diff --git a/Examples/Plugins/org.mitk.example.gui.pcaexample/documentation/UserManual/Manual.dox b/Examples/Plugins/org.mitk.example.gui.pcaexample/documentation/UserManual/Manual.dox index e754d42a76..3cd8f72689 100644 --- a/Examples/Plugins/org.mitk.example.gui.pcaexample/documentation/UserManual/Manual.dox +++ b/Examples/Plugins/org.mitk.example.gui.pcaexample/documentation/UserManual/Manual.dox @@ -1,17 +1,17 @@ /** -\page org_mitk_example_gui_pcaexample The Pcaexample +\page org_mitk_example_gui_pcaexample PCA example \imageMacro{icon.png,"Icon of Pcaexample",2.00} \tableofcontents \section org_mitk_example_gui_pcaexampleOverview Overview Describe the features of your awesome plugin here */ diff --git a/Plugins/org.mitk.gui.qt.aicpregistration/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.aicpregistration/documentation/UserManual/Manual.dox index 4da7b4f2b5..b1e0204f71 100644 --- a/Plugins/org.mitk.gui.qt.aicpregistration/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.aicpregistration/documentation/UserManual/Manual.dox @@ -1,80 +1,80 @@ /** -\page org_mitk_gui_qt_aicpregistration The Anisotropic Iterative Closest Point Registration Plugin +\page org_mitk_gui_qt_aicpregistration Anisotropic Iterative Closest Point Registration Plugin \imageMacro{"QmitkAICPRegistration_Icon.xpm","Icon of the A-ICP Registration Plugin",2} \tableofcontents \section org_mitk_gui_qt_aicpregistrationOverview Overview The Surfaceregistration plugin allows the user to compute a transformation between two surfaces and align them in 3D space. It performs the registration with the anisotropic iterative closest point algorithm (A-ICP) presented in L. Maier-Hein et al. "Convergent Iterative Closest-Point Algorithm to Accomodate Anisotropic and Inhomogenous Localization Error.", IEEE T Pattern Anal 34 (8), 1520-1532, 2012. With the plugin it's also possible to compute the target registration error (TRE) between the two aligned surfaces with a given set of target points. In order to register the surfaces they need to be initially aligned to be able to run the fine registration with the A-ICP algorithm. \imageMacro{"QmitkAICPRegistration_Plugin.png","Overview of the A-ICP Registration Plugin.",28} \section org_mitk_gui_qt_aicpregistrationUsage Usage In order to run a registration at least two surfaces need to be loaded into the data manager. Once the surfaces are loaded, a moving and a fixed surface can be selected in the associated combo boxes of the plugin. When the Register Surfaces button is pressed the registration is started and the moving surface is transformed onto the fixed surface. \imageMacro{"QmitkAICPRegistration_Input.png","Select the surfaces to register.",28} \section org_mitk_gui_qt_aicpregistrationTargetRegistrationErrorCalculation Target Registration Error Calculation To compute the target registration error, enable the calculation via the checkbox in the target registration view. Once the TRE computation is enabled the combo boxes are activated to select the according target point sets. \imageMacro{"QmitkAICPRegistration_TRECalculation.png","Usage of the TRE calculation view.",28} \section org_mitk_gui_qt_aicpregistrationRegistrationSettings Registration Settings The following additional settings are available in the plugin to configure the algorithm: \imageMacro{"QmitkAICPRegistration_Settings.png","Additional registration settings.",28} \subsection org_mitk_gui_qt_aicpregistrationTrimmedRegistration Trimmed Registration This option enables a trimmed version of the algorithm to register partial overlapping surfaces. Once the option is enabled the user can specify the overlapping part of the surface. Valid values for the overlapping part lie between 0 an 1. The trimmed version of the algorithm uses only a fixed percentage of all correspondences found during one iteration. Only the best correspondences will be used during the registration process. \subsection org_mitk_gui_qt_aicpregistrationThreshold Threshold The user can specify the threshold which is used as a termination constraint for the algorithm. When the the change of the fiducial registration error (FRE) between to registrations falls under the specified threshold the algorithm terminates. Larger values can speedup the registration process at the cost of a more accurate result. \subsection org_mitk_gui_qt_aicpregistrationMaximumIterations Maximum Iterations The maximum amount of iterations used by the algorithm can be specified by the user. Once the algorithm reaches the maximum amount of iterations it will stop the registration process. \subsection org_mitk_gui_qt_aicpregistrationSearchRadius Search Radius The user can specify the search radius in mm used during the correspondence search in a kd tree. The default value is 30 mm. A small radius can speedup the algorithm but can in addition also lead in bad correspondences and therefore in an incorrect alignment of the surfaces. */ diff --git a/Plugins/org.mitk.gui.qt.cest/documentation/UserManual/org_mitk_gui_qt_cest.dox b/Plugins/org.mitk.gui.qt.cest/documentation/UserManual/org_mitk_gui_qt_cest.dox index b559b0388e..17c8d546fd 100644 --- a/Plugins/org.mitk.gui.qt.cest/documentation/UserManual/org_mitk_gui_qt_cest.dox +++ b/Plugins/org.mitk.gui.qt.cest/documentation/UserManual/org_mitk_gui_qt_cest.dox @@ -1,66 +1,66 @@ /** -\page org_mitk_gui_qt_cest The CEST View +\page org_mitk_gui_qt_cest CEST View \imageMacro{icon.svg,"Icon of the cest view",2.00} \tableofcontents \section org_mitk_gui_qt_cestOverview Overview This view gives the option to explore and analyze CEST data. You can select a CEST data set together with either a segmentation or a point set in the datamanager. If a segmentation was selected (make sure the segmentation is the same in each time step, you can use the button at the top of the view to copy the first time step to all subsequent ones) a statistic for the selected region is shown. If a point set was selected each points grey value is plotted. \section org_mitk_gui_qt_cestDataLoading Data Loading CEST dicom data can be loaded either via file open or drag and drop. When a dicom file is loaded via MITK and it contains CEST meta information the CEST Dicom Reader is offered as an option. Selecting it will parse the CEST data as follows: An initial parsing determines whether the provided string belongs to CEST data at all. If the "tSequenceFileName" is of the format "{WHATEVER}CEST_Rev####" it is assumed that the data is indeed CEST data and was taken with revision #### (not limited to four digits). Which custom parameters to save and to which property name can be controlled by a json file. This file can be either provided as a resource for the MitkCEST module during compilation or placed next to the MitkCEST library in your binary folder. The expected format for the file "REVISIONNUMBER.json":
{
"REVISIONNUMBER" : "revision_json",
"sWiPMemBlock.alFree[1]" : "AdvancedMode",
"sWiPMemBlock.alFree[2]" : "RetreatMode"
}
where : If the sampling type is list it will try to access LIST.txt at the same location as the dicom files read the offsets. \section org_mitk_gui_qt_cestCreatingSegmentation Creating a segmentation You can use the segmentation view to create a segmentation for the CEST data. Drawing a segmentation will by default only add it to the first timestep. For more information check the help of the segmentation view. You can copy the segmentation on the first timestep to all following ones by using the copy timestep button in this view. \section org_mitk_gui_qt_cestCreatingPointSet Creating a point set You can use the point set interaction view to create a point set for the CEST data. For more information check the help of the point set interaction view. \section org_mitk_gui_qt_cestNormalizing Normalizing the CEST data Select the CEST image in the datamanager and hit the normalize button to create a new, normalized image. \section org_mitk_gui_qt_cestInspectProperties Investigate CEST meta data You can inspect the CEST meta data with the properties view. You need to enable the developer mode in the "Window->Preferences" Properties menu. When selecting a data node in the data manager with the properties view active you can change the Property List from "Data node: common" to "Base data". The cest meta data is grouped under CEST. */ diff --git a/Plugins/org.mitk.gui.qt.classificationsegmentation/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.classificationsegmentation/documentation/UserManual/Manual.dox index c1a5768584..7877e578d8 100644 --- a/Plugins/org.mitk.gui.qt.classificationsegmentation/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.classificationsegmentation/documentation/UserManual/Manual.dox @@ -1,25 +1,25 @@ /** -\page org_mitk_gui_qt_classificationsegmentation The Classificationsegmentation Plugin +\page org_mitk_gui_qt_classificationsegmentation Classificationsegmentation Plugin \imageMacro{brain.png,"Icon of Classificationsegmentation Plugin",2.00} \tableofcontents \section org_mitk_gui_qt_classificationsegmentationOverview Overview Segmentation of the classes: using a random forest based approach. Credits (from the Noun Project): */ diff --git a/Plugins/org.mitk.gui.qt.eventrecorder/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.eventrecorder/documentation/UserManual/Manual.dox index 1ac1f69e7e..4522d6cdf8 100644 --- a/Plugins/org.mitk.gui.qt.eventrecorder/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.eventrecorder/documentation/UserManual/Manual.dox @@ -1,17 +1,17 @@ /** -\page org_mitk_gui_qt_eventrecorder The Event Recorder Plugin +\page org_mitk_gui_qt_eventrecorder Event Recorder Plugin \imageMacro{icon.png,"Icon of Eventrecorder",2.00} \tableofcontents \section org_mitk_gui_qt_eventrecorderOverview Describe the features of your awesome plugin here */ diff --git a/Plugins/org.mitk.gui.qt.fit.demo/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.fit.demo/documentation/UserManual/Manual.dox index 17a7551190..7f71fb8166 100644 --- a/Plugins/org.mitk.gui.qt.fit.demo/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.fit.demo/documentation/UserManual/Manual.dox @@ -1,17 +1,17 @@ /** -\page org_mitk_gui_qt_fit_demo The Model Fit Demo View +\page org_mitk_gui_qt_fit_demo Model Fit Demo View \imageMacro{fit_demo_doc.svg,"Icon of the Fit Demo View",3.0} \tableofcontents \section FIT_DEMO_Introduction Introduction This plugin is a very simple demo plugin that allows 1) to generate a demo 3D+t image (with linear increasing values) and 2) to perform a linear fit on a selected node. It was/is use to demonstrate basic principle and to generate example fit sessions for demo and testing purposes (e.g. functionality of the fit inspector). \section FIT_DEMO_Contact Contact information This plug-in is being developed by the SIDT group (Software development for Integrated Diagnostics and Therapy) at the DKFZ (German Cancer Research Center). If you have any questions, need support, find a bug or have a feature request, feel free to contact us at www.mitk.org. */ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.fit.genericfitting/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.fit.genericfitting/documentation/UserManual/Manual.dox index 22989ba6dd..6a2c10524f 100644 --- a/Plugins/org.mitk.gui.qt.fit.genericfitting/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.fit.genericfitting/documentation/UserManual/Manual.dox @@ -1,16 +1,16 @@ /** -\page org_mitk_gui_qt_fit_genericfitting The Model Fit Generic Fitting View +\page org_mitk_gui_qt_fit_genericfitting Model Fit Generic Fitting View \imageMacro{fit_generic_doc.svg,"Icon of the Generic Fitting View",3.0} \tableofcontents \section FIT_GENERIC_Introduction Introduction This plug-in offers a generic fitting component for time resolved image data. \section FIT_GENERIC_Contact Contact information This plug-in is being developed by the SIDT group (Software development for Integrated Diagnostics and Therapy) at the DKFZ (German Cancer Research Center). If you have any questions, need support, find a bug or have a feature request, feel free to contact us at www.mitk.org. */ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.fit.inspector/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.fit.inspector/documentation/UserManual/Manual.dox index edd1f3e8a5..b09068177a 100644 --- a/Plugins/org.mitk.gui.qt.fit.inspector/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.fit.inspector/documentation/UserManual/Manual.dox @@ -1,78 +1,78 @@ /** -\page org_mitk_gui_qt_fit_inspector The Model Fit Inspector View +\page org_mitk_gui_qt_fit_inspector Model Fit Inspector View \imageMacro{fit_inspector_doc.svg,"Icon of the Model Fit Inspector View",3.0} \tableofcontents \section FIT_INSPECTOR_Introduction Introduction This view (Model Fit Inspector; MFI) offers the possibility to display the time course of the signal within an individual voxel (with or without fit). \section FIT_INSPECTOR_Contact Contact information This plug-in is being developed by the SIDT group (Software development for Integrated Diagnostics and Therapy) at the DKFZ (German Cancer Research Center). If you have any questions, need support, find a bug or have