Class based Sequencing

examples/matRad_example11_helium.m

@@ -13,49 +13,48 @@
 %
 % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
-%% In this example we will show 
+%% In this example we will show
 % (i) how to load patient data into matRad
-% (ii) how to setup a helium dose calculation 
-% (iii) how to inversely optimize the pencil beam intensities directly from command window in MATLAB. 
+% (ii) how to setup a helium dose calculation
+% (iii) how to inversely optimize the pencil beam intensities directly from command window in MATLAB.
 
 %% set matRad runtime configuration
-matRad_rc
+matRad_rc;
 
 %% Patient Data Import
 load('BOXPHANTOM.mat');
 
 %% Treatment Plan
-% The next step is to define your treatment plan labeled as 'pln'. This 
-% structure requires input from the treatment planner and defines the most 
+% The next step is to define your treatment plan labeled as 'pln'. This
+% structure requires input from the treatment planner and defines the most
 % important cornerstones of your treatment plan.
 
 %%
 % First of all, we need to define what kind of radiation modality we would
 % like to use. Possible values are photons, protons or carbon. In this
 % example we would like to use protons for treatment planning. Next, we
-% need to define a treatment machine to correctly load the corresponding 
+% need to define a treatment machine to correctly load the corresponding
 % base data. matRad features generic base data in the file
 % 'proton_Generic.mat'; consequently the machine has to be set accordingly
-pln.radiationMode = 'helium';        
+pln.radiationMode = 'helium';
 pln.machine       = 'Generic';
 pln.multScen      = 'nomScen';
 
 % Define the flavor of biological optimization for treatment planning along
 % with the quantity that should be used for optimization. As we use helium,
-% we follow a data-driven RBE parametrization to obtbain the variable 
-% relative biological effectiveness. 
+% we follow a data-driven RBE parametrization to obtbain the variable
+% relative biological effectiveness.
 pln.bioModel      = 'HEL';
 
-
 %%
 % Now we have to set the remaining plan parameters.
 pln.numOfFractions        = 30;
 pln.propStf.gantryAngles  = [0];
 pln.propStf.couchAngles   = [0];
 pln.propStf.bixelWidth    = 5;
-pln.propStf.isoCenter     = matRad_getIsoCenter(cst,ct,0);
-pln.propOpt.runDAO        = 0;
-pln.propOpt.runSequencing = 0;
+pln.propStf.numOfBeams    = numel(pln.propStf.gantryAngles);
+pln.propStf.isoCenter     = ones(pln.propStf.numOfBeams, 1) * matRad_getIsoCenter(cst, ct, 0);
+pln.propStf.isoCenter     = matRad_getIsoCenter(cst, ct, 0);
 
 % dose calculation settings
 pln.propDoseCalc.doseGrid.resolution.x = 5; % [mm]
@@ -73,27 +72,32 @@
 pln.propOpt.quantityOpt = 'effect';
 
 %% Generate Beam Geometry STF
-stf = matRad_generateStf(ct,cst,pln);
+stf = matRad_generateStf(ct, cst, pln);
 
 %% Dose Calculation
-% Lets generate dosimetric information by pre-computing dose influence 
-% matrices for unit beamlet intensities. Having dose influences available 
-% allows for subsequent inverse optimization. 
-dij = matRad_calcParticleDose(ct,stf,pln,cst);
+% Lets generate dosimetric information by pre-computing dose influence
+% matrices for unit beamlet intensities. Having dose influences available
+% allows for subsequent inverse optimization.
+dij = matRad_calcParticleDose(ct, stf, pln, cst);
 
 %% Inverse Optimization for IMPT
-% The goal of the fluence optimization is to find a set of bixel/spot 
-% weights which yield the best possible dose distribution according to the 
+% The goal of the fluence optimization is to find a set of bixel/spot
+% weights which yield the best possible dose distribution according to the
 % clinical objectives and constraints underlying the radiation treatment
-resultGUI = matRad_fluenceOptimization(dij,cst,pln);
+resultGUI = matRad_fluenceOptimization(dij, cst, pln);
 
 %% Plot the Resulting Dose Slice
 % Let's plot the transversal iso-center dose slice
-slice = matRad_world2cubeIndex(pln.propStf.isoCenter(1,:),ct);
+slice = matRad_world2cubeIndex(pln.propStf.isoCenter(1, :), ct);
 slice = slice(3);
-figure
-subplot(121),imagesc(resultGUI.physicalDose(:,:,slice)),colorbar,colormap(jet),title('physical dose')
-subplot(122),imagesc(resultGUI.RBExDose(:,:,slice)),colorbar,colormap(jet),title('RBExDose')
-
-
-
+figure;
+subplot(121);
+imagesc(resultGUI.physicalDose(:, :, slice));
+colorbar;
+colormap(jet);
+title('physical dose');
+subplot(122);
+imagesc(resultGUI.RBExDose(:, :, slice));
+colorbar;
+colormap(jet);
+title('RBExDose');

