diff --git a/Documentation/Doxygen/2-UserManual/MITKPluginManualsList.dox b/Documentation/Doxygen/2-UserManual/MITKPluginManualsList.dox index c4d4470da7..133bb9f5ab 100644 --- a/Documentation/Doxygen/2-UserManual/MITKPluginManualsList.dox +++ b/Documentation/Doxygen/2-UserManual/MITKPluginManualsList.dox @@ -1,84 +1,84 @@ /** \page PluginListPage MITK Plugin Manuals The plugins and bundles provide much of the extended functionality of MITK. Each encapsulates a solution to a problem and associated features. This way one can easily assemble the necessary capabilites for a workflow without adding a lot of bloat, by combining plugins as needed. \subpage PluginListGeneralPage \subpage PluginListSpecificPage */ diff --git a/Documentation/Doxygen/2-UserManual/MITKPluginSpecificManualsList.dox b/Documentation/Doxygen/2-UserManual/MITKPluginSpecificManualsList.dox index c470916167..ebc2b9753a 100644 --- a/Documentation/Doxygen/2-UserManual/MITKPluginSpecificManualsList.dox +++ b/Documentation/Doxygen/2-UserManual/MITKPluginSpecificManualsList.dox @@ -1,45 +1,45 @@ /** \page PluginListSpecificPage List of Application-specific Plugins \li \subpage org_mitk_gui_qt_aicpregistration \li \subpage org_mitk_gui_qt_cest \li \subpage org_mitk_gui_qt_classificationsegmentation \li \subpage org_mitk_gui_qt_flowapplication \li \subpage org_mitk_views_cmdlinemodules \li \subpage org_mitk_views_pharmacokinetics_concentration_mri \li \subpage org_mitk_views_pharmacokinetics_mri - \li \subpage org_mitk_gui_qt_pharmacokinetics_pet + \li \subpage org_mitk_views_pharmacokinetics_pet \li \subpage org_mitk_gui_qt_eventrecorder \li \subpage org_mitk_gui_qt_examples \li \subpage org_mitk_gui_qt_geometrytools \li \subpage org_mitk_gui_qt_igtexample \li \subpage org_mitk_gui_qt_igtlplugin \li \subpage org_mitk_gui_qt_igttracking - \li \subpage org_mitk_gui_qt_igttrackingsemiautomaticmeasurement + \li \subpage org_mitk_views_igttrackingsemiautomaticmeasurement \li \subpage org_mitk_views_imagestatistics \li \subpage org_mitk_gui_qt_lasercontrol \li \subpage org_mitk_views_fit_demo \li \subpage org_mitk_gui_qt_fit_genericfitting \li \subpage org_mitk_views_fit_inspector \li \subpage org_mitkexamplesopencv \li \subpage org_mitk_gui_qt_overlaymanager \li \subpage org_mitk_gui_qt_mitkphenotyping \li \subpage org_mitk_gui_qt_photoacoustics_pausmotioncompensation \li \subpage org_mitk_example_gui_pcaexample \li \subpage org_mitk_gui_qt_preprocessing_resampling \li \subpage org_mitk_views_pharmacokinetics_curvedescriptor - \li \subpage org_mitk_gui_qt_photoacoustics_imageprocessing + \li \subpage org_mitk_views_photoacoustics_imageprocessing \li \subpage org_mitk_gui_qt_pharmacokinetics_simulation \li \subpage org_mitk_gui_qt_pointsetinteractionmultispectrum \li \subpage org_mitk_gui_qt_renderwindowmanager \li \subpage org_mitk_gui_qt_photoacoustics_spectralunmixing \li \subpage org_mitk_gui_qt_spectrocamrecorder \li \subpage org_surfacematerialeditor \li \subpage org_blueberry_ui_qt_objectinspector \li \subpage org_toftutorial \li \subpage org_mitk_gui_qt_ultrasound \li \subpage org_mitk_gui_qt_igt_app_echotrack \li \subpage org_mitk_gui_qt_xnat */ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.igt.app.hummelprotocolmeasurements/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.igt.app.hummelprotocolmeasurements/documentation/UserManual/Manual.dox index 3a2b25ac16..426c619338 100644 --- a/Plugins/org.mitk.gui.qt.igt.app.hummelprotocolmeasurements/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.igt.app.hummelprotocolmeasurements/documentation/UserManual/Manual.dox @@ -1,18 +1,18 @@ /** -\page org_mitk_gui_qt_igttrackingsemiautomaticmeasurement IGT Tracking Semi Automatic Measurement +\page org_mitk_views_igttrackingsemiautomaticmeasurement The IGT Tracking Semi Automatic Measurement \imageMacro{icon.png,"Icon of IGT Tracking Semi Automatic Measurement",2.00} \tableofcontents \section Overview Dieses PlugIn dient zur semiautomatischen Aufzeichnung von Messreihen mit Trackingsystemen. Entsprechend konfiguriert ist es auch für das Hummel-Protokoll