a feature request, feel free to contact us at www.mitk.org. \section FIT_INSPECTOR_Raw Viewing without a model fit \imageMacro{fit_inspect_raw.png, "Example screen shot showing the inspection of raw dynamic data without a fit.", 10} Open the view and select the dynamic image in the data manager. The graph plot will show the time course of image intensities (signal) in the selected voxel (cross hair) as red data points. The blue point indicates the frame currently displayed in the 4-window view. \section FIT_INSPECTOR_Fit Viewing without a model fit \imageMacro{fit_inspect_fit.png, "Example screen shot showing the inspection of dynamic data and an associated fit.", 10} Selecting a parameter map of the fit of interest in the Data Manager will display the raw data curve in red dots with corresponding fit as black line in the selected cross-hair position. If an AIF-based model was used, the utilized AIF (averaged over AIF mask) is also displayed (default in green). The color of the AIF display can be adjusted. For ROI based fits, the MFI will display both the current data curve in the selected voxel (in red) and the ROI-averaged fitted curve (in dark green, can be adjusted). Scrolling through the individual voxels will change the current data curve, but the ROI-based curve remains the same. If voxels outside the fitted area defined by the mask are selected, the raw data voxel values will be displayed, however no black fit line is visualized. Below the data plot, several options for data visualization can be selected: \subsection FIT_INSPECTOR_Fit_info Fit info tab \imageMacro{fit_inspect_info.png, "Details of the fit info tab.", 5} The Fit info tab displays meta-data for selected fits performed on the displayed data set. If no fit was performed and only raw data is visualized, the fields are empty. \subsection FIT_INSPECTOR_Fit_Parameter Fit parameter tab \imageMacro{fit_inspect_results.png, "Details of the fit parameter tab.", 5} The "fit parameter" tab shows fit related parameter estimate values, derived parameters, fit criterion values and (optional) debug parameter maps in the selected voxel (and all inspection positions; see also \ref FIT_INSPECTOR_Inspect "inspection positions") listed as a table. If no fit was performed and only raw data is visualized, the table is empty. The content of the table may by copied to clipboard or exported as csv file, by clicking the respective button below the table. \subsection FIT_INSPECTOR_Fit_Inspection Inspection positions tab \imageMacro{fit_inspect_positions.png, "Details of the inspection position tab.", 5} The tab allows to manage inspection positions (see here for more about \ref FIT_INSPECTOR_Inspect "inspection positions"). - (1) Shows the coordinates of the current selected position in the workbench. - (2) Press to make the current position a inspected position. It will be added at the bottom of the list (3). - (3) List of all inspection positions - (4) Toggle adding mode on/off. If on, you can add new positions by clicking into render windows with "SHIFT + left mouse button". - (5) Manually adding inspection positions by entering the coordinates. - (6) Remove the selected inspection positions. (Hot key: Del) - (7) Move the selected inspection position up in the list (3). - (8) Move the selected inspection position down in the list (3). - (9) Save inspection points to a file. - (10) Load inspection points from a file. \subsection FIT_INSPECTOR_Fit_Settings Settings tab \imageMacro{fit_inspect_settings.png, "Details of the settings tab.", 5} The View settings tab is used to adjust the plot display, namely, x and y axis scales and colors of displayed data plots (i.e. AIF). \subsection FIT_INSPECTOR_Fit_Export Plot data export tab \imageMacro{fit_inspect_export.png, "Details of the plot data export tab.", 5} Displays voxel data (input image) and corresponding time grid together with model fit values and additional curves (like AIF values) for each time point. The table will contain the position depended values of the current selected position as well as of all inspection positions (see also \ref FIT_INSPECTOR_Inspect "inspection positions"). The data in the table can also be copied to clipboard or exported to csv files, by clicking the respective button below the table. \section FIT_INSPECTOR_Inspect Inspection positions \imageMacro{fit_inspect_positions_example.png, "Example of the usage of inspection positions.", 5} The fit inspector allows to define positions in the world coordinate system that will be constantly displayed in addition to the current selected position. These inspected position will be shown in the following parts of the view: - The plot windows. See example image above; the plot shows the current position (raw data: red dots, fit: black line) and an additional inspection position (green). - The fit parameter tab (see example image above) - The plot data export tab. It will work with dynamic data with and without an model fit. See the \ref FIT_INSPECTOR_Fit_Inspection "inspection positions tab section" for more details on how to manage inspection positions. */ diff --git a/Plugins/org.mitk.gui.qt.geometrytools/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.geometrytools/documentation/UserManual/Manual.dox index 66bc02d803..41cc6fa9a3 100644 --- a/Plugins/org.mitk.gui.qt.geometrytools/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.geometrytools/documentation/UserManual/Manual.dox @@ -1,18 +1,18 @@ /** -\page org_mitk_gui_qt_geometrytools The Geometry Tools +\page org_mitk_gui_qt_geometrytools Geometry Tools \imageMacro{icon.png,"Icon of Geometry Tools",2.00} \tableofcontents \section org_mitk_gui_qt_geometrytoolsOverview Overview A plugin to modify geometry of mitkBaseData via interaction. Currently, the following operations can be performed: */ diff --git a/Plugins/org.mitk.gui.qt.igt.app.ultrasoundtrackingnavigation/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.igt.app.ultrasoundtrackingnavigation/documentation/UserManual/Manual.dox index dad72316db..aca0042001 100644 --- a/Plugins/org.mitk.gui.qt.igt.app.ultrasoundtrackingnavigation/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.igt.app.ultrasoundtrackingnavigation/documentation/UserManual/Manual.dox @@ -1,16 +1,16 @@ /** -\page org_mitk_gui_qt_igt_app_echotrack The Usnavigation Plugin +\page org_mitk_gui_qt_igt_app_echotrack Ultrasound Navigation Plugin Available sections: - \ref org_mitk_gui_qt_igt_app_echotrackOverview \section org_mitk_gui_qt_igt_app_echotrackOverview Overview Describe the features of your awesome plugin here */ diff --git a/Plugins/org.mitk.gui.qt.igtexamples/documentation/UserManual/QmitkIGTExamples.dox b/Plugins/org.mitk.gui.qt.igtexamples/documentation/UserManual/QmitkIGTExamples.dox index e52ecd3d69..0bc225644e 100644 --- a/Plugins/org.mitk.gui.qt.igtexamples/documentation/UserManual/QmitkIGTExamples.dox +++ b/Plugins/org.mitk.gui.qt.igtexamples/documentation/UserManual/QmitkIGTExamples.dox @@ -1,14 +1,14 @@ /** -\page org_mitk_gui_qt_igtexample The IGT Examples +\page org_mitk_gui_qt_igtexample IGT Examples This plugin includes views with examples and help applications for IGT. The different views are described on the pages below: */ diff --git a/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/QmitkIGTTracking.dox b/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/QmitkIGTTracking.dox index e94957da24..67326607a3 100644 --- a/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/QmitkIGTTracking.dox +++ b/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/QmitkIGTTracking.dox @@ -1,20 +1,20 @@ /** -\page org_mitk_gui_qt_igttracking The IGT Tracking Plugin +\page org_mitk_gui_qt_igttracking IGT Tracking Plugin This plugin offers basic tracking functionalities. This includes connecting to a tracking system, logging and recording of tracking data, managing tracking tools and playing recorded tracking data. The plugin includes different views, which are described on different pages in detail. As part of the tutorial, it is recommended to get familiar with each of the following views as they lead you through the main functionality of the IGT plugin. Please read the following pages to get familiar with them: \ref Return to the \ref TrackingPlugins "[IGT Tutorial Overview]" */ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.lasercontrol/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.lasercontrol/documentation/UserManual/Manual.dox index 69fb486784..b709022b24 100644 --- a/Plugins/org.mitk.gui.qt.lasercontrol/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.lasercontrol/documentation/UserManual/Manual.dox @@ -1,17 +1,17 @@ /** -\page org_mitk_gui_qt_lasercontrol The Lasercontrol +\page org_mitk_gui_qt_lasercontrol Laser Control \imageMacro{icon.png,"Icon of Lasercontrol",2.00} \tableofcontents \section org_mitk_gui_qt_lasercontrolOverview Overview Describe the features of your awesome plugin here */ diff --git a/Plugins/org.mitk.gui.qt.matchpoint.algorithm.batch/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.matchpoint.algorithm.batch/documentation/UserManual/Manual.dox index 450c282732..74115fdd35 100644 --- a/Plugins/org.mitk.gui.qt.matchpoint.algorithm.batch/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.matchpoint.algorithm.batch/documentation/UserManual/Manual.dox @@ -1,14 +1,14 @@ /** -\page org_mitk_gui_qt_matchpoint_algorithm_batch The MatchPoint Registration Batch Processor View +\page org_mitk_gui_qt_matchpoint_algorithm_batch MatchPoint Registration Batch Processor View \imageMacro{map_icon_batch_doc.svg,"Icon of the Segmentation Utilities View",2.00} \tableofcontents \section MAP_BATCH_Introduction Introduction This plugin is very similar to the MatchPoint algorithm controller view, but allows to register a set of selected input images in one batch run onto one selected target image. \section MAP_BATCH_Usage Usage Oops. Documentation is missing and to be done. */ diff --git a/Plugins/org.mitk.gui.qt.matchpoint.algorithm.browser/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.matchpoint.algorithm.browser/documentation/UserManual/Manual.dox index 3d911805f2..a1348a0788 100644 --- a/Plugins/org.mitk.gui.qt.matchpoint.algorithm.browser/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.matchpoint.algorithm.browser/documentation/UserManual/Manual.dox @@ -1,46 +1,46 @@ /** -\page org_mitk_gui_qt_matchpoint_algorithm_browser The MatchPoint Algorithm Browser View +\page org_mitk_gui_qt_matchpoint_algorithm_browser MatchPoint Algorithm Browser View \imageMacro{map_icon_browser_doc.svg, "Icon of the MatchPoint Algorithm Browser", 3} \tableofcontents \section MAP_BROWSER_Introduction Introduction This view offers the user a way to search for available registration algorithms and select them for further usage by other views (e.g. MatchPoint Algorithm Control \ref org_mitk_gui_qt_algorithm_control). \section MAP_BROWSER_Usage Usage \remark If you see no algorithms available by the browser, please check the search paths which can be configured at the MatchPoint preference page (Ctrl+P). The basic idea of the browser is that you can use this view as central place to search for suitable algorithms. If you select an algorithm you can see its profile in the lower part of the view. If a algorithm is selected all other views (e.g. \ref org_mitk_gui_qt_algorithm_control) which use registration algorithms will be notified and allow to choose the selected algorithm for usage. \imageMacro{map_browser.png, "View of the browser with the list of available algorithm and the profile area",14} \section MAP_BROWSER_Profile Profile info In the following a short information about the different properties classified in the profile.\n */ diff --git a/Plugins/org.mitk.gui.qt.matchpoint.algorithm.control/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.matchpoint.algorithm.control/documentation/UserManual/Manual.dox index 162da76ff6..4f97da8d9b 100644 --- a/Plugins/org.mitk.gui.qt.matchpoint.algorithm.control/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.matchpoint.algorithm.control/documentation/UserManual/Manual.dox @@ -1,39 +1,39 @@ /** -\page org_mitk_gui_qt_matchpoint_algorithm_control The MatchPoint Algorithm Control View +\page org_mitk_gui_qt_matchpoint_algorithm_control MatchPoint Algorithm Control View \imageMacro{map_icon_run_doc.svg,"Icon of the MatchPoint Algorithm Control",3.0} \tableofcontents \section MAP_RUN_Introduction Introduction This plugin offers the user a way to use a selected registration algorithm in order to determine a registration for two selected images. For the selection of an algorithm please see MatchPoint Algorithm Browser (\ref org_mitk_gui_qt_matchpoint_algorithm_browser). \section MAP_RUN_Contact Contact information This plug-in is being developed by the SIDT group (Software development for Integrated Diagnostics and Therapy) at the DKFZ (German Cancer Research Center). If you have any questions, need support, find a bug or have a feature request, feel free to contact us at www.mitk.org. \section MAP_RUN_Usage Usage \imageMacro{map_control_example.png, "Example screenshot showing the control plugin in use.", 10} To use the plugin a registration algorithm must be loaded and a moving as well as a target image must be selected.