Overview of the MIDAS Processing Functions

Transcription

1 Overview of the MIDAS Processing Functions Last updated March 2016 Contents A.A. Maudsley University of Miami 1. Introduction 2 2. MIDAS Basics 2 3. The MIDAS Browser 5 4. Overview of the Processing 6 5. Starting MIDAS 2 6. The Importer Creating a New Project Importing Data Other Importer Functions Volumizing EPSI Preprocessing SI Reconstruction - FDFT RefDat Between-Series Registration - MSReg Brain Masks, Using Mask, AutoMask, and MRMask Lite Spectral Fitting - FITT Quality Maps - QMaps SI Signal Normalization (SINormT) Registration Segmentation Viewer SID SI Display PRANA Batch Miscellaneous Functions Single Voxel MRS Utilities MIDAS_Delete_Data_Nodes Copy Study data and Zip MINT Create spectrum by ROI integration SI Simulation SNR Calculation Spectrum review Other IDL Utilities 24 1

2 25. Additional information Acknowledgements Summary of Data Types and Sample Processing File Selection 26 References Introduction The MIDAS (Metabolic Imaging Data Analysis System) package provides an integrated set of data processing functions for MRSI and MRI processing. The complete processing for a typical MRSI acquisition involves many individual steps and processing options, which can at first appear quite confusing; however, once all processing options have been defined then they are easy to run in a fully-automated manner. Standard processing pipelines are provided on the MIDAS web site that enable new users up and running in no time. This document briefly introduces the individual processing functions, stepping through them in approximately the same order that would be used for a MRSI processing protocol. In separate documents are provided a Tutorial, which steps through a specific processing example for 2D MRSI, and detailed information for each of the applications. Once the functions of the individual processing steps are understood the user can then move on to developing a completely automated MRI and MRSI processing protocol. Note: You may see some differences in the widgets from those shown in this document. These applications are still under development and the appearance may also depend on the operating system. 2. Starting MIDAS The MIDAS installation automatically creates a shortcut on the Desktop that starts the MIDAS Toolbar, which is as shown here: This toolbar shows only the most frequently used programs and mainly users will use the Importer (1 st icon), the BATCH processing (2 nd icon) and the display and analysis programs (4 th to 8 th icons). Ocassionally individual processing functions may be run, which are made visible after selecting the MIDAS Functions icon (third from the left), which brings up the toolbar shown here: Almost all of the commonly used MIDAS functions are provided in the top-level toolbar and processing is usually run in a completely automate manner using predefined processing protocols, which is done with the BATCH program. As users get familiar with the processing they may wish 2

3 to change processing parameters, in which case the individual programs can be run using the second toolbar, which brings up a GUI interface for each program. These individual applications are described after Section 6 of this document. All applications can also be run from the IDL command line for users who want to develop their own scripts. In addition to providing data processing functions, the MIDAS system also provides a data management framework that manages the many data files and different types of data used, and keeps track of the all processing steps. This data management system means that it is not necessary for the user to interact with individual files or even know where they are located; however, some understanding of the organization is useful, which we review in the next section. 3. MIDAS Organization Basics The MIDAS data management system is based upon a hierarchical organization, similar to that used in the DICOM format. The top level is a Project, into which all MRI and MRSI data for multiple Subjects are Imported. When the MIDAS software is used for the first time it will be necessary to create a new project. A Project will typically comprise of all data acquired under a specific aim, e.g. Epilepsy or Normals, but the organization is up to the user. Each user has their own set of projects, which is kept in a XML file located under the user s home directory, e.g. C:\Users\UserName\MIDAS\projects.xml. That file points to one or more project description files (named xxx_project.xml, where xxx is the name of the project, e.g. Normals_Project.xml ) that contain the list of subjects in that project. To organize the data MIDAS uses a simple database that consists of a set of XML files, which the user interacts with through a MIDAS Browser. This data management system means that the user doesn t have to keep track of data file locations. The most useful XML file is always called Subject.xml, which contains all the information for all studies associated with a single Subject. All data for one Subject is contained under a unique directory name, and the Subject.xml file is located in that directory. Note, that a subject does not necessarily mean a person, but could also mean one or more studies using a phantom. Under each Subject, the data is organized in a hierarchical manner, using the primary headings of Study, Series, and DataSet. There can be several Studies, e.g. data taken on the same subject but taken at different times, and multiple image Series under each Study, such as MRI and MRSI. There is also a Process category, or node, which indicates any new data that was created as a result of a processing procedure. A Process node may can be placed at almost any level in the hierarchy, depending on whether it resulted from an operation on one data set (i.e. a single-series process), or involved multiple datasets (e.g. using data from multiple Series under one Study, or perhaps from multiple subject data). In addition, there are finer levels of organization that we need not be concerned with at this stage. After data is imported and processed, if you look at the directory structure it will look something like this: 3