examples/matRad_example13_fitAnalyticalParticleBaseData.m

@@ -151,8 +151,6 @@
 pln.propOpt.optimizer       = 'IPOPT';
 pln.propOpt.bioOptimization = 'none'; % none: physical optimization;             const_RBExDose; constant RBE of 1.1;
                                       % LEMIV_effect: effect-based optimization; LEMIV_RBExDose: optimization of RBE-weighted dose
-pln.propOpt.runDAO          = false;  % 1/true: run DAO, 0/false: don't / will be ignored for particles
-pln.propOpt.runSequencing   = false;  % 1/true: run sequencing, 0/false: don't / will be ignored for particles and also triggered by runDAO below
 
 % retrieve scenarios for dose calculation and optimziation
 pln.multScen = matRad_multScen(ct,'nomScen');

examples/matRad_example14_spotRemoval.m

@@ -15,7 +15,7 @@
 
 clear;
 %% set matRad runtime configuration
-matRad_rc; %If this throws an error, run it from the parent directory first to set the paths
+matRad_rc; % If this throws an error, run it from the parent directory first to set the paths
 
 %% Patient Data Import
 % Let's begin with a clear Matlab environment and import the prostate
@@ -24,18 +24,18 @@
 load('PROSTATE.mat');
 
 %% Treatment Plan
-% The next step is to define your treatment plan labeled as 'pln'. This 
-% structure requires input from the treatment planner and defines the most 
+% The next step is to define your treatment plan labeled as 'pln'. This
+% structure requires input from the treatment planner and defines the most
 % important cornerstones of your treatment plan.
 
 %%
 % First of all, we need to define what kind of radiation modality we would
 % like to use. Possible values are photons, protons or carbon. In this
 % example we would like to use protons for treatment planning. Next, we
-% need to define a treatment machine to correctly load the corresponding 
+% need to define a treatment machine to correctly load the corresponding
 % base data. matRad features generic base data in the file
 % 'proton_Generic.mat'; consequently the machine has to be set accordingly
-pln.radiationMode = 'protons';        
+pln.radiationMode = 'protons';
 pln.machine       = 'Generic';
 pln.bioModel      = 'constRBE';
 pln.multScen      = 'nomScen';
@@ -45,45 +45,43 @@
 % alongside the physical dose. Therefore you need to activate the
 % corresponding option during dose calculcation
 pln.propDoseCalc.calcLET = 0;
-                                       
+
 %%
 % Now we have to set the remaining plan parameters.
 pln.numOfFractions        = 30;
 pln.propStf.gantryAngles  = [90 270];
 pln.propStf.couchAngles   = [0 0];
 pln.propStf.bixelWidth    = 3;
-pln.propStf.isoCenter     = matRad_getIsoCenter(cst,ct,0);
-pln.propOpt.runDAO        = 0;
-pln.propOpt.runSequencing = 0;
+pln.propStf.isoCenter     = matRad_getIsoCenter(cst, ct, 0);
 
 % dose calculation settings
 pln.propDoseCalc.doseGrid.resolution.x = 5; % [mm]
 pln.propDoseCalc.doseGrid.resolution.y = 5; % [mm]
 pln.propDoseCalc.doseGrid.resolution.z = 5; % [mm]
 
 %% Generate Beam Geometry STF
-stf = matRad_generateStf(ct,cst,pln);
+stf = matRad_generateStf(ct, cst, pln);
 
 %% Dose Calculation
-% Lets generate dosimetric information by pre-computing dose influence 
-% matrices for unit beamlet intensities. Having dose influences available 
-% allows for subsequent inverse optimization. 
-dij = matRad_calcParticleDose(ct,stf,pln,cst);
+% Lets generate dosimetric information by pre-computing dose influence
+% matrices for unit beamlet intensities. Having dose influences available
+% allows for subsequent inverse optimization.
+dij = matRad_calcParticleDose(ct, stf, pln, cst);
 
 %% Inverse Optimization for IMPT
-% The goal of the fluence optimization is to find a set of bixel/spot 
-% weights which yield the best possible dose distribution according to the 
+% The goal of the fluence optimization is to find a set of bixel/spot
+% weights which yield the best possible dose distribution according to the
 % clinical objectives and constraints underlying the radiation treatment
 pln.propOpt.quantityOpt = quantityOpt;
-resultGUI = matRad_fluenceOptimization(dij,cst,pln);
+resultGUI = matRad_fluenceOptimization(dij, cst, pln);
 