\n The moving image is registered onto the target image. Thus the result is a mapped input image in the geometry (field of view, orientation, spacing) defined by the target image.\n All images are selected in the data manager using multi select (press the CTRL-key while selecting the nodes in the data manager). The first selection is the moving image, the second is the target image.\n If an algorithm is loaded and input images are selected, the plugin will automatically switch to the "Execution" tab. \subsection MAP_RUN_Usage_selection Selection tab \imageMacro{map_control_step1_selection.png, "Details of the selection tab.", 5} In this tab registration algorithms that are selected in the MatchPoint Algorithm Browser can be chosen. In the tab you see the ID of the algorithm selected by the browser and its profile information.\n If you press "Load selected algorithm", the algorithm will be used by the control plugin. The name of the algorithm occurs in the text field "Loaded algorithm" (at the top of the plugin view).\n At this point, it has no effect if you change the selection in the browser. The control plugin will keep the loaded algorithm until you choose to load another one. \subsection MAP_RUN_Usage_exec Execution tab \imageMacro{map_control_step2_execution.png, "Details of the execution tab.", 5} In this tab you can specify a name for the registration job (this will determine the names of the result nodes in the data manager).\n You can also choose to "store registration" (which is normally the goal of the whole process, because this is the very result of the algorithm ;).\n Additionally you can choose "Generate + store mapped result". This is a convenience feature which often saves you the time to use the mapper plugin afterwards. It will do the same like using the mapper plugin with the moving and target image, setting padding value "0" and using "linear interpolation". If you need other settings, skip the convenience generation and use the MatchPoint mapper plugin directly.\n "Start" will trigger the registration process. Some algorithms can be stopped while processing takes place. In those cases, there is a "Stop" button enabled, as soon as the registration process starts. \subsection MAP_RUN_Usage_settings Settings tab \imageMacro{map_control_step3_settings.png, "Details of the settings tab.", 5} In this tab, you can change the parametrization of the loaded algorithm (before it starts), if it offers any possibility to do so. */ diff --git a/Plugins/org.mitk.gui.qt.matchpoint.evaluator/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.matchpoint.evaluator/documentation/UserManual/Manual.dox index 1a7ecf6d90..f801c728fd 100644 --- a/Plugins/org.mitk.gui.qt.matchpoint.evaluator/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.matchpoint.evaluator/documentation/UserManual/Manual.dox @@ -1,50 +1,50 @@ /** -\page org_mitk_gui_qt_matchpoint_evaluator The MatchPoint Registration Evaluation View +\page org_mitk_gui_qt_matchpoint_evaluator MatchPoint Registration Evaluation View \imageMacro{map_evaluator_doc.svg, "Icon of the MatchPoint Registration Evaluator", 3} \tableofcontents \section MAP_REGEVAL_Introduction Introduction This view offers the possibility to evaluate the quality of the registration/mapping of two given images by visual inspection. One may select no registration. Then the images will be displayed in evaluation mode assuming an identity transform (so no mapping). It is one of several MatchPoint registration plug-ins.\n \section MAP_REGEVAL_Contact Contact information This plug-in is being developed by the SIDT group (Software development for Integrated Diagnostics and Therapy) at the DKFZ (German Cancer Research Center). If you have any questions, need support, find a bug or have a feature request, feel free to contact us at www.mitk.org. \section MAP_REGEVAL_Usage Usage \imageMacro{map_view_example.png, "Example screenshot showing the plug-in in use.", 14} To use the evaluation view you must have selected at least the moving and the target image you want to use to evaluate. If you select a registration with referenced target and moving image (the normal state if you generate registrations with the MatchPoint plugins) these images will be auto selected by just clicking on the registration. If you select no registration the view will assume that an identity transform should be used.\n As long as no valid set of data is selected the "Start evaluation" button will be disabled. If its enabled you may start the evaluation mode with it. \imageMacro{map_no_data_selected.png, "Example screenshot showing the state if no data is selected", 5} If the evaluation view is active you can choose between different modes of visualization. For more details see \ref MAP_REGEVAL_Styles.\n To stop the evaluation mode, you may use the "Stop evaluation" button or just close the evaluation view. \remark The evaluation view will use the level window settings of the used images. So to changes the level windowing of the evaluation view, you must change the level windowing of the respective images. \section MAP_REGEVAL_Styles Visualization styles You can choose from the following visualization styles to evaluate the registration/mapping quality:\n \li "Blend": Blends the images with a user defined weight. Default is 50:50. \imageMacro{map_style_blend.png, "Example for mode: Blend", 5} \li "Checkerboard": Checkerboard style that composes both images. You may define the resolution of the checkerboard. \imageMacro{map_style_checkerboard.png, "Example for mode: Checkerboard", 5} \li "Color blend": Color blend of the images (blue: target image; yellow: moving). Areas where you see no color implies good intensity matchings. \imageMacro{map_style_color_blend.png, "Example for mode: Color blend", 5} \li "Contour": Blend mode that display one image as blue "background" and the other image in yellow contours. You may choose the role of the images. \imageMacro{map_style_contour.png, "Example for mode: Contour", 5} \li "Difference": Displays the absolute difference of both images. \li "Wipe": Blend mode that makes a rectilinear combination of the images. You can choose the mode how the images are splitted. The split is synchronized with the current selection. So you may interact with the split border to position it on interesting areas. \imageMacro{map_style_wipe_cross.png, "Example for mode: Wipe cross", 5} \imageMacro{map_style_wipe_horizontal.png, "Example for mode: Wipe horizontal", 5} */ diff --git a/Plugins/org.mitk.gui.qt.matchpoint.framereg/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.matchpoint.framereg/documentation/UserManual/Manual.dox index 5b5a1cb796..0cacc57216 100644 --- a/Plugins/org.mitk.gui.qt.matchpoint.framereg/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.matchpoint.framereg/documentation/UserManual/Manual.dox @@ -1,64 +1,64 @@ /** -\page org_mitk_gui_qt_matchpoint_framereg The MatchPoint Motion/Frame Correction View +\page org_mitk_gui_qt_matchpoint_framereg MatchPoint Motion/Frame Correction View \imageMacro{"map_framereg_icon_doc.svg", "Icon of the MatchPoint Algorithm Control", 3} \tableofcontents \section MAP_FRAME_Introduction Introduction This plugin offers the user a way to use a selected registration algorithm in order to make a frame/motion correction for a selected 3D+t images. The plugin is for example helpfull if you have a dynamic image with motion artifacts in same time points and you want to reduce/remove this motion artifacts. For the selection of an algorithm please see MatchPoint Algorithm Browser (\ref de_dkfz_matchpoint_mitk_gui_qt_algorithm_browser). \section MAP_FRAME_Contact Contact information This plug-in is being developed by the SIDT group (Software development for Integrated Diagnostics and Therapy) at the DKFZ (German Cancer Research Center). If you have any questions, need support, find a bug or have a feature request, feel free to contact us at www.mitk.org. \section MAP_FRAME_Usage Usage \imageMacro{"map_framereg_example.png" , "Example screenshot showing the plugin in use.", 15} To use the plugin a registration algorithm must be loaded and the image must be selected, that should be corrected.\n The correction is performed that every frame/timpoint of the image is registered to the first frame. And the corrected frames is mapped in the same geometry then the first frame.\n If an algorithm is loaded and input images are selected, the plugin will automatically switch to the "Execution" tab. \subsection MAP_FRAME_Usage_selection Algorithm selection tab \imageMacro{map_step1_selection.png, "Details of the algorithm selection tab.", 6} In this tab you can load/"book" the algorithm selected in the MatchPoint Algorithm Browser. In the tab you see the ID of the algorithm selected by the browser and its profile information.\n If you press "Load selected algorithm", the algorithm will be used by the plugin for the frame correction and the name of the algorithm occurs in the text field "Loaded algorithm" (at the top of the plugin view).\n At this point, it has no effect if you change the the selection in the browser. The plugin will keep the loaded algorithm until you choose to load another one. \subsection MAP_FRAME_Usage_exec Execution tab \imageMacro{map_step2_execution.png, "Details of the execution tab.", 6} In this tab you can specify a name for the correction job (this will determine the names of the result nodes in the data manager).\n "Start" will trigger the correction process. \subsection MAP_FRAME_Usage_settings Settings tab \imageMacro{map_step3_settings.png, "Details of the settings tab.", 6} In this tab, you can (1) define the mapping settings \ref MAP_FRAME_Mapper_Settings "(See details)", used for the corrected frames, or (2) parametrize the loaded algorithm (before it starts), if it offers any possibility to do so. \subsubsection MAP_FRAME_Mapper_Settings Mapper settings For the mapping of corrected images, you have several settings available:\n \li "Allow undefined pixels": Activate to handle pixels of the result image that are not in the field of view of the input image. This pixel will get the "padding value". \li "Allow error pixels": Activate to handle pixels of the result image that can not be mapped because the registration does not support this part of the output image. This pixel will get the "error value". \li "Interpolator": Set to choose the interpolation strategy that should be used for mapping. \ref MAP_FRAME_Interpolation "(see details)" \subsubsection MAP_FRAME_Interpolation Interpolation You can choose from the following interpolation strategies:\n \li "nearest neighbour": Use the value of the nearest pixel. Fastest, but high interpolation errors for gray value images. Right choice for label images or masks. \li "Linear": Fast linear interpolation with often sufficient quality. Tends to blur edges. \li "BSpline (3rd order)": Good trade off between time and quality. \li "Windowed Sinc (Hamming)": Good interpolation quality but very time consuming. \li "Windowed Sinc (Welch)": Good interpolation quality but very time consuming. \subsection MAP_FRAME_Usage_frame_selection Frame selection tab \imageMacro{map_step4_frameselection.png, "Details of the frame selection tab.", 6} In this tab you can specify the frames of the currently selected image that should be corrected. As default all frames of an image will be corrected. If you only select specific frames, these frames will be corrected all other frames will be just copied unchanged. */ diff --git a/Plugins/org.mitk.gui.qt.matchpoint.manipulator/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.matchpoint.manipulator/documentation/UserManual/Manual.dox index d376891913..454dee239b 100644 --- a/Plugins/org.mitk.gui.qt.matchpoint.manipulator/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.matchpoint.manipulator/documentation/UserManual/Manual.dox @@ -1,69 +1,69 @@ /** -\page org_mitk_gui_qt_matchpoint_manipulator The MatchPoint Registration Manipulator View +\page org_mitk_gui_qt_matchpoint_manipulator MatchPoint Registration Manipulator View \imageMacro{map_manipulator_icon_doc.svg, "Icon of the MatchPoint Registration Manipulator", 3} \tableofcontents \section MAP_REGMANIP_Introduction Introduction This view offers the possibility to manually manipulate a registration to establish a good mapping between data. The effect of manipulation is visualized with to user defined images to allow visual inspection.\n It is one of several MatchPoint registration plug-ins.