einsetzbar. Zentrale Komponente des PlugIns ist eine IGT Pipeline zur Messdatenaufzeichnung, wie in Abbildung 1 dargestellt. \imageMacro{pipeline.png,"Icon of IGT Tracking Semi Automatic Measurement",10.00} Ein Screenshot der Benutzeroberfläche des PlugIns ist in Abbildung 2 zu sehen. Das Initialisieren und Starten des Trackingsystems erfolgt dabei im nicht dargestellten Tab "Tracking Initialization", der im Wesentlichen aus dem TrackingDeviceConfigurationWidget besteht. Zur Durchführung der Messungen unterstützt das PlugIn das Laden einer Liste mit Dateinamen für die Messungen, wie im oberen Teil des Screenshots zu sehen. Diese Liste wird abgearbeitet, wobei mit dem Button "Start Next Measurement" jeweils die nächste Messung gestartet wird. Die während der Messung aufgezeichneten Daten werden in eine Datei das Ausgabeverzeichnis geschrieben. Dabei entspricht der Dateiname dem aktuellen Namen aus der Liste. Die Anzahl der aufzuzeichnenden Messwerte pro Messung kann in den Einstellungen angegeben werden. Gab es bei einer Messung einen Fehler kann die Messung durch Auswahl des entsprechenden Buttons auch wiederholt werden. \imageMacro{screenshot.png,"Icon of IGT Tracking Semi Automatic Measurement",10.00} Das PlugIn unterstützt außerdem die Ansteuerung eines zweiten Trackingsystems. Dieses System soll einen am Phantom angebrachtes Tool (Reference Sensor) tracken und so sicherstellen, dass sich das Phantom während der Messung nicht bewegt. Wurde eine Bewegung des Phantoms festgestellt wird im unteren Teil des PlugIns "NOT OK" angezeigt und die Messung muss ggf. wiederholt werden. */ 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 46579d046d..126c9e07f2 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 @@ -1,104 +1,104 @@ /** -\page org_mitk_views_pharmacokinetics_mri DCE MR Perfusion Datafit View +\page org_mitk_views_pharmacokinetics_mri The 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 In dynamic contrast-enhanced (DCE) MRI, pharmacokinetic (PK) modeling can be used to quantify tissue physiology. Parameters describing the tissue microvasculature can be derived by fitting a pharmacokinetic model, e.g. a compartment model, to the dynamic data. This view offers a comprehensive set of tools to perform pharmacokinetic analysis. \section FIT_DCE_Contact Contact information 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. https://doi.org/10.1186/s12859-018-2588-1 (BMC Bioinformatics 2019 20:31) \section FIT_DCE_Data_and_ROI_Selection Time series and mask selection \imageMacro{dce_mri_maskAndFittingStrategy.png, "Time series and mask selection.", 10} 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 Pixel based and/or ROI based averaged fits of time-varying curves. The ROI based fitting option becomes enabled, if a mask is selected. \section 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 two/three segment linear model (2SL/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_modelSettings.png, "Model settings of the view for the standard Tofts model.", 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 compartment models that require the input of the concentration time curve in the tissue feeding artery, the arterial input function (AIF). In the DCE MR Perfusion Datafit View, 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 selected. This can be done by clicking on the AIF Mask selection widget and selecting a suitable AIF segmentation from the data loaded in the 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 using the corresponding Dedicated AIF image selection widget. An alternative option is to define the AIF via an external file by selecting Select AIF from 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 AIF values and corresponding timepoints (in tuple format (Time, Value)). Caution: the file must 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 as default to the literature value of 0.45. \subsection FIT_DCE_Settings_start Start parameter \imageMacro{dce_mri_start.png, "Example screenshot 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. To load a starting value