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Brain2DFocus.m
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Brain2DFocus.m
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%% Carregar modelo cerebro e objetivo a focar
clear; clc;
% carregar modelo cerebro
%brain_model = niftiread('brain_model.nii');
brain_model = load('brain_model_skull.mat'); brain_model = brain_model.brain_model;
brain_model = brain_model(25:225, 35:274, 39:239); % dimunuir espaço inutil
% definir uma slice
model = squeeze(brain_model(85,:,:));
% visualizar o modelo
figure;
imshow(model);
% definir sequencia de pontos a focar com ultrassom
%{
target_points = [[70, 80];...
[90, 110];...
[130, 90];...
[160, 140]]
%}
target_points = [[80, 75]];
% mascara com os pontos que irão ser focados
focus_points_mask = zeros(size(model));
for ipoint=1:size(target_points,1)
focus_points_mask(target_points(ipoint,1), target_points(ipoint,2)) = 1;
end
%% Definir o meio e suas propriedades
% create the 2D - computational grid
[Nx, Ny] = size(model); % number of grid points in the X/Y direction
dx = 1e-3; % grid point spacing in the X direction [m]
dy = 1e-3; % grid point spacing in the Y direction [m]
dz = 1e-3; % grid point spacing in the Z direction [m]
kgrid = kWaveGrid(Nx, dx, Ny, dy);
% define the properties of the propagation medium
%tissue speed[m/s] density(Kg/m3) absortion[dB/(MHz.cm)] img-pixel-values
%air - 343.0 - 1.20 - 0.0004 - [0]*
%water - 1475.0 - 1000 - 0.05? - [0]*
%midbrain - 1546.3 - 1000 - 0.6 - [21-39] U [51-78]
%white matter - 1552.5 - 1050 - 0.6 - [40-50]
%grey matter - 1500.0 - 1100 - 0.6 - [81-220]
%cSpinalFluid - 1475.0 - 1000 - 0.05 - [1-9]
%scalp - 1540.0 - 1000 - 0.1 - [10-20]
%skull - 3476.0 - 1979 - 2.7 - [221-255]
%* caso seja usado agua ou ar como meio exterior
% speed [m/s]
medium.sound_speed = 1500 * ones(Nx, Ny); % default
%medium.sound_speed(model==0)=343.0 ; % air
medium.sound_speed(model==0)=1504.0; % water
medium.sound_speed(model>=21 & model<=78)=1546.3; % midbrain
medium.sound_speed(model>=40 & model<=50)=1552.5; % white matter
medium.sound_speed(model>=81 & model<=220)=1500.0; % grey matter
medium.sound_speed(model>=1 & model<=9)=1475.0; % cerebroSpinalFluid
medium.sound_speed(model>=10 & model<=20)=1540.0; % scalp
medium.sound_speed(model>=221)=3476.0; % skull
% density [Kg/m3]
medium.density = 1000 * ones(Nx, Ny); % default
%medium.density(model==0)=1.20; % air
medium.density(model==0)=1000; % water
medium.density(model>=21 & model<=78)=1075; % midbrain
medium.density(model>=40 & model<=50)=1050; % white matter
medium.density(model>=81 & model<=220)=1100; % grey matter
medium.density(model>=1 & model<=9)=1000.0; % cerebroSpinalFluid
medium.density(model>=10 & model<=20)=1000.0; % scalp
medium.density(model>=221)=1969.0; % skull
% absortion [dB/(MHz^y cm)]
medium.alpha_power = 1.5; % default
medium.alpha_coeff = 0.75 * ones(Nx, Ny); % default
%medium.alpha_coeff(model==0)=1.6; % air
medium.alpha_coeff(model==0)=0.05; % water
medium.alpha_coeff(model>=21 & model<=78)=0.6; % midbrain