\n \imageMacro{map_view_example.png, "Example screenshot showing the plug-in in use", 10} \section MAP_REGMANIP_Contact Contact information This plug-in is being developed by the SIDT group (Software development for Integrated Diagnostics and Therapy) at the DKFZ (German Cancer Research Center). If you have any questions, need support, find a bug or have a feature request, feel free to contact us at www.mitk.org. \section MAP_REGMANIP_Usage Usage \imageMacro{map_view_steps.png, "Illustration of the workflow steps.", 7} The typical workflow with the manipulator has following steps/sections: 1. Source selection: You can choose between starting a new registration and using a selected registration. For later option, the registration must be selected in the data manager. \remark If you choose a new registration, the manipulator will automatically pre initialize this new transform to align the centers of the used images and therefore starts with sensible settings. \remark If you choose an existing registration, the registration will *not* be altered. It serves as template/baseline for the manipulation, which will be "on top" of the existing registration. 2. Image selection: To allow visual inspection of the manipulation to images are needed. If you have selected a registration (independent from the source selection mode) the manipulator will use the moving and target images used to determine the selected registration as images for the manipulation. You can also explicitly select images in the data manager (press shift while selecting for multi select). 3. Start manual registration: If all settings are valid, you can start the manipulation. The render windows will automatically switch to the visual inspection mode. The views will be reinitialized to the field of view of the target image. 4. Generation settings: You may choose to give the resulting registration a special name. Additionally you can choose the convenience option to map the moving image with the confirmed registration automatically. 5. Settings: You can alter the settings of the transform (\ref MAP_REGMANIP_TransSettings) and the rendering settings (\ref MAP_REGMANIP_EvalSettings) for the visual inspection. 6. Cancel or confirmation: You may cancel the manipulation process (Closing the view equals cancelation) or confirm the determined registration and store it in the data storage with the given name.\n \section MAP_REGMANIP_TransSettings Transformation settings You can alter the translation and the rotation of the transform. In addition you may choose the center of rotation type. You have the following options:\n - Moving image center: Rotate around the center of the moving image. - World origin: Rotate around (0.0,0.0,0.0), the world origin. - Current navigator position: Rotate around the current navigator position in the render views. \remark FAQ: Why are the translation values "jumping" when I change the center mode or when I am rotating?\n The reason is the relation between center, rotation, and translation.\n A transformation is defined as x' = R (x - C) + C + T\n where x': transformed point; x: point to transform; R: rotation matrix; C: center point; T: translation vector.\n The offset of a transform is defined as O = -RC + C + T\n The offset as well as the rotation matrix stay constant if the center point changes, therefore the translation has to be altered. \note To ease the orientation, the edit fields have background colours which resemble the colours of the plane the changes will "happen".\n For translation, the translation vector will be perpendicular to the indicated plane (The image moves "through" the plane). For rotation, the rotation axis will be perpendicular to the indicated plane. \section MAP_REGMANIP_EvalSettings Evaluation settings The settings you can choose are equal to the settings of the evaluation view (\ref org_mitk_gui_qt_matchpoint_evaluator). Please see the documentation of the MatchPoint Registration Evaluator view for more details. */ diff --git a/Plugins/org.mitk.gui.qt.matchpoint.mapper/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.matchpoint.mapper/documentation/UserManual/Manual.dox index c02863a2a6..97112ce700 100644 --- a/Plugins/org.mitk.gui.qt.matchpoint.mapper/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.matchpoint.mapper/documentation/UserManual/Manual.dox @@ -1,85 +1,85 @@ /** -\page org_mitk_gui_qt_matchpoint_mapper The MatchPoint Image Mapper View +\page org_mitk_gui_qt_matchpoint_mapper MatchPoint Image Mapper View \imageMacro{map_mapper_icon_doc.svg, "Icon of the MatchPoint Image Mapper",3} \tableofcontents \section MAP_MAPPER_Introduction Introduction This view offers the possibility to map any image or point set in the data manager using a user selected registration object. Using the Mapper to map images the user can control the field of view (image geometry) the image should be mapped into, as well as interpolation strategy that should be used.\n It is one of several MatchPoint registration plugins.\n Typical usage scenarios\n \li You have registered image I1 onto image I2. Now you want to transfer the segmentation of I1 to I2 in order to evaluate I2 within this mapped segmentation using \ref org_mitk_views_imagestatistics . \li You have registered image I1 onto image I2. Now you want to map I3 (e.g. an other MRI sequence of the same session) also onto I2 with the same registration. \li You have registered image I1 onto image I2. Now you want to map a segmentation done on I1 also onto I2 with the same registration. \li You have registered image I1 onto image I2. Now you want to map a point set of image I1 also onto I2 with the same registration. \section MAP_MAPPER_Contact Contact information This plug-in is being developed by the SIDT group (Software development for Integrated Diagnostics and Therapy) at the DKFZ (German Cancer Research Center). If you have any questions, need support, find a bug or have a feature request, feel free to contact us at www.mitk.org. \section MAP_MAPPER_Usage Usage \imageMacro{map_mapper-examplescreen.png, "Example screenshot showing the Mapper plugin in use.", 14} To use the mapper at least an input data (image or point set) must be selected. Additionally you may select a registration object and a reference image. Registration objects are marked with a small blue icon (e.g. the data "Registration" in the data manager of the screen shot above). The Reference image defines the geometry (field of view, orientation, spacing) that should be used for the result image. By default the view will try to automatically determine the reference image (by default it is the target image of the selected registration). If auto selection cannot determine the reference it will choose the input image as reference. The reference image can be also defined by the user explicitly by activating manual selection.\n REMARK: If you map point sets you can ignore the reference image slot. It has no affect.\n You can multi select registration and data (press the CTRL-key while selecting the nodes in the data manager). The Mapper will automatically sort the selections in the correct "slots" of the view.\n REMARK: The mapping results will be added as child nodes to the used input node.\n REMARK: If you do not select an registration the view will assume that you make an identity transform. This is a convenient way if you just want to resample an image into the geometry of an other image (when no registration is needed). Also in this use case you can take advantage of the different interpolation and sub/super sampling strategies. \imageMacro{map_mapper.png, "Details of the mapper view.", 8} (1) The currently selected registration, that will be used for mapping.\n (2) The currently selected input data, that will be mapped.\n (3) The currently (automatically or by user) selected reference image, that defines the geometry of the result.\n (4) The name of the result data in the data manger.\n (5) The start button(s) to commence the mapping process. For details regarding the two options see \ref MAP_MAPPER_Refine.\n (6) Log windows with messages regarding the mapping process.\n\n Every "slot" has the ability to be locked. If locked the last selection will be kept, regardless the current selection in the data manager. You can use this for example to lock the registration, if you want to map multiple images. Doing so it is enough to just select the next image in the data manager. To lock a slot, click at the "lock" button at the right side (see example images below). \imageMacro{map_node-unlocked.png, "Unlocked slot/node (default state). Changes with the selections in the data manager.",6} \imageMacro{map_node-locked.png, "Locked slot/node. Stays, regardless the selections in the data manager.",6} \section MAP_MAPPER_Refine Mapping or geometry refinement The mapper view offers two options to map images:\n \li "Map" (default) \li "Refine geometry" For images "Map" fills the pixels of the output image by interpolating input image pixels using the registration object. This option always works. But may take longer and introduces interpolation errors, because a new image is resampled.\n The second option "Refine geometry" is only offered, if the registration (more precise its inverse kernel) is matrix based and the selected data is an image. In this case it just clones the image and refines its image geometry (origin and orientation) to project it to the position indicated by the registration; thus no interpolation artefacts are introduced. \remark If you want to use a mapped image in conjunction with the statistic plugin and an mask of the reference image (or you want to proceed any other computation that expects the voxel to be in the same grid for direct numeric comparison), you must use "Map" to ensure the same geometry (including the same image grid; including same spacing and resolution). Otherwise operations like the statistic plugin will fail. \section MAP_MAPPER_Settings Settings If you map the image (and not just refine the geometry), you have several settings available:\n \li "Allow undefined pixels": Activate to handle pixels of the result image that are not in the field of view of the input image. This pixel will get the "padding value". \li "Allow error pixels": Activate to handle pixels of the result image that can not be mapped because the registration does not support this part of the output image. This pixel will get the "error value". \li "Interpolator": Set to choose the interpolation strategy that should be used for mapping. \li "Activate super/sub sampling": Activate if you want to use origin and orientation of the reference image but want to alter the spacing. \section MAP_MAPPER_Interpolation Interpolation You can choose from the following interpolation strategies:\n \li "nearest neighbor": Use the value of the nearest pixel. Fastest, but high interpolation errors for gray value images. Right choice for label images or masks. \li "Linear": Fast linear interpolation with often sufficient quality. Tends to blur edges. \li "BSpline (3rd order)": Good trade off between time and quality. \li "Windowed Sinc (Hamming)": Good interpolation quality but very time consuming. \li "Windowed Sinc (Welch)": Good interpolation quality but very time consuming. \section MAP_MAPPER_Masks Handling of masks/segmentations If you select an mask as input image, the plugin will be automatically reconfigured to settings that are suitable for the task of mapping masks. Most importantly the interpolator will be set to "nearest neighbor". */ diff --git a/Plugins/org.mitk.gui.qt.matchpoint.visualizer/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.matchpoint.visualizer/documentation/UserManual/Manual.dox index 46cdb16a37..eafd85fc5e 100644 --- a/Plugins/org.mitk.gui.qt.matchpoint.visualizer/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.matchpoint.visualizer/documentation/UserManual/Manual.dox @@ -1,21 +1,21 @@ /** -\page org_mitk_gui_qt_matchpoint_visualizer The MatchPoint Registration Visualizer View +\page org_mitk_gui_qt_matchpoint_visualizer MatchPoint Registration Visualizer View \imageMacro{map_vis_icon_doc.svg, "Icon of the Registration Visualizer",3} \tableofcontents \section MAP_VIS_Introduction Introduction This view is in development to offer the user a way to visualize MatchPoint registrations in a MITK scene. Currently only a simple grid visualization and glyph visualization is implemented.