image, change the Type from scalar to image. This can be done by double-clicking on the type cell. In the Value column, selection of a starting value image will be available. \subsection FIT_DCE_Settings_constraint Constraints settings \imageMacro{dce_mri_constraints.png, "Example screenshot for constraints 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 parameters are selected, their sum will be used) by selection in the drop down menu. The second column Type defines whether the constraint defines an upper or lower boundary. Value defines the actual constraint value, that should not be crossed, and Width allows for a certain tolerance width. \subsection FIT_DCE_Settings_concentration Signal to concentration conversion settings \imageMacro{dce_mri_concentration.png, "Example screenshot for concentration conversion settings.", 10} Most models require contrast agent concentration values as input rather than raw signal intensities (i.e. all compartment models). The DCE MR Perfusion DataFit View offers a variety of tools for the conversion from signal to concentration: by means of relative and absolute signal enhancement, via a T1-map calculated by the variable flip angle method, as well as a special conversion for turbo flash sequences. For the conversion methods, a baseline image prior to contrast agent arrival is required. In many data sets, multiple baseline images are available. The Baseline Range Selection allows for selection of a range of time frames, from which the average image (along the time dimension) is calculated and set as baseline input image. Remark: The number of the first time frame is 0. \section FIT_DCE_Fitting Executing a fit In order to distinguish results from different model fits to the data, a Fitting name can be defined. 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. In the following definitions, an evaluation describes the process of cost function calculation and evaluation by the optimizer for a given parameter set. - Stop condition: Reasons for the fit termination, i.e. criterion reached, maximum number of iterations,... - Optimization time: The overall time from fitting start to termination. - Number of iterations: The number of iterations from fitting start to termination. - Constraint penalty ratio: Ratio between evaluations that were penalized and all evaluations. 0.0 means no evaluation was penalized; 1.0 all evaluations were. Evaluations that hit the failure threshold count as penalized, too. - Constraint last failed parameter: Ratio between evaluations that were beyond the failure threshold. 0.0 means no evaluation was a failure (but some may be penalized). - Constraint failure ratio: Index of the first (in terms of index position) parameter, which failed the constraints in the last evaluation. 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 of 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.mri/plugin.xml b/Plugins/org.mitk.gui.qt.pharmacokinetics.mri/plugin.xml index bfb82f6dd2..84bf024784 100644 --- a/Plugins/org.mitk.gui.qt.pharmacokinetics.mri/plugin.xml +++ b/Plugins/org.mitk.gui.qt.pharmacokinetics.mri/plugin.xml @@ -1,12 +1,12 @@ - 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 57414c01dd..feb1aa49d5 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 Dynamic PET DataFit View +\page org_mitk_views_pharmacokinetics_pet The 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 German Cancer Research Center (DKFZ). 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_PET_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.pet/plugin.xml b/Plugins/org.mitk.gui.qt.pharmacokinetics.pet/plugin.xml index aaf2b2ee75..8ac3ef9396 100644 --- a/Plugins/org.mitk.gui.qt.pharmacokinetics.pet/plugin.xml +++ b/Plugins/org.mitk.gui.qt.pharmacokinetics.pet/plugin.xml @@ -1,12 +1,12 @@ - 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 2967567cfe..ee77922748 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 Photoacoustics Imageprocessing Plugin +\page org_mitk_views_photoacoustics_imageprocessing The 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. */