medium.alpha_coeff(model>=40 & model<=50)=0.6; % white matter
medium.alpha_coeff(model>=81 & model<=220)=0.6; % grey matter
medium.alpha_coeff(model>=1 & model<=9)=0.05; % cerebroSpinalFluid
medium.alpha_coeff(model>=10 & model<=20)=0.1; % scalp
medium.alpha_coeff(model>=221)=2.7; % skull
% tempo e step de simulação
kgrid.makeTime(medium.sound_speed);
%% Definir source de ondas Ultrassonoras
n_elements = 37; % grid points - impar
sensor.mask = zeros(Nx, Ny);
source2use = 1;
%Source Position:
% 1 - Array de pontos sequenciais colados ao topo do cerebro
% 2 - Array de pontos sequenciais colados ao topo e abaixo do cerebro
% 3 - Array linear em cima do cerebro
% 4 - Pontos aleatorios no lado de cima do cerebro
% 5 - Pontos completamente aleatorios
if source2use==1
%%%%%%% Probe com varios elementos colados em cima do cerebro %%%%%%%%%
array_step=6;
array_center_pos_y = size(model,1)/2;
for ypos=0:array_step:array_step*(n_elements-1)/2 % puts all aray elements in contact with the brain
% lado positivo em relacao ao centro
xpos = find(model(array_center_pos_y+ypos,:)>0,1,'last');
sensor.mask(array_center_pos_y+ypos, xpos) = 1;
% lado negativo em relacao ao centro
xpos = find(model(array_center_pos_y-ypos,:)>0,1,'last');
sensor.mask(array_center_pos_y-ypos, xpos) = 1;
end
end
if source2use==2
%%%%%%% Probe com varios elementos colados em cima e baixo do cerebro %%%%%%%%%
array_step=5;
n_elements=(n_elements-1)/2;
array_center_pos_y = size(model,1)/2;
for ypos=0:array_step:array_step*(n_elements-1)/2 % puts all aray elements in contact with the brain
% lado positivo em relacao ao centro
xpos = find(model(array_center_pos_y+ypos,:)>0,1,'last'); % cima
sensor.mask(array_center_pos_y+ypos, xpos) = 1;
xpos = find(model(array_center_pos_y+ypos,:)>0,1,'first'); % baixo
sensor.mask(array_center_pos_y+ypos, xpos) = 1;
% lado negativo em relacao ao centro
xpos = find(model(array_center_pos_y-ypos,:)>0,1,'last'); % cima
sensor.mask(array_center_pos_y-ypos, xpos) = 1;
xpos = find(model(array_center_pos_y-ypos,:)>0,1,'first'); % baixo
sensor.mask(array_center_pos_y-ypos, xpos) = 1;
end
end
if source2use==3
%%%%%%%%%%%%%%%%%%%% Probe linear em cima do cerebro %%%%%%%%%%%%%%%%%%%%%
array_step=1;
array_center_pos_y = size(model,1)/2;
max_brain_x = 0;
for ypos=0:array_step:array_step*(n_elements-1)/2 % find outer brain position for array
max_brain_x = max(max_brain_x, find(model(array_center_pos_y+ypos,:)>0,1,'last'));
max_brain_x = max(max_brain_x, find(model(array_center_pos_y-ypos,:)>0,1,'last'));
end
sensor.mask(array_center_pos_y-ypos:array_step:array_center_pos_y+ypos, max_brain_x)=1;
end
if source2use==4
%%%%%%%%%%%% Fontes aleatorias do lado de cima do cerebro %%%%%%%%%%%%%
sensor_points = sort(randi(numel(model)/3,[n_elements,1])) + numel(model)*(2/3);
vector_mask = zeros(prod([Nx,Ny]),1);
vector_mask(sensor_points)=1;
sensor.mask = reshape(vector_mask, [Nx,Ny]);
end
if source2use==5
%%%%%%%%%%%% Fontes aleatorias na matriz %%%%%%%%%%%%%
sensor_points = sort(randi(numel(model),[n_elements,1]));
vector_mask = zeros(prod([Nx,Ny]),1);