\n \remark This is an experimental version and work in progress. So please excuse errors or usage issues and report them. This view will be improved and polished with the next releases. \section MAP_VIS_Contact Contact information This plug-in is being developed by the SIDT group (Software development for Integrated Diagnostics and Therapy) at the DKFZ (German Cancer Research Center). If you have any questions, need support, find a bug or have a feature request, feel free to contact us at www.mitk.org. \section MAP_VIS_Usage Usage Oops. Documentation is missing and to be done. */ diff --git a/Plugins/org.mitk.gui.qt.materialeditor/documentation/UserManual/QmitkSurfaceMaterialEditor.dox b/Plugins/org.mitk.gui.qt.materialeditor/documentation/UserManual/QmitkSurfaceMaterialEditor.dox index 1a6935a94d..d748264e16 100644 --- a/Plugins/org.mitk.gui.qt.materialeditor/documentation/UserManual/QmitkSurfaceMaterialEditor.dox +++ b/Plugins/org.mitk.gui.qt.materialeditor/documentation/UserManual/QmitkSurfaceMaterialEditor.dox @@ -1,10 +1,10 @@ /** -\page org_surfacematerialeditor The Surface Material Editor +\page org_surfacematerialeditor Surface Material Editor \imageMacro{QmitkSurfaceMaterialEditor_Icon.png,"Icon of the Surface Material Editor",2.00} The Surface Material Editor shows the properties of the selected data that are relevant for the selected shader. These properties can be filtered to find a specific property. The preview window shows the representation of a neutral 3D object with the currently selected settings. \imageMacro{QmitkSurfaceMaterialEditor_Gui.png,"The Surface Material Editor",10.92} */ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.openigtlink/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.openigtlink/documentation/UserManual/Manual.dox index c94e724867..d60a2c51fd 100644 --- a/Plugins/org.mitk.gui.qt.openigtlink/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.openigtlink/documentation/UserManual/Manual.dox @@ -1,17 +1,17 @@ /** -\page org_mitk_gui_qt_igtlplugin The Igtlplugin +\page org_mitk_gui_qt_igtlplugin IGT Plugin \imageMacro{icon.png,"Icon of Igtlplugin",2.00} \tableofcontents \section org_mitk_gui_qt_igtlpluginOverview Overview Describe the features of your awesome plugin here */ diff --git a/Plugins/org.mitk.gui.qt.overlaymanager/documentation/UserManual/QmitkOverlayManager.dox b/Plugins/org.mitk.gui.qt.overlaymanager/documentation/UserManual/QmitkOverlayManager.dox index e34dbb5b5c..0d52ed9311 100644 --- a/Plugins/org.mitk.gui.qt.overlaymanager/documentation/UserManual/QmitkOverlayManager.dox +++ b/Plugins/org.mitk.gui.qt.overlaymanager/documentation/UserManual/QmitkOverlayManager.dox @@ -1,11 +1,11 @@ /** -\page org_mitk_gui_qt_overlaymanager The OverlayManager Plugin +\page org_mitk_gui_qt_overlaymanager Overlay Manager Plugin \imageMacro{icon.png,"Icon of Overlaymanager",2.00} \tableofcontents \section org_mitk_gui_qt_overlaymanagerOverview Overview The OverlayManager plugin allows to view all annotations currently managed. Properties of added annotations can be modified. Additionally it is possible to create some basic overlays and register them. */ diff --git a/Plugins/org.mitk.gui.qt.pharmacokinetics.concentration.mri/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.pharmacokinetics.concentration.mri/documentation/UserManual/Manual.dox index 97e5f59a25..92124917a5 100644 --- a/Plugins/org.mitk.gui.qt.pharmacokinetics.concentration.mri/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.pharmacokinetics.concentration.mri/documentation/UserManual/Manual.dox @@ -1,16 +1,16 @@ /** -\page org_mitk_gui_qt_pharmacokinetics_concentration_mri The Concentration Curve Converter View +\page org_mitk_gui_qt_pharmacokinetics_concentration_mri Concentration Curve Converter View \imageMacro{pharmacokinetics_concentration_doc.svg,"Icon of the Concentration Curve Converter View",3.0} \tableofcontents \section org_mitk_gui_qt_pharmacokinetics_concentration_mri_overview Overview Stand-alone conversion of image signal intensities to contrast agent concentration units can be performed with a dedicated plugin. The plugin distinguishes between T1 weighted and T2 weighted sequences. T1 conversion can be performed in terms of absolute and relative signal enhancement as well as turbo flash sequences for both 3D images (baseline images S0 without contrast enhancement (pre-contrast) input required) and 4D sequences (baseline selected as first frame of time series). \section org_mitk_gui_qt_pharmacokinetics_concentration_mri_Contact Contact information This plug-in is being developed by Charlotte Debus and the SIDT group (Software development for Integrated Diagnostics and Therapy) at the DKFZ (German Cancer Research Center). If you have any questions, need support, find a bug or have a feature request, feel free to contact us at www.mitk.org. */ diff --git a/Plugins/org.mitk.gui.qt.pharmacokinetics.curvedescriptor/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.pharmacokinetics.curvedescriptor/documentation/UserManual/Manual.dox index b1e55f62a7..8e37c6e770 100644 --- a/Plugins/org.mitk.gui.qt.pharmacokinetics.curvedescriptor/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.pharmacokinetics.curvedescriptor/documentation/UserManual/Manual.dox @@ -1,26 +1,26 @@ /** -\page org_mitk_gui_qt_pharmacokinetics_curvedescriptor The Perfusion Curve Description Parameters View +\page org_mitk_gui_qt_pharmacokinetics_curvedescriptor Perfusion Curve Description Parameters View \imageMacro{pharmacokinetics_curve_desc_doc.svg,"Icon of the Perfusion Curve Description Parameters View",3.0} \tableofcontents \section org_mitk_gui_qt_pharmacokinetics_concentration_mri_overview Overview In cases where data quality is not sufficient for dedicated pharmacokinetic analysis, or if global scouting of the overall image should be performed to identify regions of interest, it is often advisable to use semi-quantitative measures that describe the general shape and type of the curve. The Perfusion Curve Description Parameters plugin can be used to voxelwise calculate these parameters. Currently the following parameters are offered by the tool: - area-under-the-curve (AUC) - area-under the first moment curve (AUMC), - mean-residence-time (MRT; AUMC/AUC) - time to peak and maximum signal These parameters are calculated directly from the sampled data. AUC and AUMC are calculated by step-wise integration with linear interpolation between sampling points. Maximum and time to peak are derived from the highest intensity value (overall maximum) of all data points. Note: If semi-quantitative parameters should be calculated from concentration time curves rather than raw data signal intensities, use the concentration n curve converter view (See 5) Parameters of interest can be selected from the list. Selecting a 4D image in the Data manager enables the Calculate Parameters button. Resulting parameter maps will afterwards be added to the data manager as subnodes to the analyzed 4D image. \section org_mitk_gui_qt_pharmacokinetics_concentration_mri_Contact Contact information This plug-in is being developed by Charlotte Debus and the SIDT group (Software development for Integrated Diagnostics and Therapy) at the DKFZ (German Cancer Research Center). If you have any questions, need support, find a bug or have a feature request, feel free to contact us at www.mitk.org. */ diff --git a/Plugins/org.mitk.gui.qt.pharmacokinetics.mri/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.pharmacokinetics.mri/documentation/UserManual/Manual.dox index ab56d736e1..2859796b18 100644 --- a/Plugins/org.mitk.gui.qt.pharmacokinetics.mri/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.pharmacokinetics.mri/documentation/UserManual/Manual.dox @@ -1,88 +1,88 @@ -/** -\page org_mitk_gui_qt_pharmacokinetics_mri The DCE MR Perfusion Datafit View +/** +\page org_mitk_gui_qt_pharmacokinetics_mri DCE MR Perfusion Datafit View \imageMacro{pharmacokinetics_mri_doc.svg,"Icon of the DCE MR Perfusion View",3.0} \tableofcontents \section FIT_DCE_Introduction Introduction For pharmacokinetic analysis of DCE MRI/CT data using compartment models in non-linear least square fitting the DCE MR Perfusion Datafit plugin can be used. \section FIT_DCE_Contact Contact information This plug-in is being developed by Charlotte Debus and the SIDT group (Software development for Integrated Diagnostics and Therapy) at the DKFZ (German Cancer Research Center). If you have any questions, need support, find a bug or have a feature request, feel free to contact us at www.mitk.org. \subsection FIT_DCE_Cite Citation information If you use the view for your research please cite our work as reference:\n\n Debus C and Floca R, Ingrisch M, Kompan I, Maier-Hein K, Abdollahi A, Nolden M, MITK-ModelFit: generic open-source framework for model fits and their exploration in medical imaging – design, implementation and application on the example of DCE-MRI (arXiv:1807.07353) \section FIT_DCE_General General information \imageMacro{dce_mri_init.png, "Example screen shot showing the view at first start.", 10} For pharmacokinetic analysis of DCE MRI/CT data using compartment models in non-linear least square fitting the DCE MR Perfusion Datafit view can be used. In principle, every model can be fitted on the entire image. However, for model configuration reasons (e.g. AIF required) and computational time cost, this is often not advisable. Therefore, apart from the image to be fitted (Selected Time Series), a ROI segmentation can be defined (Selected Mask), within which model fitting is performed. The view currently offers voxel wise and/or ROI averaged fits of intensity-time curves with different quantitative and semi-quantitative models. If a mask is selected, ROI-based fitting (fit average curve within ROI) is enabled (radio button Fitting Strategy – Pixel based / ROI based). \subsection FIT_DCE_General_models Supported models Currently the following pharmacokinetic models for gadolinium-based contrast agent are available: - The Descriptive Brix model \ref FIT_DCE_lit_ref1 "[1]" - A semi-quantitative three segment linear model (3SL) - The standard tofts model \ref FIT_DCE_lit_ref2 "[2]" - The extended Tofts model \ref FIT_DCE_lit_ref3 "[3]" - The two compartment exchange model (2CXM) \ref FIT_DCE_lit_ref4 "[4, 5]" \section FIT_DCE_Settings Model Settings \imageMacro{dce_mri_config.png, "Example screenshot showing the config settings of the view.", 10} \subsection FIT_DCE_Settings_model Model specific settings Selecting one of the \ref FIT_DCE_General_models "supported models" will open below tabs for further configuration of the model. - The descriptive Brix model requires only definition of the duration of the bolus, i.e. the overall time of the injection (Injection Time [min]) - The 3SL is a semi-quantitative descriptive model that distinguishes three different segments of the signal: A constant baseline, the initial fast rise (wash-in) and the final slow rise / signal decrease (washout). Each of these segments is approximated by a linear curve, with change points in-between. It requires no further configuration - The standard Tofts model, the extended Tofts model and the 2CXM are all three compartment models that require the input of the concentration time curve in the tissue feeding artery, the AIF. In the DCE MRI Model fitting plugin, the arterial input function can be defined in several ways. For patient individual image derived AIFs, select the radio button "Select AIF from Image". In that case, a segmentation ROI for the artery has to be given to the tool (Drop-down menu AIF Mask from Data Manager). In cases, where the respective artery does not lie in the same image as the investigated tissue (e.g. in animal experiments, where a slice through the heart is used for AIF extraction), a dedicated AIF image can be selected from the Data Manager. The other option is to define the AIF via an external file (e.g. for population derived AIFs or AIFs from blood sampling). By clicking the Browse button, one can select a csv file that holds the arterial intensity values and corresponding timepoints (in tuple format (Time, Value)). Caution: the file may not contain a header line, but the first line must start with Time and Intensity values. Furthermore, the hematocrit level has to be set (from 0 to 1) for conversion from whole blood to plasma concentration. It is set to the literature default value of 0.45. \subsection FIT_DCE_Settings_start Start parameter \imageMacro{dce_mri_start.png, "Example screen shot for start parameter settings.", 10} In cases of noisy data it can be useful to define the initial starting values of the parameter estimates, at which optimization starts, in order to prevent optimization results in local optima. Each model has default scalar values (applied to every voxel) for initial values of each parameter, however these can be adjusted. Moreover, initial values can also be defined locally for each individual voxel via starting value images. \subsection FIT_DCE_Settings_constraint Constraint settings \imageMacro{dce_mri_constraint.png, "Example screen shot for constraint settings.", 10} To limit the fitting search space and to exclude unphysical/illogical results for model parameter estimates, constraints to individual parameters as well as combinations can be imposed. Each model has default constraints, however, new ones can be defined or removed by the + and – buttons in the table. The first column specifies the parameter(s) involved in the constraint (if multiple selected, their sum will be used) by selection in the drop down menu. The second column defines whether the constraints defines an upper or lower boundary. Value and Width define the actual constraint value, that should not be crossed, and a certain tolerance width. \subsection FIT_DCE_Settings_concentration Signal to concentration conversion settings \imageMacro{dce_mri_concentration.png, "Example screen shot for concentration conversion settings.", 10} Most models require concentration values as input rather than raw signal intensities (i.e. all compartment models). The DCE MR Perfusion view offers conversion to concentration by means of relative and absolute signal enhancement as well as a special conversion for turbo flash sequences. \section FIT_DCE_Fitting Executing a fit After configuration of the entire fit routine, the respective time series to be fitted and eventually the ROI mask have to be selected. If only an image is needed, selection of the respective time series in the data manager is sufficient. If a mask is to be selected as well, image and mask have to be selected by holding the shift key and selecting them in this order from the Data manager.