4 This figure illustrates that there are several Subjects listed under a single Project, which in this case is named Normals_15T, and that each subject has its own directory (MN011, MN012, etc). Within each of these subject directory is a subject.xml file and several subdirectories with different data types in (mri, mrsi, etc.). It is recommended that project names be descriptive and subject names be short such that the full name can be seen in the MIDAS browser. Each Project is associated with directories containing Processing Files, an Atlas, and a Reference. Although the MIDAS processing can be run one process at a time with all processing parameters being set up manually, this would be very cumbersome and the usual method is to first develop a set of processing, or Proc, files, that contain the parameters that define the processing steps done for each application, and then run the complete processing pipeline using the BATCH program. The format of the processing files is also XML, and although manual editing of these files is possible this is not recommended, and instead all processing settings should be set from the individual applications. It is possible for a project to have a unique set of processing files (local path), or to use a common set of files that may be shared between multiple projects. If you are using MIDAS for the first time, or are creating a new project, you will either have to copy the processing files into a local ProcessingFiles directory, or define the path to an existing directory at the time of creating the project. Example processing files come with the MIDAS distribution, but these must frequently must be tailored to the needs of each project. The Atlas is used for spatial registration to a common reference frame and consists of a reference MRI (of the brain) and associated set of region labels. The reference MRI has been manually labeled to identify all voxels located in several anatomical regions. One of the last operations in the processing pipeline can be to warp the metabolite images into the Atlas space, so that analyses can be done by brain region. Methods are also available to map an atlas back into the acquired subject space. The Reference directory contains data that can be used in the data analysis. This is a set of files with the normative values for all metabolite and metabolite ratios, for different age groups. 4

5 4. The MIDAS Browser Under MIDAS, you do not need to be concerned with data organization and never need to deal with data filenames directly. Instead, all applications use the MIDAS Browser to navigate through the project and subject selections. An example screen shot for the MIDAS Browser is shown here: In this example the Normals_15T project has again been selected and the list of subject data is displayed along with some additional information on the selected Study and Series. To guide the user in navigating through the many data types, Labels are used to indicate the type of data in each section, such as MRI_T1, and SI. The metabolite MRSI data is always given the SI label, and in this example that data node has been selected (highlighted) and the individual sub-nodes are displayed, which reflect the organization of the information within the subject.xml file. The sections indicate the unprocessed (Raw) and Processed (Spectral) MRSI data that contain the additional time or spectral dimension, and any processed images (Maps), which in this case are at the SI spatial resolution. When selecting data, some programs are very specific on which level in the browser hierarchy the selection must be made, although in many cases the program will automatically find the default data type. For example, to do the FT of the SI data it would normally be necessary to select the Raw data node in the tree shown in the above figure, but if selection is made at the SI node level the program will assume you want to reprocess the raw data. However, it would not be possible to make the selection at the subject level, since the program does not know if you want to process the metabolite SI data or the water reference SI, which is labeled as SI_Ref. The MIDAS Browser also provides several useful data management features, which are illustrated in the following figure: 5

6 Here the bottom section has been enabled to display information at the selected node in this case the SI Series level. In the center of the screen is a list of options that become available by using a Right Click on a node. Available options include deleting a Subject, Series, or Data node, depending on the node level that was selected. 5. Overview of the Processing The following diagram outlines the order of processing for a volumetric MRSI data set that was acquired using the EPSI sequence. The acquisition also obtains a water-reference SI dataset, labeled SI_Ref, which is used to provide several functions in the processing of the SI data. 6

7 Raw Data Import Water-SI SI Volumize Volumize EPSI3D EPSI3D FDFT (Spatial) RefDat (B0 or ECC Map) FDFT (Spatial + spectral) FDFT (Spatial+Spectral) Water image AutoMask Registration (MRI-to-SI) Lipid mask Brain mask LITE FITT Metabolite images QMAPS SINorm Prior spectral information MRISeg (segmentation) Registration (Spatial normalization) Transformation parameters Analysis or Database In this document is given a general introduction to each of these processing functions, and then a more hands-on description is provided in the tutorial. More detailed descriptions can be found in the help files for each application. 6. The Importer This module manages projects and brings new data into the MIDAS environment. When new data is Imported into MIDAS, this involves copying the raw SI and processed MRI data to a designated location and creating a new Subject.xml file, which maintains all relevant subject and study parameters. Click the MIDAS button to start this program: Creating a New Project Functions in the Importer allow creating or deleting projects, or modifying paths to files associated with a Project. As an example, let s create a new project, which is done via the File New Project option, and the following widget appears: 7