 %% Spot removal
 % instantiate spot removal class
-spotRemover = matRad_SpotRemovalDij(dij,resultGUI.w);
+spotRemover = matRad_SpotRemovalDij(dij, resultGUI.w);
 
 spotRemover.removalMode = 'relative';
 spotRemover.propSpotRemoval.relativeThreshold = 0.05;
-resultGUI2 = spotRemover.reoptimize(cst,pln);
+resultGUI2 = spotRemover.reoptimize(cst, pln);
 
 % numOfRemovedSpots = sr_cfg.numOfRemovedSpots;
 
@@ -93,6 +91,4 @@
 % weightLogical = sr_cfg.getLogical;
 
 %% Plot difference of the doses
-matRad_compareDose(resultGUI.RBExDose,resultGUI2.RBExDose,ct,cst);
-
-
+matRad_compareDose(resultGUI.RBExDose, resultGUI2.RBExDose, ct, cst);

examples/matRad_example16_photonMC_MLC.m

@@ -13,43 +13,40 @@
 %
 % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
-%% In this example we will show 
+%% In this example we will show
 % (i) how to load patient data into matRad
-% (ii) how to setup a photon dose calculation based on the VMC++ Monte Carlo algorithm 
-% (iii) how to inversely optimize the beamlet intensities directly from command window in MATLAB. 
+% (ii) how to setup a photon dose calculation based on the VMC++ Monte Carlo algorithm
+% (iii) how to inversely optimize the beamlet intensities directly from command window in MATLAB.
 % (iv) how to visualize the result
 
 %% set matRad runtime configuration
-matRad_rc %If this throws an error, run it from the parent directory first to set the paths
+matRad_rc; % If this throws an error, run it from the parent directory first to set the paths
 
 %% Patient Data Import
 % Let's begin with a clear Matlab environment and import the boxphantom
-% into your workspace. 
+% into your workspace.
 load('BOXPHANTOM.mat');
 
 %% Treatment Plan
-% The next step is to define your treatment plan labeled as 'pln'. This 
+% The next step is to define your treatment plan labeled as 'pln'. This
 % structure requires input from the treatment planner and defines the most
 % important cornerstones of your treatment plan.
 
-pln.radiationMode           = 'photons';  
+pln.radiationMode           = 'photons';
 pln.machine                 = 'Generic';
 pln.numOfFractions          = 30;
 pln.propStf.gantryAngles    = [0:72:359];
 pln.propStf.couchAngles     = [0 0 0 0 0];
-%pln.propStf.gantryAngles    = [0];
-%pln.propStf.couchAngles     = [0];
+% pln.propStf.gantryAngles    = [0];
+% pln.propStf.couchAngles     = [0];
 pln.propStf.bixelWidth      = 10;
-pln.propStf.isoCenter       = matRad_getIsoCenter(cst,ct,0);
-% Enable sequencing and direct aperture optimization (DAO).
-pln.propOpt.runSequencing   = 1;
-pln.propOpt.runDAO          = 1;
+pln.propStf.isoCenter       = matRad_getIsoCenter(cst, ct, 0);
 
-quantityOpt    = 'physicalDose';                                     
-modelName      = 'none';  
+quantityOpt    = 'physicalDose';
+modelName      = 'none';
 
 % retrieve bio model parameters
-pln.bioModel = matRad_bioModel(pln.radiationMode,quantityOpt, modelName);
+pln.bioModel = matRad_bioModel(pln.radiationMode, quantityOpt, modelName);
 
 % retrieve scenarios for dose calculation and optimziation
 pln.multScen = matRad_NominalScenario(ct);
@@ -59,40 +56,43 @@
 pln.propDoseCalc.doseGrid.resolution.z = 3; % [mm]
 
 %% Generate Beam Geometry STF
-stf = matRad_generateStf(ct,cst,pln);
+stf = matRad_generateStf(ct, cst, pln);
 
 %% Dose Calculation
 
-dij = matRad_calcDoseInfluence(ct,cst,stf,pln);
+dij = matRad_calcDoseInfluence(ct, cst, stf, pln);
 