\n\n In order to distinguish results from different model fits to the data, a Fitting name can be defined in the bottom field. As default, the name of the model and the fitting strategy (pixel/ROI) are given. This name will then be appended by the respective parameter name.\n\n For development purposes and evaluation of the fits, the option "Generate debug parameter images" is available. Enabling this option will result in additional parameter maps displaying the status of the optimizer at fit termination, like needed optimization time, number of iterations, constraint violations and reasons for fit termination (criterion reached, maximum number of iterations, etc.).\n\n After all necessary configurations are set, the button "Start Modelling" is enabled, which starts the fitting routine. Progress can be seen in the message box on the bottom. Resulting parameter maps will afterwards be added to the data manager as sub-nodes to the analyzed 4D image. \section FIT_DCE_lit References/Literature - \anchor FIT_DCE_lit_ref1 [1] Brix G, Semmler W, Port R, Schad LR, Layer G, Lorenz WJ. Pharmacokinetic parameters in CNS Gd-DTPA enhanced MR imaging. J Comput Assist Tomogr. 1991;15:621–8. - \anchor FIT_DCE_lit_ref2 [2] Tofts PS, Kermode AG. Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magn Reson Med. 1991;17:357–67. - \anchor FIT_DCE_lit_ref3 [3] Sourbron SP, Buckley DL. On the scope and interpretation of the Tofts models for DCE-MRI. Magn Reson Med. 2011;66:735–45. - \anchor FIT_DCE_lit_ref4 [4] Brix G, Kiessling F, Lucht R, Darai S, Wasser K, Delorme S, et al. Microcirculation and microvasculature in breast tumors: Pharmacokinetic analysis of dynamic MR image series. Magn Reson Med. 2004;52:420–9. - \anchor FIT_DCE_lit_ref5 [5] Sourbron, Buckley. Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability - pdf. Phys Med Biol. 2012. http://iopscience.iop.org/article/10.1088/0031-9155/57/2/R1/pdf. Accessed 1 May 2016. */ diff --git a/Plugins/org.mitk.gui.qt.pharmacokinetics.pet/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.pharmacokinetics.pet/documentation/UserManual/Manual.dox index 6b0075415c..2104d039f9 100644 --- a/Plugins/org.mitk.gui.qt.pharmacokinetics.pet/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.pharmacokinetics.pet/documentation/UserManual/Manual.dox @@ -1,46 +1,46 @@ -/** -\page org_mitk_gui_qt_pharmacokinetics_pet The Dynamic PET DataFit View +/** +\page org_mitk_gui_qt_pharmacokinetics_pet Dynamic PET DataFit View \imageMacro{pharmacokinetics_pet_doc.svg,"Icon of the DCE MR Perfusion View",3.0} \tableofcontents \section FIT_PET_Overview Overview Pharmacokinetic analysis of concentration time curves is also of interest in the context of dynamic PET acquisition over the accumulation of a radioactive tracer in tissue. \section FIT_PET_Contact Contact information This plug-in is being developed by Charlotte Debus and the SIDT group (Software development for Integrated Diagnostics and Therapy) at the DKFZ (German Cancer Research Center). If you have any questions, need support, find a bug or have a feature request, feel free to contact us at www.mitk.org. \subsection FIT_DCE_Cite Citation information If you use the view for your research please cite our work as reference:\n\n Debus C and Floca R, Ingrisch M, Kompan I, Maier-Hein K, Abdollahi A, Nolden M, MITK-ModelFit: generic open-source framework for model fits and their exploration in medical imaging – design, implementation and application on the example of DCE-MRI (arXiv:1807.07353) \section FIT_PET_General General information All models require definition of the arterial tracer concentration, i.e. the AIF. For AIF definition see section 3. Instead of the hematocrit level, the whole blood to plasma correction value needs to be specified. The literature value commonly used is 0.1 Since PET images are already in concentration units of activity per volume ([Bq/ml], translates to number of nuclei per volume), no conversion of signal intensities to concentration is offered in the plugin. If, however, conversion of the 4D images to standard uptake values (SUV) is desired, this can be performed with the separate PET SUV calculation plugin. Start parameters and parameter constraints can be defined in the same manner as for the DCE tool. \subsection FIT_PET_General_models Supported models The PET dynamic plugin works in analogy to the DCE MRI perfusion plugin. It currently supports the following compartmental models: - One tissue compartment model (without blood volume VB) - Extended one tissue compartment model (with blood volume VB) - Two tissue compartment model (with blood volume) - Two tissue compartment model for FDG (without back exchange k4) \section FIT_PET_Settings Model Settings \subsection FIT_PET_Settings_start Start parameter In cases of noisy data it can be useful to define the initial starting values of the parameter estimates, at which optimization starts, in order to prevent optimization results in local optima. Each model has default scalar values (applied to every voxel) for initial values of each parameter, however these can be adjusted. Moreover, initial values can also be defined locally for each individual voxel via starting value images. \subsection FIT_PET_Settings_constraint Constraint settings To limit the fitting search space and to exclude unphysical/illogical results for model parameter estimates, constraints to individual parameters as well as combinations can be imposed. Each model has default constraints, however, new ones can be defined or removed by the + and – buttons in the table. The first column specifies the parameter(s) involved in the constraint (if multiple selected, their sum will be used) by selection in the drop down menu. The second column defines whether the constraints defines an upper or lower boundary. Value and Width define the actual constraint value, that should not be crossed, and a certain tolerance width. */ diff --git a/Plugins/org.mitk.gui.qt.pharmacokinetics.simulation/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.pharmacokinetics.simulation/documentation/UserManual/Manual.dox index 667143d2b5..f495f3409c 100644 --- a/Plugins/org.mitk.gui.qt.pharmacokinetics.simulation/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.pharmacokinetics.simulation/documentation/UserManual/Manual.dox @@ -1,5 +1,5 @@ /** -\page org_mitk_gui_qt_pharmacokinetics_simulation The Perfusion Data Simulation View +\page org_mitk_gui_qt_pharmacokinetics_simulation Perfusion Data Simulation View */ diff --git a/Plugins/org.mitk.gui.qt.photoacoustics.imageprocessing/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.photoacoustics.imageprocessing/documentation/UserManual/Manual.dox index 783c23717a..2967567cfe 100644 --- a/Plugins/org.mitk.gui.qt.photoacoustics.imageprocessing/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.photoacoustics.imageprocessing/documentation/UserManual/Manual.dox @@ -1,66 +1,66 @@ -/** -\page org_mitk_gui_qt_photoacoustics_imageprocessing The Photoacoustics Imageprocessing Plugin +/** +\page org_mitk_gui_qt_photoacoustics_imageprocessing Photoacoustics Imageprocessing Plugin \imageMacro{pai.png,"Icon of Imageprocessing",2.00} \tableofcontents \section org_mitk_gui_qt_photoacoustics_imageprocessingOverview Overview This plugin offers an interface to perform image processing on photoacoustic, as well as ultrasound images, i.e. to use beamforming and post-processing filters. For convenience, image processing can be done automatically for a whole batch of files containing PA or US data. \section org_mitk_gui_qt_photoacoustics_imageprocessingPrerequisites Prerequisites To use the much more performant openCL filters which run on the graphics card, MITK has to be able to use openCL, for which it is necessary to install the openCL implementation provided by your graphics card vendor. \section org_mitk_gui_qt_photoacoustics_imageprocessingFiltering Using the filters To perform image processing, simply load an image into MITK and select it in the Data manager. Only the selected image will be processed by the filters. \imageMacro{QmikPhotoacousticsImageProcessing_DataManager.png,"Select the image to be processed",7.62} Before performing reconstruction or using other filters those can be configured using the plugin's settings panel. \imageMacro{QmikPhotoacousticsImageProcessing_Settings.png,"The plugin's GUI",7.62} \subsection org_mitk_gui_qt_photoacoustics_imageprocessingImageDetails Image Details To create the .nrrd images necessary for the plugin from raw data, one can use e.g. pynrrd, a python package for very straightforward creation of .nrrd images. The Beamforming Filter is also able to read certain paramters, as the scan depth and the transducer pitch from the selected image. To this end, the image must have a time-axis spacing in µs and a horizontal spacing in mm. \subsection org_mitk_gui_qt_photoacoustics_imageprocessingBeamforming The Beamforming Settings For beamforming, three beamforming algorithms are available: Each of those can be coupled with either spherical delay calculation or a quadratic approximation for the delays. To supress noise, one of the following apodizations can be chosen to be used when beamforming: Other Standard beamforming parameters are available, which have to be chosen depending on the source image to attain a correctly reconstructed image. As mentioned above, Plugin is able to calculate the used scan depth as well as the transducer pitch from the selected image if the time-axis spacing is in microseconds, and the horizontal spacing in mm. If such a spacing is given, check the box "Auto Get Depth" to make the plugin read those values by itself. If the US source or the laser used for imaging is not located at the top of the image, an option is given to cut off pixels at the top of the image until the source. This value should be calibrated by the user to match the used hardware. If one wishes to beamform only certain slices of a given image, those can be selected by checking "select slices" and setting the "min" and "max" values accordingly, which are to be understood as closed interval boundaries. \subsection org_mitk_gui_qt_photoacoustics_imageprocessingBandpass The Bandpass Settings The bandpass uses an itk implementation of an 1D Fast Fourier Transform (FFT) to transform the image vertically, then filters the image using a Tukey window in the frequency domain and performs an inverse 1D FFT to get the filtered image. The "smoothness" of the tukey window can be chosen by using the "Tukey window alpha" parameter. The Tukey window interpolates between a Box window (alpha = 0) and a Von Hann window (alpha = 1). The filtered frequencies can be set by defining the High and Low pass frequencies. \subsection org_mitk_gui_qt_photoacoustics_imageprocessingCrop The Crop Filter Settings The crop filter cuts off parts of the image at the top and the bottom. The amount of pixels cut off can be configured using the "Cut Top" and "Cut Bottom" parameters. \subsection org_mitk_gui_qt_photoacoustics_imageprocessingBMode The BMode Filter Settings The B-mode filters available are: If desired, the filter can also resample the image to a given spacing; to do this, check the "Do Resampling" box and set the desired spacing in mm. Afterwards a logarithmic filter can be applied, if "Add Logfilter" is checked. \subsection org_mitk_gui_qt_photoacoustics_imageprocessingBatch Batch Processing When processing large amounts of data, an option is available to automatically process multiple images by applying all filters in order to those images and saving the resulting images. In the first row of the Batch Processing Panel one can select which filters should be applied to the image; in the second row one can select whether the resulting image from the filter should be saved. After pressing the "Start Batch Processing" button, one can choose first the images to be processed, and then the folder where they will be saved. */ diff --git a/Plugins/org.mitk.gui.qt.photoacoustics.pausmotioncompensation/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.photoacoustics.pausmotioncompensation/documentation/UserManual/Manual.dox index 4497cf1a23..719631874f 100644 --- a/Plugins/org.mitk.gui.qt.photoacoustics.pausmotioncompensation/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.photoacoustics.pausmotioncompensation/documentation/UserManual/Manual.dox @@ -1,17 +1,17 @@ /** -\page org_mitk_gui_qt_photoacoustics_pausmotioncompensation The Pausmotioncompensation +\page org_mitk_gui_qt_photoacoustics_pausmotioncompensation Pausmotioncompensation \imageMacro{icon.png,"Icon of Pausmotioncompensation",2.00} \tableofcontents \section org_mitk_gui_qt_photoacoustics_pausmotioncompensationOverview Overview Describe the features of your awesome plugin here */ diff --git a/Plugins/org.mitk.gui.qt.photoacoustics.spectralunmixing/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.photoacoustics.spectralunmixing/documentation/UserManual/Manual.dox index 177ecacda1..1e62f690f4 100644 --- a/Plugins/org.mitk.gui.qt.photoacoustics.spectralunmixing/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.photoacoustics.spectralunmixing/documentation/UserManual/Manual.dox @@ -1,37 +1,37 @@ /** -\page org_mitk_gui_qt_photoacoustics_spectralunmixing The spectral unmixing (SU) plugin +\page org_mitk_gui_qt_photoacoustics_spectralunmixing Spectral Unmixing (SU) Plugin \imageMacro{icon.png,"Icon of Spectralunmixing",2.00} \table of contents \section org_mitk_gui_qt_photoacoustics_spectralunmixingIntroduction The spectral unmixing plugin provides a GUI tool to perform spectral unmixing of multispectral MITK images. It was designed to unmix beamformed photoacoustic images. The outputs are MITK images for every chosen absorber (endmember). Furthermore it is possible to calculate the oxygen saturation of the multispectral input if the endmembers oxy- and deoxyhemoglobin are selected in the GUI as well as an output image that contains the information about the relative error between unmixing result and the input image. Detailed information about the Plugin, the baseclass and its subclasses can be found in their header files. If you want to call the SU filter from your own class have a look at the “mitkSpectralUnmixingTest.cpp”. There you find information about which functions are callable or have to be called from your class to guarantee the promised functionality of the SU filter. \section org_mitk_gui_qt_photoacoustics_spectralunmixingOverview \section How to add an additional algorithm: If the algorithm fits in one of the existing classes you can ignore steps 0. – 3. 