8 Here it can be seen that the project name (e.g. MIDAS-Data ), and the PI must be defined, and the path to the top-level directory under which the project will be created, e.g. N:\. The Atlas Path, Processing Files Path, and Reference Path will be set by default to be under the project path, but can be changed, for example to use the same processing file directory that is used for multiple projects. If you have multiple projects using the same acquisition and processing protocol it is recommended to use a common location to the ProcessingFiles directory to simplify maintenance of these files. After creating a new project the screen should look as: 6.2. Importing Data The next step and major function of the Importer is to Import the raw SI and MRI data. When a directory that contains the raw DICOM files is selected, the program will scan the files and a tree listing the Subject/Study/Series information will appear in the Data Browser section. Normally all acquired data will be imported, so selection can be made on the Subject level, i.e. 8

9 then select Import Files. However, there are many cases when several MRI series have been copied off the scanner, but are not needed for MIDAS processing, for example the initial Scout image series. In this case it is better to select only those image series that are needed. The importing/copying can take several minutes for volumetric MRSI data. When the data import is complete, the widget shown below appears and some additional information must be added to the MIDAS database by the operator. An essential requirement here is to assign a Series Label to each image series. The correct assignment of the Series Label is very important, as several processing functions use these to find the appropriate data. Some Series require that only one data set have a given label, notably the MRI segmentation program will always look for a T1-weighted data set, so there would be an ambiguity if two T1 MRIs were imported and both had the MRI_T1 label. Several other labels can be chosen, e.g. MRI_FLAIR, MRI_T2 etc., and the user can also enter in their own unique labels. Multi-echo MRI is imported as a single Series since all images have the same DICOM Series_ID, and can be given a single label only. One example of this is for a short- and long-echo acquisition, and the convention here is to give it the Series label MRI_T2, and not, for example, MRI_PD (proton density). However, the MRI_PD label is available from the pull-down menu for the case where you have only a single MRI that is proton-density weighted. 9

10 For the volumetric EPSI acquisition there are two datasets, which get imported as SI and SI_Ref. These can be distinguished by the Study ID (a and b) and TE, i.e. TE=0 must be labeled SI_Ref and TE=70 (or other value used) must have the SI label. It is possible to import multiple SI datasets obtained in one study, for example using two different TE values, but these must have unique labels. The convention is that the first pair (metabolite and water SI) is labeled SI/SI_Ref, and the second will be SI2/SI2_Ref, etc. For batch processing of multiple SI Series the labels shown in the BATCH pipelines need to be modified accordingly. Notes: The Importer is known to work with the proprietary data formats from GE, and Philips for their respective software versions in use in Support for Siemens using DICOM covers all current software versions. It is also possible to set up an AutoImport service that will do the import, Series label assignments, and start the processing automatically. This requires further effort to get set up Other Importer Functions The Importer also contains several utility functions, for example for deidentifying DICOM files, changing Subject_IDs, or importing data in Analyze format. An example uses of this last function is to import segmentation or DTI images that have been created using other programs. There is an important limitation with these files, however, because the Analyze/Nifti format doesn t have the full study information. It is therefore necessary to have an existing Series Volume node that was created from a DICOM file and therefore contains complete details of the acquisition. See the Importer documentation for details. 7. Volumizing For volumetric image data, MIDAS requires that the one-file-per-slice format used by DICOM be ordered in a single volume. Therefore, after the individual-slice DICOM format MRI data has been imported the next step is to create a single data volume from the multiple-file data. Additional functions include converting MRI data into a common orientation and data format, and organizing data into appropriate subdirectories. The program is started from the Vol button. It is only necessary to select the Series to be processed, e.g. the MRI_T1 node, and the Volumize button starts the process. You will have to repeat this for all your Series, e.g. MRI_T1, MRI_T2, SI, and SI_Ref. The raw (DICOM) MRI data is retained in subdirectory raw, while the volumized MRSI and MRI data are placed in their corresponding new directories. When viewed with the MIDAS browser, the volume MRI data will be listed under a Volume label while the original multi-slice will be listed as Original. For the SI data, the label is Raw, e.g.: 10

11 For the SI data, the default action is to delete the raw data to save space. If you want to keep the raw data, e.g. for testing, then the Volumizer must be run from the widget and the option to delete the raw data unselected. 8. EPSI Preprocessing The EPSI2 process takes the raw EPSI data, of 2 or 3 spatial dimensions, and applies a regridding procedure to produce rectilinear sampled k-space data. In the 3D-EPSI data acquisition a sinusoidal (or trapezoidal) function is used at the start and end of each readout gradient during the data acquisition, with the result that the k-space data is not uniformly sampled in the k x -time dimension (for x readout direction). Therefore, this 3D-EPSI preprocessing step is used to resample the k- space data on the uniform grid. The program is started from the button, and the only allowed data selections are the SI or SI_Ref data node. After selecting a processing file, or setting the options, click Start EPSI Reconstruction to do the processing. The k-space data resampling is dependent on the k-space trajectory and the standard EPSI k-space trajectory file is provided in the Midas installation. This program also allows correction for a frequency drift during the data acquisition, which has been observed on several MR systems. The program can estimate this drift value, but it is also recommended that you independently check this on your instrument, i.e. by checking the resonance frequency before and after the EPSI acquisition. The frequency drift correction assumes a linear frequency drift, which has been found to be sufficient and is easy to implement. Lineshape distortion caused by the residual uncorrected frequency drift correction can also be (partially) corrected for using the ECC correction option in the FT 11

