element, detector single_element_data(:,source_loop) = sum(sum(sensor_data,1),1); end
Finally, the maxima of the time series, one per point source, are plotted as a function of the angle between the detector plane and a line joining the point source and the centre of the detector face. The directionality introduced by the large size of the detector (because it averages the incoming wave over its area) is clearly seen.
![]() |
Modelling Sensor Directivity in 2D | Sensor Element Directivity in 2D | ![]() |
© 2009-2012 Bradley Treeby and Ben Cox.