0. Have a look at the commit rMITK36cfd1731089: implement three empty classes for Simplex, Lagrange and Vigra SU algorithms. Which actually are exactly the first (not all!) steps to implement a new algorithm. 1. Add your future header and cpp file to files.cmake 2. Create a header file which needs at least the methods shown in header.png \imageMacro{header.png,"empty header for a new SU algorithm",2.00} 3. Create a cpp file which takes an Eigen endmember matrix and an Eigen input vector as inputs and returns an Eigen vector as result. A structure like in the cpp.png is recommended. If your class will consist of more than one algorithm you should have an if/else decision between them with an enum like in the cpp.png otherwise you can directly return your result. \imageMacro{cpp.png,"example cpp file for a new SU algorithm",2.00} 4. In the Plugin you just have to add another “else if” like in the plugin.png. The string in the else if has to be the same then selectable in the GUI(step 5). \imageMacro{plugin.png,"changes of Plugin for a new SU algorithm",2.00} 5. To make you algorithm selectable you have to add to the GUI Combobox. Klick at 1. (GUI.png), then at 2. and then name your algorithm 3. like in step 4. \imageMacro{GUI.png,"changes of GUI for a new SU algorithm",2.00} */ diff --git a/Plugins/org.mitk.gui.qt.pointsetinteractionmultispectrum/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.pointsetinteractionmultispectrum/documentation/UserManual/Manual.dox index 4f242d923f..a3df95b4f8 100644 --- a/Plugins/org.mitk.gui.qt.pointsetinteractionmultispectrum/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.pointsetinteractionmultispectrum/documentation/UserManual/Manual.dox @@ -1,17 +1,17 @@ /** -\page org_mitk_gui_qt_pointsetinteractionmultispectrum The Pointsetinteractionmultispectrum +\page org_mitk_gui_qt_pointsetinteractionmultispectrum Point Set Interaction Multispectrum \imageMacro{icon.png,"Icon of Pointsetinteractionmultispectrum",2.00} \tableofcontents \section org_mitk_gui_qt_pointsetinteractionmultispectrumOverview Overview Describe the features of your awesome plugin here */ diff --git a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/QmitkPhenotypingPortalPage.dox b/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/QmitkPhenotypingPortalPage.dox index e9d319220e..090d76edd3 100644 --- a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/QmitkPhenotypingPortalPage.dox +++ b/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/QmitkPhenotypingPortalPage.dox @@ -1,41 +1,41 @@ /** -\page org_mitk_gui_qt_mitkphenotyping MITK Phenotyping +\page org_mitk_gui_qt_mitkphenotyping Phenotyping \tableofcontents MITK Phenotyping is a selection of algorithms that can be used to extract image-based phenotypes, for example using a radiomics approach. The software is part of the research of the Division of Medical Image Computing of the German Cancer Research Center (DKFZ). MITK Phenotyping is not intended to be a single application, it is rather a collection of the necessary plugins within the offical MITK releases. The functionality of MITK Phenotyping can be accessed in different ways: Using the graphical interface using the Plugins listed below, using command line applications, or using one of the programming interfaces. \section org_mitk_gui_qt_mitkphenotyping_Tutorials Tutorials \li \subpage org_mitk_views_radiomicstutorial_gui_portal A tutorial on how to use the grapical interface of MITK Phenotying \section org_mitk_gui_qt_mitkphenotyping_Views Views \subsection sub2 Specific Views: Views that were developed with the main focus on Radiomics. They still might be used in other use-cases as well: \li \subpage org_mitk_views_radiomicstransformationview : Image transformations like Resampling, Laplacian of Gaussian, and Wavelet Transformations \li \subpage org_mitk_views_radiomicsmaskprocessingview : Processing and Cleaning of Masks \li \subpage org_mitk_views_radiomicsarithmetricview : Processing images using mathematical operations \li \subpage org_mitk_views_radiomicsstatisticview : Calculate Radiomics Features \subsection sub1 Non-Specific Views: This section contains views that are included within MITK Phenotyping, but were developed with a broader application in mind. \li \subpage org_mitk_views_basicimageprocessing : Deprecated plugin for performing different image-related tasks like subtraction, mutliplaction, filtering etc. \li \subpage org_mitk_gui_qt_matchpoint_algorithm_browser : Selection of MatchPoint (Registration) Algorithm \li \subpage org_mitk_gui_qt_matchpoint_algorithm_control : Configuring and Controlling MatchPoint (Registration) Algorithm \li \subpage org_mitk_gui_qt_matchpoint_evaluator : Evaluate the Registration performance using MatchPoint \li \subpage org_mitk_gui_qt_matchpoint_manipulator : Adapt a registration calculated using MatchPoint \li \subpage org_mitk_gui_qt_matchpoint_mapper : Apply a MatchPoint Registration to a specific image \li \subpage org_mitk_gui_qt_matchpoint_visualizer : Visualize a Registration obtained with MatchPoint \li \subpage org_mitk_gui_qt_matchpoint_algorithm_batch : Running MatchPoint over multiple images (BatchMode) \li \subpage org_mitk_views_multilabelsegmentation : Create and editing of Multilabel-Segmentations. \li \subpage org_mitk_views_segmentation : Create simple segmentations \li \subpage org_mitk_views_segmentationutilities : Utilities for the processing of simple segmentations. \section radiomics_miniapps MiniApps (Command line Tools) \li \subpage MiniAppExplainPage Explanation of the Command Line App concept in MITK \li \subpage mitkBasicImageProcessingMiniAppsPortalPage : List of common preprocessing MiniApps \li \subpage mitkClassificationMiniAppsPortalPage : (Incomplete) list of MITK Classification MiniApps */ diff --git a/Plugins/org.mitk.gui.qt.renderwindowmanager/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.renderwindowmanager/documentation/UserManual/Manual.dox index 62b08443be..5b00c3db4f 100644 --- a/Plugins/org.mitk.gui.qt.renderwindowmanager/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.renderwindowmanager/documentation/UserManual/Manual.dox @@ -1,17 +1,17 @@ /** -\page org_mitk_gui_qt_renderwindowmanager The Renderwindowmanager +\page org_mitk_gui_qt_renderwindowmanager Renderwindowmanager \imageMacro{icon.png,"Icon of Renderwindowmanager",2.00} \tableofcontents \section org_mitk_gui_qt_renderwindowmanagerOverview Overview Describe the features of your awesome plugin here */ diff --git a/Plugins/org.mitk.gui.qt.spectrocamrecorder/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.spectrocamrecorder/documentation/UserManual/Manual.dox index 994c6468d9..dbdd4f158e 100644 --- a/Plugins/org.mitk.gui.qt.spectrocamrecorder/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.spectrocamrecorder/documentation/UserManual/Manual.dox @@ -1,17 +1,17 @@ /** -\page org_mitk_gui_qt_spectrocamrecorder The Spectrocamrecorder +\page org_mitk_gui_qt_spectrocamrecorder Spectrocamrecorder \imageMacro{icon.png,"Icon of Spectrocamrecorder",2.00} \tableofcontents \section org_mitk_gui_qt_spectrocamrecorderOverview Overview Describe the features of your awesome plugin here */ diff --git a/Plugins/org.mitk.gui.qt.toftutorial/documentation/Manual/Manual.dox b/Plugins/org.mitk.gui.qt.toftutorial/documentation/Manual/Manual.dox index 2b5d2dda30..c1d1db20c4 100644 --- a/Plugins/org.mitk.gui.qt.toftutorial/documentation/Manual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.toftutorial/documentation/Manual/Manual.dox @@ -1,13 +1,13 @@ /** -\page org_toftutorial ToFTutorial +\page org_toftutorial ToF Tutorial \imageMacro{icon.png,"Icon of ToFTutorial",2} Available sections: - \ref ToFTutorialOverview \section ToFTutorialOverview This is the description for the ToFTutorial. */ diff --git a/Plugins/org.mitk.gui.qt.ultrasound/documentation/UserManual/QmitkUltrasound.dox b/Plugins/org.mitk.gui.qt.ultrasound/documentation/UserManual/QmitkUltrasound.dox index 4abc2ba488..b07ad8ca3d 100644 --- a/Plugins/org.mitk.gui.qt.ultrasound/documentation/UserManual/QmitkUltrasound.dox +++ b/Plugins/org.mitk.gui.qt.ultrasound/documentation/UserManual/QmitkUltrasound.dox @@ -1,95 +1,95 @@ /** -\page org_mitk_gui_qt_ultrasound The Ultrasound Plugin +\page org_mitk_gui_qt_ultrasound Ultrasound Plugin \imageMacro{QmitkUltrasound_Icon.png,"Icon of the Ultrasound Plugin",2.12} \tableofcontents \section org_mitk_gui_qt_ultrasoundOverview Overview This plugin offers a simple interface to create and manage ultrasound devices. Devices, once configured, will be stored and loaded on the next start of MITK. One can configure several aspects of the images acquired. Last but not least, this plugin makes the configured devices available as a microservice, exposing them for further usage in other plugins. \section org_mitk_gui_qt_ultrasoundPrerequisites Prerequisites To make use of this plugin, you obviously require an ultrasound device. The device must have a video output or must be one of the supported API devices (at the moment only Telemed LogicScan 128 is supported as an API device. Typical video outputs are: HDMI, DVI, VGA and S-Video. You also need a Video-Grabber that can acquire the image data from the ultrasound device. In principal, this plugin is compatible with any grabber that allows the operating system to access it's functionality. However, not all grabbers are created equal. Make sure your grabber supports the video-out offered by your ultrasound device and that it can achieve a satisfying framerate. We have made good experiences with epiphan Grabbers and currently recommend the Epiphan DVI2USB 3.0 device which supports HDMI, DVI and VGA, but less costly grabbers certainly are an option. \section org_mitk_gui_qt_ultrasoundCreateDevice Creating an Device To configure an ultrasound device as a video device, connect it to the grabber and the grabber to the computer. Start the ultrasound device and open the ultrasound plugin. The devicemanager will open. \imageMacro{QmitkUltrasound_DeviceManagement.png,"MITK Screenshot With the Device Manager Activated",7.54} Any currently configured devices are listed in the box, which accordingly is empty now. The creation of API devices depends on the device. A Telemed device would be listed in the box if this version of MITK was compiled with support for Telemed devices (see \link USHardwareTelemedPage\endlink). Such a device requires no configuration. Click "New Video Device" if you want to create a new video device. \imageMacro{QmitkUltrasound_NewVideoDevice.png,"The 'New Device' form",7.62} In the appearing form, enter descriptive data on your device in the corresponding fields. Manufacturer and model will be used to display the device in MITK. You may choose the video source ID if more than one is available (as is the case on laptops with built-in webcams). Try 0 and 1. If the wrong camera is addressed, simply try the next ID. Most ultrasound images are grey scale, so using a grey scale conversion doesn't take information away from the image, but makes processing images significantly faster. Only uncheck this box if you require color. Click "Add Video Device" to save your changes. \imageMacro{QmitkUltrasound_DeviceManagement2.png,"Devicemanager With a Configured Device",7.64} An ultrasound device in MITK can be activated or removed. Removing may not be available for some API devices. The device you just created is available to all other plugins in MITK, but does not yet