12 processing, with the correction functions derived from the water-reference SI data using the RefDat program. Running the EPSI process gives us an excuse to take another look at some of the features of the MIDAS Browser. In the following figure the browser Depth has been set to the lowest (most detailed) level and the tree has been opened up to illustrate that the programs have maintained a record of the processing that has been done so far (Volumizer and EPSI2). You can obtain additional information such as the filename, and view some parameters from the Show Details button. In this example we can see that the frequency drift estimation was enabled and that the program estimated this to be 4.25 Hz. 9. SI Reconstruction - FDFT FDFT is the utility to do Fourier Transform reconstruction of MRSI k-space data. Click the button to launch the GUI, which is shown here: Click Browse in the Data File section to open the Browser and select the data to be processed, e.g. the SI or SI_Ref data. The FDFT program is our first example of an application that uses a Processing File. On clicking Browse, the program will 12

13 start with file selection at the default path defined in the Project.xml file, which was defined when the project was created (Section 6.1 above), but if this is not defined the program will look for the local \ProcessingFiles subdirectory in the project of the selected data. There is no convention for the names of the Proc files, but generally these aim to be descriptive and start with the name of the processing function, i.e. FDFT_ in this case. Examples of these files are given in the Tutorial. When the processing parameters are read in from a file the processing can be started immediately; however, you may want to check or change settings. For this, the Manual Settings button brings up a widget that lists all settings: Detailed information on the options available is given in the FDFT_help file. Here, a brief overview is given only. In the general section, it lists the Input/Out data format, the data dimension, the FFT options that you want to apply, reference image type, transpose option, B0 correction, and spatial filter. If you need to do B0 correction on your SI data, you need to have already generated the B0 Map file. This is typically done using the water reference SI data and the RefDat function. In the spectral parameters, it specifies the parameters that related the spectral information, such as input size, FFT processing size, spectral apodization, etc. In the H2O subtraction section, it specifies the water filter processing parameters in the case that you need to apply water filter to suppress the residual water signal. 13

14 In the X,Y,Z parameters, it specifies the parameters that related the spatial information, such as data spatial size, FFT processing size, spatial apodization. From the Manual Setup button you can view the spectrum that corresponds to the zero-phase encoding measurement, i.e. coming from the whole object. This is typically very distorted, but allows for example, you to check the frequency scale direction is correct. Once you finish all the preparation, click Accept and Do FFT to do the FT processing on your data. The result of the FT is placed in a Spectral data node. Notes: If you are following an example processing session (e.g. the Overview on page 3) you may have to repeat the FDFT process depending on whether you are creating the B0 map (using RefDat), and whether you have SI and SI_Ref data. The typical processing steps can be summarized as: FDFT SI_Ref/raw, spatial FT only RefDat on SI_Ref/spectral to generate the B0 map FDFT SI_Ref/raw, spatial and spectral FT, with B0 correction FDFT SI/raw, spatial and spectral FT, with B0 correction FDFT includes options to flip the spectrum and take the complex conjugate, which govern the direction of the frequency scale and the spectral phase. These must be consistent for B0 correction and spectral fitting to be applied correctly. On setting these parameters, confirm that you get this appearance of the spectrum shown here, i.e. this has lipid on the Right and note the form of the imaginary part of the data (red). This display is from the SID program, with the Options/Datatype=Complex set, but you can usually also check this in FDFT from the Manual Setup function. If the data has already been processed, the program will assume you want to reprocess the RAW data and the previously processed data and the entries maintained under the Spectral and Maps data nodes, will be deleted. However, this clean up may be incomplete, and the user needs to keep track of what processing has been done. For example, if processing after FT has been done (RefDat, Mask) the FDFT program will not delete the data frames created by these other programs. In this case the user will have to take care of the other cleanup. A utility is available to do a more comprehensive clean up of old files and processing, which is described later. FDFT supports reduced k-space acquisition using a phase sensitive GRAPPA multichannel combination. The program automatically detects if the reduced k-space sequence has been used. 14