 %% Inverse Optimization for IMRT
 pln.propOpt.quantityOpt = quantityOpt;
-resultGUI = matRad_fluenceOptimization(dij,cst,pln);
+resultGUI = matRad_fluenceOptimization(dij, cst, pln);
 %% Sequencing
-% This is a multileaf collimator leaf sequencing algorithm that is used in 
-% order to modulate the intensity of the beams with multiple static 
-% segments, so that translates each intensity map into a set of deliverable 
+% This is a multileaf collimator leaf sequencing algorithm that is used in
+% order to modulate the intensity of the beams with multiple static
+% segments, so that translates each intensity map into a set of deliverable
 % aperture shapes.
-resultGUI = matRad_siochiLeafSequencing(resultGUI,stf,dij,5,1);
-[pln,stf] = matRad_aperture2collimation(pln,stf,resultGUI.sequencing,resultGUI.apertureInfo);
+resultGUI = matRad_sequencing(resultGUI, stf, pln, dij);
+[pln, stf] = matRad_aperture2collimation(pln, stf, resultGUI.sequencing, resultGUI.sequencing.apertureInfo);
+
 %% Aperture visualization
 % Use a matrad function to visualize the resulting aperture shapes
-matRad_visApertureInfo(resultGUI.apertureInfo)
+matRad_visApertureInfo(resultGUI.sequencing.apertureInfo);
 %% Plot the Resulting Dose Slice
 % Just let's plot the transversal iso-center dose slice
-slice = matRad_world2cubeIndex(pln.propStf.isoCenter(1,:),ct);
+slice = matRad_world2cubeIndex(pln.propStf.isoCenter(1, :), ct);
 slice = slice(3);
-figure,
-imagesc(resultGUI.physicalDose(:,:,slice)),colorbar, colormap(jet)
+figure;
+imagesc(resultGUI.physicalDose(:, :, slice));
+colorbar;
+colormap(jet);
 
 %% Dose Calculation
-%resultGUI_MC = matRad_calcDoseInfluence(ct,cst,stf,pln);
+% resultGUI_MC = matRad_calcDoseInfluence(ct,cst,stf,pln);
 pln.propDoseCalc.engine = 'TOPAS';
 pln.propDoseCalc.beamProfile = 'phasespace';
 pln.propDoseCalc.externalCalculation = 'write';
-resultGUI_MC = matRad_calcDoseForward(ct,cst,stf,pln,resultGUI.w);
+resultGUI_MC = matRad_calcDoseForward(ct, cst, stf, pln, resultGUI.w);
 
 %% readout
-waitforbuttonpress; %We will wait since we do need to do the external calculation first
+waitforbuttonpress; % We will wait since we do need to do the external calculation first
 pln.propDoseCalc.externalCalculation = resultGUI_MC.meta.TOPASworkingDir;
-resultGUI_MC = matRad_calcDoseForward(ct,cst,stf,pln,resultGUI.w);
+resultGUI_MC = matRad_calcDoseForward(ct, cst, stf, pln, resultGUI.w);

examples/matRad_example17_biologicalModels.m

@@ -12,21 +12,19 @@
 pln.propStf.gantryAngles  = 0;
 pln.propStf.couchAngles   = 0;
 pln.propStf.bixelWidth    = 5;
-pln.propStf.isoCenter     = matRad_getIsoCenter(cst,ct,0);
-pln.propOpt.runDAO        = 0;
-pln.propSeq.runSequencing = 0;
+pln.propStf.isoCenter     = matRad_getIsoCenter(cst, ct, 0);
 
 % dose calculation settings
 pln.propDoseCalc.doseGrid.resolution.x = 8;
 pln.propDoseCalc.doseGrid.resolution.y = 8;
 pln.propDoseCalc.doseGrid.resolution.z = 8;
 pln.propDoseCalc.engine = 'HongPB';
 
-%Optimization Settings
+% Optimization Settings
 pln.propOpt.quantityOpt = 'RBExDose';
 
 %% stf
-stf = matRad_generateStf(ct,cst,pln);
+stf = matRad_generateStf(ct, cst, pln);
 
 %% Dose calc
 
@@ -38,19 +36,19 @@
 
 % We will compare the MCN model to constRBE.
 % We will plan with the the MCN model.
-pln.bioModel = matRad_MCNamara(); 
-%alternative: pln.bioModel = matRad_bioModel(pln.radiationMode,'MCN');
-%altnerative: pln.bioModel = 'MCN';
-dij = matRad_calcDoseInfluence(ct,cst,stf,pln);
+pln.bioModel = matRad_MCNamara();
+% alternative: pln.bioModel = matRad_bioModel(pln.radiationMode,'MCN');
+% altnerative: pln.bioModel = 'MCN';
+dij = matRad_calcDoseInfluence(ct, cst, stf, pln);
 
 %% Fluence optimization
-resultGUI = matRad_fluenceOptimization(dij,cst,pln);
+resultGUI = matRad_fluenceOptimization(dij, cst, pln);
 
 %% Now let's recalculate with the constRBE model
 pln.bioModel = matRad_ConstantRBE();
-pln.bioModel.RBE = 1.1; %1.1 is standard, this is for illustration
+pln.bioModel.RBE = 1.1; % 1.1 is standard, this is for illustration
 
-resultGUI_recalc = matRad_calcDoseForward(ct,cst,stf,pln,resultGUI.w);
+resultGUI_recalc = matRad_calcDoseForward(ct, cst, stf, pln, resultGUI.w);
 
 %% Compare Dose distributions
-matRad_compareDose(resultGUI.RBExDose,resultGUI_recalc.RBExDose,ct,cst);
+matRad_compareDose(resultGUI.RBExDose, resultGUI_recalc.RBExDose, ct, cst);