generate image data. Activating the device will start image generating. \section org_mitk_gui_qt_ultrasoundUseDevice Using an Ultrasound Device Click the device, then click "Activate Device". The device is now activated and generates image data continuously. The device is listed in the box on the bottom of the view now. Viewing of the image data can be started by selecting the device in this list and click the "Start Viewing" button. \imageMacro{QmitkUltrasound_Imaging.png,"US Imaging Tab for a Video Device",7.60} You can adjust the cropping parameters to reduce the acquired image size which will further increase speed and remove unnecessary information. All changes are saved and restored whenever MITK is started. If an API device was selected, buttons for controlling the b mode imaging may be available. This depends on the implementation of a control interface for this specific device in MITK. \imageMacro{QmikUltrasound_BModeControls.png,"B Mode Controls for an API Device",7} */ diff --git a/Plugins/org.mitk.gui.qt.volumevisualization/documentation/UserManual/QmitkVolumeVisualization.dox b/Plugins/org.mitk.gui.qt.volumevisualization/documentation/UserManual/QmitkVolumeVisualization.dox index 49360ff183..786233b54b 100644 --- a/Plugins/org.mitk.gui.qt.volumevisualization/documentation/UserManual/QmitkVolumeVisualization.dox +++ b/Plugins/org.mitk.gui.qt.volumevisualization/documentation/UserManual/QmitkVolumeVisualization.dox @@ -1,154 +1,154 @@ /** -\page org_mitk_views_volumevisualization The Volume Visualization Plugin +\page org_mitk_views_volumevisualization Volume Visualization Plugin \imageMacro{volume_visualization.svg,"Icon of the Volume Visualization Plugin",2.00} \tableofcontents \section QVV_Overview Overview The Volume Visualization Plugin is a basic tool for visualizing three dimensional medical images. MITK provides generic transfer function presets for medical CT data. These functions, that map the gray-value to color and opacity, can be interactively edited. Additionally, there are controls to quickly generate common used transfer function shapes like the threshold and bell curve to help identify a range of grey-values. \imageMacro{QmitkVolumeVisualization_Overview.png,"",16.00} \section QVV_EnableVRPage Enable Volume Rendering \subsection QVV_LoadingImage Loading an image into the application Load an image into the application by Volume Visualization imposes following restrictions on images: \subsection QVV_EnableVR Enable Volumerendering \imageMacro{QmitkVolumeVisualization_Checkboxen.png,"",8.21} Select an image in datamanager and click on the checkbox left of "Volumerendering". Please be patient, while the image is prepared for rendering, which can take up to a half minute. \subsection QVV_LODGPU Dropdown menus for the rendering and blend modes Two dropdown menus allow selection of rendering mode (Default, RayCast, GPU) and the blend mode (Composite, Max, Min, Avg, Add). Any Volume Rendering mode requires a lot of computing resources including processor, memory and often also graphics card. The Default selection usually finds the best rendering mode for the available hardware. Alternatively, it is possible to manually specify the selections RayCast and GPU. The RayCast selection is based on CPU computation and therefore typically slow, but allows to render without hardware acceleration. The GPU selection uses computing resources on the graphics card to accelerate volume rendering. It requires a powerful graphics card and OpenGL hardware support for shaders, but achieves much higher frame rates than software-rendering. Blend modes define how the volume voxels intersected by the rendering rays are pooled. The composite mode specifies standard volume rendering, for which each voxel contributes equally with opacity and color. Other blend modes simply visualize the voxel of maximum / minimum intensity and average / add the intentities along the rendering ray. \section QVV_PresetPage Applying premade presets \subsection QVV_Preset Internal presets There are some internal presets given, that can be used with normal CT data (given in Houndsfield units). A large set of medical data has been tested with that presets, but it may not suit on some special cases. Click on the "Preset" tab for using internal or custom presets. \imageMacro{QmitkVolumeVisualization_InternalPresets.png,"",8.30} \subsection QVV_CustomPreset Saving and loading custom presets After creating or editing a transferfunction (see \ref QVV_Editing or \ref QVV_ThresholdBell), the custom transferfunction can be stored and later retrieved on the filesystem. Click "Save" (respectively "Load") button to save (load) the threshold-, color- and gradient function combined in a single .xml file. \section QVV_ThresholdBell Interactively create transferfunctions Beside the possibility to directly edit the transferfunctions (\ref QVV_Editing), a one-click generation of two commonly known shapes is given. Both generators have two parameters, that can be modified by first clicking on the cross and then moving the mouse up/down and left/right. The first parameter "center" (controlled by horizontal movement of the mouse) specifies the gravalue where the center of the shape will be located. The second parameter "width" (controlled by vertical movement of the mouse) specifies the width (or steepness) of the shape. \subsection Threshold Click on the "Threshold" tab to active the threshold function generator. \imageMacro{QmitkVolumeVisualization_Threshold.png,"",8.21} A threshold shape begins with zero and raises to one across the "center" parameter. Lower widths results in steeper threshold functions. \subsection Bell Click on the "Bell" tab to active the threshold function generator. \imageMacro{QmitkVolumeVisualization_Bell.png,"",8.23} A threshold shape begins with zero and raises to one at the "center" parameter and the lowers agains to zero. The "width" parameter correspondens to the width of the bell. \section QVV_Editing Customize transferfunctions in detail \subsection QVV_Navigate Choosing grayvalue interval to edit \imageMacro{QmitkVolumeVisualization_Slider.png,"",8.23} To navigate across the grayvalue range or to zoom in some ranges use the "range"-slider. All three function editors have in common following: There are three transferfunctions to customize: \subsection QVV_GO Grayvalue -> Opacity \imageMacro{QmitkVolumeVisualization_Opacity.png,"grayvalues will be mapped to opacity.",8.04} An opacity of 0 means total transparent, an opacity of 1 means total opaque. \subsection QVV_GC Grayvalue -> Color \imageMacro{QmitkVolumeVisualization_Color.png,"grayvalues will be mapped to color.",8.81} The color transferfunction editor also allows by double-clicking a point to change its color. \subsection QVV_GGO Grayvalue and Gradient -> Opacity \imageMacro{QmitkVolumeVisualization_Gradient.png,"",8.85} Here the influence of the gradient is controllable at specific grayvalues. */ diff --git a/Plugins/org.mitk.gui.qt.xnat/documentation/UserManual/QmitkXnatPluginManual.dox b/Plugins/org.mitk.gui.qt.xnat/documentation/UserManual/QmitkXnatPluginManual.dox index 2ee8680fff..71ee218211 100644 --- a/Plugins/org.mitk.gui.qt.xnat/documentation/UserManual/QmitkXnatPluginManual.dox +++ b/Plugins/org.mitk.gui.qt.xnat/documentation/UserManual/QmitkXnatPluginManual.dox @@ -1,113 +1,113 @@ /** -\page org_mitk_gui_qt_xnat The XNAT Plugin +\page org_mitk_gui_qt_xnat XNAT Plugin \imageMacro{xnat-docu-icon.png,"Icon of XNAT Plugin",1.00} \tableofcontents \section org_mitk_gui_qt_xnatOverview Overview This plug-in is allows the communication with a XNAT server from within MITK. The benefits of this plug-in are: \imageMacro{QmitkXnat_IMGTreeBrowser.png,"The XNAT tree-browser plugin", 6.00} \section org_mitk_gui_qt_xnatConnection Connect to XNAT For communicating with XNAT you have to connect to your XNAT instance. \subsection org_mitk_gui_qt_xnatPreferences XNAT preferences In order to establish the connection to XNAT you have to specify the XNAT server address, username and password in the XNAT preference page. It is also possible to specify a network proxy if you are behind one (this could lead to connection errors). Via the download path you can tell MITK where to save the data that is uploaded or downloaded. A screenshot of the preference page can be seen below. \imageMacro{QmitkXnat_IMGPreferences.png,"The XNAT preference page", 10.00} \subsection org_mitk_gui_qt_xnatSessionHandling Session time-out handling Once you are connected to XNAT, MITK takes care about the session handling. A existing XNAT session times out after a certain period of inactivity, usually after 15 minutes. One minute before session time-out MITK asks you to renew the session: \imageMacro{QmitkXnat_IMGSessionAboutToTimeOut.png,"Notification that the session will time-out in a minute", 6.00} If you do not renew the session MITK notifies you if the session has expired: \imageMacro{QmitkXnat_IMGSessionExpired.png,"Notification that the session has expired", 6.00} \section org_mitk_gui_qt_xnatBrowsing Browse data Once you are connected to XNAT, MITK displays the XNAT data hierachy as a tree within the XNAT tree browser. You can browse through the data by expanding the tree items ( which ist also possible via double click). If you hover the mouse cursor over an item you will see a tool tip with detailed information. The following table gives an overview about the icons of the treeview and the related XNAT objects
XNAT Icon Overview
Icon XNAT object
\imageMacro{xnat-server.png, "" ,0.50} XNAT Server
\imageMacro{xnat-project.png, "" ,0.50} XNAT Project
\imageMacro{xnat-subject.png, "" ,0.50} XNAT Subject
\imageMacro{xnat-experiment.png, "" ,0.50} XNAT Experiment
\imageMacro{xnat-folder-icon.png, "" ,0.50} XNAT Scan top-level folder
\imageMacro{xnat-scan.png, "" ,0.50} XNAT Scan
\imageMacro{xnat-folder-icon.png, "" ,0.50} XNAT Resource top-level folder
\imageMacro{xnat-resource.png, "" ,0.50} XNAT Resource folder
\imageMacro{xnat-file.png, "" ,0.50} XNAT File
If you have selected a XNAT project, subject or experiment, MITK displays detailed information in a separate info area below the tree browser \section org_mitk_gui_qt_xnatDownload Download data MITK allows you to download your data from XNAT. You can either simply download the data to your file system or your can download it and open it immediately in the workbench. \subsection org_mitk_gui_qt_xnatDownload Download data to file system If you just want to download data, simply right-click on a XNAT file or scan on the tree brower and select "Download". The data will be stored in the specified download location. \subsection org_mitk_gui_qt_xnatDownload Download data and open it in MITK You can download and open data in three ways: \imageMacro{QmitkXnat_IMGDownloadButton.png,"The XNAT dowload button", 16.00} \section org_mitk_gui_qt_xnatUpload Upload data You can also upload data to XNAT from within the workbench. Please note that it is currently only possible to upload files as resources. Uploading DICOMs is not yet available. \subsection org_mitk_gui_qt_xnatUpload1 Upload from file system If you just want to upload a local file, right-click on a resource and select "Upload". A file dialog will open, which allows you to select a file for upload. \subsection org_mitk_gui_qt_xnatUpload2 Upload via data manager If you want to upload data from within the MITK Workbench you can simply right-click on the data node in the data manager and select "Upload to XNAT". An "XNAT Upload dialog" will appear: \imageMacro{QmitkXnat_IMGUploadDialog.png,"The XNAT upload dialog", 16.00} You can then either select a existing resource folder if MITK is able to find ones or you can select the upload destination in a tree view. \subsection org_mitk_gui_qt_xnatUpload3 Upload via drag and drop Another way to upload the data is to drag the data node on the XNAT resource where you want to upload it to. \subsection org_mitk_gui_qt_xnatUpload4 Upload via XNAT Plugin Finally, if you select a data node and you select a XNAT resource, the upload button of the XNAT plugin will be enabled. If you click on that, the data will be uploaded. \imageMacro{QmitkXnat_IMGUploadButton.png,"The XNAT upload button", 16.00} \section org_mitk_gui_qt_xnatAddResourceFolder Create a resource folder You can also create new resources (i.e. folders to group your files in XNAT). Therefor you can either right-click on a project, subject, experiment or scan and enter the name of the folder in the appearing dialog. Or you simply select one of these items and click on the "Add folder button" above the tree browser. \imageMacro{QmitkXnat_IMGAddFolderButton.png,"The XNAT add folder button", 16.00} \subsection org_mitk_gui_qt_xnatCreateSubject Create a subject You can create subjects by right-clicking on a project and selecting "Create new subject". A dialog will appear where you have to enter the subject information. \subsection org_mitk_gui_qt_xnatCreateExperiment Create a experiment You can create experiments by right-clicking on a subject and selecting "Create new experiment". A dialog will appear where you have to enter the experiment information. */