15 10. RefDat RefDat is a tool that generates correction functions from a water reference MRSI (or a metabolite SI data set that still has significant residual water signal). Commonly, the B0 and the phase correction maps are created, though options are also available for ECC and full lineshape deconvolution. The input data must be Fourier transformed in the spatial dimension only, i.e. space-time data, and almost always it should be using the water-reference SI. Select the Ref icon to get the main widget: A common feature of most of the processing modules is that is a specific data selection is not made then the default data type will be used. In this case the Data File selection can be made at the Subject level and the program will automatically refine this to take the SI_Ref data. On the other hand, it would be possible to select the SI data node and the program would process that data (with unpredictable results!). A processing file can be selected, though the default values are frequently adequate if only a B0 map is required and a processing file is not necessary. The result of this program are stored in a MAPS node (typically under SI_Ref), e.g. using the B0_Map label and Phase_Map, if produced. If ECC or lineshape correction functions are obtained they can be viewed using the SID program. When testing parameter settings it can be useful to look at the result without saving the data, which is done by checking the Display result only checkbox before clicking Start. This causes the B0, phase, and amplitude maps to be displayed (see figure). 15

16 11. Between-Series Registration - MSReg This program applies inter-series image registrations, using the T1 MRI as the spatial reference. For a single study, it will ensure that the spatial position vectors are correctly set so that the MRSI data is aligned with the MRI_T1, but additionally it applies a spatial shift to the MRSI data such that the image occupies the same relative position within the FOV. The reason for this latter function is that the MRSI acquisition does not allow complete flexibility in the placement of the FOV, and the center of the acquired image is always the magnet isocenter. For some MR systems, the head is positioned slightly above the magnet isocenter, so the resultant metabolite image is shifted towards the top of the FOV. 12. Brain Masks A couple of steps in the typical processing pipeline make use of simple binary images that cover either the brain or the subcutaneous lipid region, and which are assigned the labels mask_brain and mask_lipid respectively. The brain mask is used to identify which voxels should be selected for spectral fitting, and the lipid mask is used for lipid k-space extrapolation, which reduces Gibbs ringing when the MRSI acquisition covers the whole brain. For the volumetric EPSI acquisition the MRMask program is used for this purpose, which gets the information from the T1 MRI and tissue segmentation data. There are two other programs, Mask and Automask, which derive the masks from the SI_Ref (water SI) data and can be used if the MRI is not available. The Mask program provides manual editing tools, but it has now largely been replaced by the AutoMask program. Manual editing is sometime useful, so the program has been retained. See the help files for those programs for more information. The MRMask program, launched from the icon, uses the T1-weighted MRI and the tissue segmentation maps, which are derived from the T1 MRI. The GUI for this is shown to the right: One additional feature of this application is that voxels can be excluded from the brain mask based on the T2* of the water SI data. This helps speed up the spectral fitting by not spending time on poor-quality regions. The T2* map is created in the RefDat program. The figure below shows an example of the MRMask output: 16

17 13. Lite Lite performs selective extrapolation of k-space data for lipid signals in 1H MR Spectroscopic Imaging data. The extrapolation method uses the Papoulis Gerchberg algorithm (Haupt, Schuff et al. 1996). This program makes use of the known distributions of subcutaneous lipid signals to limit Gibbs ringing that occurs at the edges of these strong signals when limited k-space sampling is used. Click the icon here: to start this program, the widget is shown When this processing is included, it is essential that spatial smoothing was not applied in the FDFT program. To ensure this, all parameters for LITE are set from the FDFT parameter setup and there should be nothing to do here. Just click Start LITE. Approximately 40 iterations are considered the minimum, though the potential improvement after that is small and highly dependent on individual data quality. For volumetric SI data there is a tradeoff between speed and memory usage and quality if the extrapolation is done slice-by-slice or as a true volume. See the help file for details. 14. Spectral Fitting - FITT This program does the parametric spectral fitting. It is started from the FITT icon. After selection of the data (which defaults to the SI node) the program will prompt for the Proc file selection. Normally, you only need to run the fitting (click Do Fit ), but options are available for setting parameters, viewing fit results, and fitting individual spectra. Setting up the parameters requires a good understanding of the program and spectral fitting. See the help file for details. The FITT program uses prior knowledge of the spectrum, generated by computer simulation (Young, Govindaraju et al. 1998; Soher, Young et al. 2007), in a parametric optimization procedure. Using an iterative time-frequency approach, the baseline is characterized using a wavelet model (Soher, Young et al. 1998; Young, Soher et al. 1998). Some of the features of the program include use of the brain mask described in the last section to define which voxels to fit, and the ability to incorporate constraints based on spatial characteristics as well as spectral constraints. The program can apply the B0 and phase correction that was determined in the fitting to the spectral data, which 17

