%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% MCXLAB - Monte Carlo eXtreme for MATLAB/Octave by Qianqina Fang
%
% In this example, we compare SVMC algorithm against conventional VMC in a
% full-head atlas template domain(USC 19.5 year group[Sanchez2012]).
%
% This demo is similar to the MCX simulation used for Fig. 3(c) in [Yan2020]
% and Fig. 9(a) in [TranYan2019].
%
% [Sanchez2012] C.E.Sanchez J.E.Richards and C.R.Almli, "Age-Specific MRI Templates
% for Pediatric Neuroimaging," Developmental Neuropsychology 37, 379–399 (2012).
%
% [Yan2020] Shijie Yan and Qianqian Fang, "Hybrid mesh and voxel based Monte
% Carlo algorithm for accurate and efficient photon transport modeling in
% complex bio-tissues," Biomed. Opt. Express 11, 6262-6270 (2020)
%
% [TranYan2019] Anh Phong Tran, Shijie Yan, Qianqian Fang, "Improving model-based
% functional near-infrared spectroscopy analysis using mesh-based anatomical and
% light-transport models," Neurophoton. 7(1) 015008 (22 February 2020)
%
% This file is part of Monte Carlo eXtreme (MCX) URL:https://mcx.space
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

clear cfg cfg_svmc;

%% common MC setup
cfg.nphoton = 1e8;
cfg.seed = randi([1 2^31 - 1], 1, 1); % random seed

% inward-pointing pencil beam light source
cfg.srctype = 'pencil';
cfg.srcpos = [133.5370, 90.1988, 200.0700];
cfg.srcdir = [-0.5086, -0.1822, -0.8415];
cfg.issrcfrom0 = 1;

% optical properties
cfg.prop = [0.0   0.0   1.0  1.0    % background medium
            0.019 7.8   0.89 1.37   % scalp
            0.019 7.8   0.89 1.37   % skull
            0.0004 0.009 0.89 1.37  % CSF
            0.02  9.0   0.89 1.37   % gray matter
            0.08  40.9  0.84 1.37   % white matter
            0.0   0.0   1.0  1.0];  % air cavity

% time-domain simulation parameters
cfg.tstart = 0;
cfg.tend = 5e-9;
cfg.tstep = 5e-10;

% enable boundary reflection/refraction
cfg.isreflect = 1;

% spatial resolution
cfg.unitinmm = 1;

% output fluence
cfg.outputtype = 'fluence';

% GPU settings
cfg.gpuid = 1;
cfg.autopilot = 1;

%% prepare vmc input volume
load('fullhead_atlas.mat');
cfg_mcx = cfg;
cfg_mcx.vol = USC_atlas;

%% prepare svmc input volume
addpath('../../utils');
tic;
[svmcvol] = mcxsvmc(USC_atlas, 'smoothing', 1);
fprintf('SVMC preprocessing complete, ');
toc;

cfg_svmc = cfg;
cfg_svmc.srcpos = cfg_svmc.srcpos + 0.5;
cfg_svmc.vol = uint8(svmcvol);

%% run simulations
addpath ../;
output_vmc = mcxlabcl(cfg_mcx);   % conventional vmc
output_svmc = mcxlabcl(cfg_svmc); % svmc

%% convert time-resolved fluence to CW fluence
phi_vmc = sum(output_vmc.data, 4);
phi_svmc = sum(output_svmc.data, 4);

%% replace zero with nan
phi_vmc(phi_vmc == 0) = nan;
phi_svmc(phi_svmc == 0) = nan;

%% compare CW fluence distributions using contour lines
y_plane = 90.5; % coronal plane selected for fluence plot
[xx, zz] = meshgrid(0.5:(size(USC_atlas, 1) - 0.5), 0.5:(size(USC_atlas, 3) - 0.5));

% interpolate SVMC results to slice y=91 (as marching cube mesh has an spatial offset of 0.5)
y = 0.5:((size(USC_atlas, 2) - 0.5));
phi_svmc_y_pivot = permute(phi_svmc, [2, 1, 3]);
phi_svmc_interp = squeeze(interp1(y, phi_svmc_y_pivot, y_plane + 0.5));

% plot CW fluence distribution using contour lines
figure;
clines = -20:0.5:0;
contourf(xx - 0.5, zz - 0.5, log10(abs(phi_svmc_interp')), clines, 'linestyle', '-', ...
         'linecolor', 'k', 'linewidth', 2, 'DisplayName', 'SVMC');
hold on;
contour(xx, zz, log10(abs(squeeze(phi_vmc(:, ceil(y_plane), :))')), clines, 'linestyle', '--', ...
        'linecolor', 'w', 'linewidth', 2, 'DisplayName', 'VMC');
colorbar('EastOutside');

% plot tissue boundaries
contour(squeeze(USC_atlas(:, ceil(y_plane), :))', 'linestyle', '--', ...
        'linecolor', [0.5 0.5 0.5], 'linewidth', 1.5, 'HandleVisibility', 'off');

axis equal;
lg = legend;
set(lg, 'color', '[0.5 0.5 0.5]');
set(lg, 'box', 'on');

set(gca, 'ylim', [160 220]);
ylabel('z(mm)');
set(gca, 'xlim', [45 165]);
xlabel('x(mm)');
set(gca, 'clim', [-12 0]);
set(gca, 'fontsize', 18);