vector_mask(sensor_points)=1;
sensor.mask = reshape(vector_mask, [Nx,Ny]);
end
%% Mostra todo o layout(Densidade/VelocidadeSom/Fontes/Targets)
figure;
subplot(2,2,1); % Densidade do meio
imagesc(medium.density);
colormap(gray);
b=colorbar;
title('Densidade');
xlabel(b, '[Kg/L]');
subplot(2,2,3); % Velocidade do som no meio
imagesc(medium.sound_speed);
colormap(gray);
b=colorbar;
title('Velocidade');
xlabel(b, '[m/s]');
interfaces_mask = edge(medium.density.*medium.sound_speed);
subplot(2,2,[2 4]); % Interfaces US
imshow(interfaces_mask);
colormap(gray);
title('Interfaces sigificativas US');
figure;
subplot(1,2,1); % Targets US
imshow(focus_points_mask);
colormap(gray);
title('Targets US');
subplot(1,2,2); % Fontes US
imshow(sensor.mask);
colormap(gray);
title('Fontes US');
pause(0.5);
%% Definir ping dos targets de ondas Ultrassonoras
% target ping signal
ping_pressure = 20; % [Pa]
signal_freq = 0.25e6; % [Hz]
ping_burst_cycles = 1;
source.p = ping_pressure * toneBurst(1/kgrid.dt, signal_freq, ping_burst_cycles);
input_args = {'DataCast', 'single', ...
'PlotSim', false};
% calcular delays para cada ponto
for ipoint=1:size(target_points,1)
% definir target para fazer ping
source.p_mask = zeros(Nx, Ny);
source.p_mask(target_points(ipoint,1),target_points(ipoint,2))=1;
% simular
sensor_data = kspaceFirstOrder2D(kgrid, medium, source, sensor, input_args{:});
[~, max_pos] = max(sensor_data,[],2);
sensor_delays(ipoint,:) = max(max_pos)-max_pos;
end
%% Simulate wave focusing on multiple targets sequentialy
% swap sensor to source and monitor pressure on all grid
source.p_mask = sensor.mask;
sensor.mask = ones(Nx,Ny);
sensor.record = {'p', 'p_max'};
% definir propriedades do sinal a emitir
focus_pressure = 10; % [Pa]
focus_burst_cycles = 2;
total_sensor_max_pressure = zeros(Nx*Ny,1);
for ipoint=1:size(target_points,1)
% cria sinal a emitir para focar(com delays)
source.p = focus_pressure*toneBurst(1/kgrid.dt, signal_freq, focus_burst_cycles, ...
'SignalOffset', sensor_delays(ipoint,:));
% simular
input_args = {'PlotLayout', false, ...
'PlotPML', false, ...
'DisplayMask', source.p_mask | focus_points_mask | model==255,...
'RecordMovie', true, 'MovieName', strcat('FocusTarget',num2str(ipoint)),...
'DataCast', 'single'};
sensor_data = kspaceFirstOrder2D(kgrid, medium, source, sensor, input_args{:});
if size(target_points,1)>1
figure;
p_max = reshape(sensor_data.p_max, Nx, Ny);
p_max(model<21 | model>221) = 0; % ignora pontos fora do cranio
imagesc(kgrid.y_vec * 1e3, kgrid.x_vec * 1e3, p_max);
h = colorbar; xlabel(h, '[Pa]');
title(strcat('Max Acoustic Pressure - Point (', num2str(target_points(ipoint,1)), ', ', num2str(target_points(ipoint,2)), ')'));
end
% add new sensor data to overal pressure data
total_sensor_max_pressure = max(total_sensor_max_pressure,sensor_data.p_max); % max
end
figure;
p_max = reshape(total_sensor_max_pressure, Nx, Ny);
p_max(model<21 | model>221) = 0; % ignora pontos fora do cranio
imagesc(kgrid.y_vec * 1e3, kgrid.x_vec * 1e3, p_max);
h = colorbar; xlabel(h, '[Pa]');
title('Max Acoustic Pressure');