18 simplifies visualization of the spectra, and can calculate Cramer-Rao bounds and confidence limits, which can be used in the final analysis steps. The FITT2 program makes use of multicore processing; however, if using the free Virtual Machine IDL license then only single core is available in IDL. By using computer simulated basis functions the program can easily be adapted to support any field strength, compound, or pulse sequence. The GAVA program was original developed for this purpose (Soher et al. J Magn Reson, 2007), but this has now been replaced by the VESPA program ( 15. Quality Maps - QMaps This program performs a simple quality evaluation to form a Quality Map that can aid visual evaluation of the image data, i.e. by viewing the Quality image alongside the metabolite images. This image can also be used to exclude voxels when running statistical analysis tests; however, the user should also consider adding more specific criteria that are not included in this function. Started from the Qmaps icon, appears:, the widget shown to the right Like many of the programs this can be run with the Display only option set and the user can evaluate the effect of different parameter settings. When these are finalized they can be saved to a processing file for use in the automated batch processing mode. The result of this program is stored in the SI/MAPS node and is labeled Quality_Map. 16. SI Signal Normalization (SINormT) This program scales the metabolite images generated by FITT into a normalized intensity scale, thereby enabling comparison of results between subjects and between multiple studies in the same subject. In addition, the images are corrected for any bias field. This program applies a scaling based on the tissue water signal, and there are 3 options for where this signal is derived from: a) a proton-density weighted MRI, b) a water-density measurement under a MRI_FA1 node (see the TIMO program), or c) from the SI_Ref water reference image. In each case it is necessary to have run the MRI tissue segmentation before running this program. The program is started from the icon: The program processes metabolite images in the SI/Maps node (i.e. the results after spectral fitting), as well as the water-reference image in the SI_Ref node. The program includes options for creating ratio images, e.g. NAA/Cre, and for removing outliers from the images. The output of this program is another copy of the metabolite images (in the same SI/MAPS node), but these are renamed to 18

19 distinguish the uncalibrated images (e.g. NAA_Area) from the intensity-normalized images (e.g. NAcetylAspartate). Note: The first version of this program used the proton-density MRI for signal normalization (Maudsley, Darkazanli et al. 2006); however, the method that uses the water-reference SI (Maudsley and Domenig 2008; Maudsley, Domenig et al. 2009; Maudsley, Domenig et al. 2010) has been found to be more convenient and more robust. The M0 mapping method is expected to be the best, but is still under evaluation. 17. Registration This program performs several spatial transformation functions with the final aim of warping individual subject metabolite images into a common reference frame. Firstly, the affine transformation between the metabolite images and the subject T1 MRI is calculated using the waterreference SI (SI_Ref / Reference data). Since this image has high SNR and shows sufficient anatomic structure it provides a good SI-resolution data set for registration with the MRI. Secondly, the non-linear transformation between the T1 MRI and the reference MRI is obtained. Then finally the combined transformation from the metabolite image to the reference space is performed (Rousseau, Maudsley et al. 2005). The standard reference MRI is the MNI data (Montreal Neurological Institute), which is associated with a lobar-scale brain atlas; however, other brain atlases are available (currently not included in the standard distribution) and different reference MRIs can be used. The program is started from the icon: The program automatically steps through the several processing steps needed, and the main choice of the user is to define the target resolution, which is the spatial resolution of the final metabolite images. The default value is 2 mm. 18. Segmentation This is for tissue segmentation using MRI data. It is started from the icon:. The program can use single contrast, typically the MRI_T1, or multi-contrast, e.g. including the MRI_T2 data. The bias-field correction option is generally not needed for 1.5T MRI data obtained with standard headcoils, but is needed at 3 T or with phased-array coils. The segmentation program includes three classification algorithms, but the FSL/FAST v4.1 ( option is the only one that is used. 19

20 The contributions of the FSL developers are gratefully acknowledged. Another segmentation method is described by Hore et al. (Hore, Hall et al. 2009). As of 2016, a second version of the segmentation is available, and is started from the icon. This also uses the FSL/FAST program, but offers some additional options, including an iterative BET application, which was found to help with sagittal-acquired T1 MRI data, and using a FLAIR image for the brain extraction. 19. Viewer The MIDAS Viewer program is a Java application that provides multiparametric image display. It is started from the toolbar icon:, or from a desktop icon that is created during installation: This program provides a comprehensive display for all images together with voxel selection for spectral display. The program can set up to load predefined layouts showing multiple image types and enables quick and simple review of all the data. There are many display and analysis options available, as well as integration with Importer and Processing functions, and the user is directed to the VIEWER_Help document for details. The MIDAS Viewer also enables display of images located on a remote server. Sample images are available on the project web site. This can be reached by going to the File/Server menu option and selecting: mrir.med.miami.edu:8080. You will need to obtain the login. 20

21 20. SID SI Display The SID program also provides a display for MR Spectroscopic Imaging data and corresponding MRI data. This program provides a more complex interface than the Viewer along with some different functionality. The program is started from the SID icon: The MIDAS Browser starts up when the program is started, and the simplest approach is to make a selection at the Subject or Study level, which causes the program to load the default images and display the SI and MRI_T1 data, if available. Selection can also be made at any lower level, making the program load just the selected data. The program offers many display functions. See the SID_help.pdf for details on this program. 21. PRANA This program is started from the toolbar icon, and provides many functions for reviewing multiple datasets within a project, or multiple projects, and several image analysis functions. Studies can be selected depending on several criteria, and image display and analyses can be done for data in in subject space, e.g. for comparisons between multiple studies in the same subject, or in standard space. Analyses in standard space include options that make use of the brain atlas. Functions include: - Display of selected images from multiple studies within a project. - Calculation of mean values by brain region, including correction for CSF volume contribution and separate values for grey- and white-matter. - Calculation of mean value images, and standard deviations, across a group of subjects in standard space. - Voxel-based t-test between two subject groups. 21

22 - Calculation of z-score images of individual subjects, including correction for multiple comparisons, to indicate significant differences relative to control values. - Review of parameter values from all subjects. This program has many features, and the reader is directed to the help document for more information. 22. Batch Once you have defined all processing settings and created all Proc files for your processing protocol then all steps can run in a fully automated manner using the Batch program. This is frequently the main program the typical user sees until all processing is completed The BATCH program is started from the icon: example processing pipeline is shown here:, and an As shown in the figure, multiple studies can be queued up (upper part of the widget) and processed using multiple processing steps (lower part). The processing pipeline can be set up using a simple editor. The reader is directed to the BATCH_help documentation for additional information on this program. It is also possible for the researcher to develop IDL procedures, or other forms of command-line driven scripts, that call the individual processing functions, in a similar manner to that provided by the BATCH program. 22

23 23. Miscellaneous Functions IMCALC This is an Image Calculator program. By typing simple IDL command-line instructions many types of operation can be applied to any image or parameter map. Applications include applying image threshold operations or regressions between two parameter maps. It also includes an image blend option QUID This is a display program that allows a mosaic format display of multi-slice data and is useful for preparing slides, e.g Single Voxel MRS Selecting this icon brings up another toolbar that includes some functions for single voxel MRS. Please look at the various SVS documents for further description. This uses a single voxel spectral analysis function VESPA developed by Dr. Brian Soher Utilities The Tools icon on the MIDAS toolbar,, provides a way for users to customize the available functions. Information on implementing new tools is provided in the MIDASTools_help.pdf document. The Tools section includes the following programs: MIDAS_Delete_Data_Nodes This is a utility to restore a study to its (almost) raw data stage or remove specific data and nodes in the XML descriptor. For several processing steps, if they are repeated then the previously processed result is automatically deleted; however, this is not always done, nor possible to do given the considerable flexibility of the system. Therefore, this program provides a means of restarting a processing protocol from a raw set of data. SI data is restored back to the result after EPSI3D (for volumetric data) and MRI is restored back to the RAW data. A separate help file is available from this application widget Copy Study data and Zip This utility provides a method for copying and/or compressing selected files from one or more subjects. It provides a convenient method for archiving the raw data files in a zip file, or for packing all final images to be sent to another user. 23

24 MINT ROI spectral integration This creates a spectrum by integrating over a region of interest, which may be from a user-defined mask (e.g. a tumor) or an atlas-defined region, and can optionally run the spectral fitting on this spectrum. Since integration is done after B0 correction, and data are acquired with a small nominal spatial resolution, the integrated spectra retain excellent linewidth, and with the increase in SNR this enables greater accuracy in the spectral analysis SI Simulation This is a program that creates simulated SI data and a corresponding MRI. This can be useful for evaluating new processing tools. A description is provided in MakeSIData_help.pdf SNR Calculation This program creates images of the SNR, from either the peak height or the fitted peak areas. This can also be added to a batch processing pipeline Spectrum review This provides a way of looking at single spectra that may be saved in the SID program or the MINT utility. If the option in FITT is enabled to save the spectral fitting result, then this can be displayed as an overlay on the spectral data plot. 24. Other IDL Utilities There are also several small utility programs that are not specifically listed on the toolbar, but can be run from IDL command line. These can be found in the *:\Midas\Bin\utilities directory, or in the *\idl_progs\utilities directory of the source code SVN distribution. These may also be useful as starting points for researchers to modify for their own requirements. E.g. analyzeflipper Utilities for changing the spatial orientation of Analyze format images. This may be needed when moving images between MIDAS (which uses Dicom convention) and Analyze format. FakeSegMask Create fake segmentation image from a phantom object. This is used when processing data from phantoms. ViewRawData IDL command line utility to read in and view.vol and.epsi raw data. Shrink_Image_inXYZ Used to create lower resolution atlas files from 1 mm version. 24

25 25. Additional information Documentation is distributed with the MIDAS installation and is located under the default directory, :\Midas\Documents. They are also on the web site: Part 2 of this document is \Educational\MIDAS Tutorial.pdf, which goes through a specific processing example. Additional tutorial information that is more specific for the volumetric EPSI processing is in \Documents\Educational\EPSI_Processing_Introduction. All applications have a Help file with detailed user information, under Documents\Help_files. In most cases these can be displayed from an option on the main widget of each program, A detailed description of the XML data management system and data organization is available in the document MIDAS Project Description. Some information for developers can be found under the Documents/Development directory. New users should also look at the past newsletters for some tips: Acknowledgements The MIDAS project was originally funded under a NIH Bioengineering Research Partnership grant, R01EB00822 and maintained under R01EB The package gratefully makes use of the following external open-source packages and libraries: FSL: ImageJ: SAX: JDOM: XERCES: 7Zip: The spectral fitting program uses basis functions simulated using the VESPA program developed by Brian Soher: which in turn makes use of the GAMMA library (Smith, Levante et al. 1994). 25

26 27. Summary of Data Types and Sample Processing File Selection Process Selected Input Typical Processing file name Output Node/Data Action Node/Data Type (Note, names are defined by user) Type Importer any n/a Raw Adds subject to a Project and creates the Subject.xml file Volumizer SI/Original SI_Ref/Original n/a Volume (MRI) Raw (SI) Creates single data volume from multiplane data MRI/Original EPSI2 SI_Ref / Raw EPSI2.xml Raw Resampling of EPSI data SI / Raw (Overwrites data) FDFT SI_Ref / Raw FDFT_SI_Ref_spatial.xml Spectral Spatial FT RefDat SI_Ref / Spectral REFDAT_SI_Ref.xml Maps B0 and Phase maps (Input for 4DFT) FDFT SI_Ref / Raw FDFT_SI_Ref_spatial_spectral.xml Spectral Spatial & Spectral FT FDFT SI / Raw FDFT_SI_spatial_spectral.xml Spectral Spatial & Spectral FT Mask SI_Ref / Spectral n/a Maps Brain mask (input to FITT) Mask SI/ Spectral n/a Maps Lipid mask (input for LITE) AutoMask SI_Ref/Spectral Mask_SI_Ref.xml Maps Brain and Lipid masks MRMask MRI_T1 / Volume MRMask.xml Maps Brain and Lipid masks Lite SI/ Spectral n/a Spectral Lipid Extrapolation (Overwrites data) FITT SI/ Spectral FITT_SI_TE70.xml Maps Spectral fitting (Optional Spectral) QMaps SI/ Spectral QMAPS.xml Maps Map indicating SI Quality SID any n/a n/a Display only SINormT SI/Maps & SINormT_3T.xml Maps Signal normalized metabolite Segmentation maps. Registration MRI and SI n/a Maps Non-linear registration to MNI. 26

27 References Haupt, C. I., N. Schuff, et al. (1996). "Removal of lipid artifacts in 1H spectroscopic imaging by data extrapolation." Magn Reson Med 35(5): Hore, P., L. O. Hall, et al. (2009). "A scalable framework for segmenting magnetic resonance images." J Sign Process Syst 54: Maudsley, A. A., A. Darkazanli, et al. (2006). "Comprehensive processing, display and analysis for in vivo MR spectroscopic imaging." NMR Biomed. 19(4): Maudsley, A. A. and C. Domenig (2008). Signal normalization for MR spectroscopic imaging using an interleaved water-reference. International Society for Magnetic Resonance in Medicine, Toronto. Maudsley, A. A., C. Domenig, et al. (2009). "Mapping of brain metabolite distributions by volumetric proton MR spectroscopic imaging (MRSI)." Magn. Reson. Med. 61(3): Maudsley, A. A., C. Domenig, et al. (2010). "Reproducibility of serial whole-brain MR spectroscopic imaging." NMR Biomed. 23: Rousseau, F., A. A. Maudsley, et al. (2005). Evaluation of sub-voxel registration accuracy between MRI and 3D MR spectroscopy of the brain. Proc Soc Photo Opt Instrum Eng San Diego. Smith, S. A., T. O. Levante, et al. (1994). "Computer simulations in magnetic resonance. An objectoriented programming approach." J. Magn. Reson. A106: Soher, B. J., K. Young, et al. (2007). "GAVA: spectral simulation for in vivo MRS applications." J Magn Reson 185(2): Soher, B. J., K. Young, et al. (1998). "Automated spectral analysis III: application to in vivo proton MR spectroscopy and spectroscopic imaging." Magn. Reson. Med. 40(6): Young, K., V. Govindaraju, et al. (1998). "Automated spectral analysis I: Formation of a priori information by spectral simulation." Magn. Reson. Med. 40: Young, K., B. J. Soher, et al. (1998). "Automated spectral analysis II: Application of wavelet shrinkage for characterization of non-parameterized signals." Magn. Reson. Med. 40: