decision support system - interpret 3 -...

© 2012 GABRMN - Grup d'Aplicacions Biomèdiques de la Ressonància Magnètica Nuclear

Decision Support System - INTERPRET 3.1

HelpAndManual_unregistered_evaluation_copy

Decision Support System - INTERPRET 3.12


Table of Contents

1 DSS INTERPRET 3.1 3

................................................................................................................................... 41.1 Introduction

................................................................................................................................... 71.2 Overview Panel

......................................................................................................................................................... 8Using the Overview

......................................................................................................................................................... 10Classifier Plots

......................................................................................................................................................... 18Manual Overview

................................................................................................................................... 181.3 Options Panel

................................................................................................................................... 201.4 Data Inspection Panel

......................................................................................................................................................... 22MRI Viewer

......................................................................................................................................................... 23Clinical Record Viewer

......................................................................................................................................................... 24Case Notes Viewer

................................................................................................................................... 251.5 Test Cases

................................................................................................................................... 251.6 New Cases

......................................................................................................................................................... 26Load New Cases

.................................................................................................................................................. 28Siemens scanners

.................................................................................................................................................. 28GE scanners

.................................................................................................................................................. 29Philips scanners

.................................................................................................................................................. 29jMRUI and jDMS

......................................................................................................................................................... 30Decision Support

......................................................................................................................................................... 31Save Loaded Cases

......................................................................................................................................................... 35Edit User Cases

................................................................................................................................... 361.7 Reference Material

................................................................................................................................... 391.8 Disclaimer

3DSS INTERPRET 3.1


1 DSS INTERPRET 3.1

This is the help and tutorial for the Decision Support System INTERPRET 3.1.This is the evolution of the tool generated by the European project INTERPRET (Tate et al. NMR Biomed. 2006 Jun;19(4):411-434). In this helpyou will find the information about the general use of this system.

For more information related to the INTERPRET project go to:

http://gabrmn.uab.es and select the "INTERPRET" option.

For information about data processing, Pattern Recognition techniques andproducing compatible data contact: <[email protected]> or <[email protected]>

4 Decision Support System - INTERPRET 3.1


1.1 Introduction

The INTERPRET GUI provides you with an easy access to an embeddeddatabase of MR spectra, images (obtained at 1.5T) and clinical informationfrom 304 patients originating from the INTERPRET project and itscontributors. Furthermore, data from other institutions like IDI- Bellvitge (70patients) has been incorporated to version 3.0. The system is designed toallow the display of classification plots that are useful for evaluating andinterpreting the SV MRS data.

The INTERPRET database is composed of a set of cases which have beenvalidated by several expert committees, from clinicians to expertspectroscopists. For more information see Julià-Sapé, et al., MAGMA. 2006Feb;19(1):22-33.

Cases that belong to the IDI-Bellvitge dataset contain a basic set of clinicalinformation, directly available from the patient's clinical record.

All cases are anonymised, which means that they have been dissociatedfrom any personal or institutional code, therefore preserving the personalinformation of each consenting patient.

Currently eight classification plots are provided. The first two were alsoavailable in version 2.0, although we have retrained them for version 3.0 withthe help of an independent test set.1. Most common tumour types, short TE.2. Most common tumour types, long TE.3. Most common tumour types, short and long TE.4. Tumour vs. pseudotumoural disease, short TE.5. Tumour vs. pseudotumoural disease, long TE.6. Tumour vs. pseudotumoural disease, short and long TE.7. Tumour vs. pseudotumoural disease with metabolite ratios.8. Glioblastoma vs Metastasis (Vellido, NMR Biomed, 2012).

The classifiers presented in 1-6 have been trained essentially using a patternrecognition strategy described in NMR Biomed. 2006 Jun;19(4):411-434. Inthis version of the DSS, all classifiers validated have been validated with an independent test set. We are working in the detailed description of the wholeprocess.

In 7 however, we implemented a peak ratio classifier that is described in AJNRAm J Neuroradiol. 2009 Mar;30(3):544-551.

Finally, in 8 we implemented a classifier that is described in NMR Biomed.2012 Jun ;25(6):819-28.

We envisage to add new classification plots for other discriminations in thefuture.

5DSS INTERPRET 3.1


You can use the INTERPRET 3.1 system to analyse new SV MRS cases at 1.5T, taking into account that the acquisition parameters for your cases shouldbe compatible with those in the INTERPRET DSS.

When you load a new case, the system will treat it as a study case (ofunknown pathology). Then you can evaluate it with the system. Once youfinish your analysis, you can store your case into the embedded database inorder to have your own collection of interesting cases which are called usercases.

Note: Beware that the installation of version 3.1 will not migrate your usercases or notes saved in previous versions. The installation of version 3.1 doesnot uninstall previous versions, therefore you can check your cases in theversion you have entered them. We plan to provide automatic migrations ofcases in subsequent versions.

Using the system:

Click on the INTERPRET DSS 3.1 icon on the desktop or in your start menu.The start-up screen will appear; you may have to wait a few seconds whilethe database loads. When the data has finished loading, the “I Accept” buttonbecomes active. To continue, select your login name and type your ownpassword in and click the “I Accept” button. If it is the first use of the system,or if you do not need to create another user for the application, then selectthe "admin" login account. If you are reading this help, you received thepassword after registering at our web site.

Sections:



The program has one main window with which the user interacts (shownabove). The window is composed by 5 sections: the Menu Bar, the OverviewPanel, the Options Panel and two Data Inspection panels.• The Menu Bar is the entry point to execute several common operations.

You can load and edit cases and perform user administration tasks.• The Overview Panel graphically shows the set of cases. Mathematically

speaking, for classifiers 1-6 it is the latent space. Each case is representedby a glyph.

• The Options Panel groups the display options. You can select which tissuetypes and cases sets are shown. Glyph colours, size and widths of thetissue types of the cases set can be selected. You can also customise thebackground colour of each classifier class in the "show boundaries"display.

• The top and the bottom data Inspection Panels are used to show allavailable data (MRS, MRI, clinical data and case notes), for a particularcase.

In the following sections of this help, you will find more information about theproper use of each section of the main program window.

7DSS INTERPRET 3.1


1.2 Overview Panel

What does the Overview Panel show?

By default, when you run the DSS, there is a visual overview of all cases onthe left part of the screen . This is a scatter-plot that shows cases as a glyph(circle, triangle, square or diamond).

Each case is displayed in a particular position which is brought by theclassifier. Classifiers were built using linear discriminant analysis (LDA)performed on the MRS information of the cases set except in the one called"Tumour vs. pseudotumoural disease with metabolite ratios".

On top of the Overview Panel there is a menu next to the "Classif:" title, thatallows you to choose the desired classifier.

The specifications for each classifier will be found in the popup informationwindow when the user clicks the blue i button on the top right part of theOverview Panel, next to the menu that allows you to choose the Classifier.There you can find the relevant information related to the classificationproblem, the TE and other acquisition conditions of the data used to train theclassifier, and about what pattern recognition technique has been employed,



and so on.

Each classifier was made using a specific data set or a combination of them.

Each dataset is represented by one of the four available glyphs: i.e. theoriginal INTERPRET cases, for example, are represented by circles.

You can customize this presentation at any time you want.

By default, the DSS contains four basic data sets:• INTERPRET, the cases used as training for the INTERPRET-based

classifiers.• IDI-Bellvitge, the cases used as training for the IDI-Bellvitge-based

classifiers.• Test cases may contain other cases from other databases, that have not

been used in the classifier development. We do not distribute datasetswith the system as a normal basis, but we use them for demonstrationpurposes.

• User cases is initially empty, but is where your own cases will be stored ifyou choose to do so.

Glyph colour indicates the tissue type of the case. We say "tissue type"instead of "tumour class" or "disease", since we decided that the systemshould be prepared to handle not only distinctions among different tumoursbut also between diseases or regions of the brain (such as normal non-affected brain or diseased brain). See the Option panel to learn about thecolour <--> tissue correspondences.

Crosses indicate the position of the average spectral pattern for a certaintissue type or the average of several tissue types grouped in a specificclassifier i.e. the "super class".

1.2.1 Using the Overview

Selecting a Case: To select a case, use the mouse to position the cursor over a glyph in theoverview plot and press the right button of the mouse.A popup menu will appear with the following options:

9DSS INTERPRET 3.1


• A short list with the nearest displayed cases to the selected point (e.g.

mm:I0011). Each one has a submenu and the user can choose whetherto display it on the top or the bottom data inspection panel.

• The Find case option, which displays a list with all available casessorted by tissue type. Each of these items has a submenu with the samefunctionality as above.

• The Clear Views option only appears if you had previously selectedcases to be shown on the data inspection panels. It clears both datainspection panels for the selected cases.

• The Zoom In option, which is used to zoom in a specific region of thespectrum.

• The Zoom out option appears when the overview has any casedisplayed out of their boundaries. Use it to change the overview zoom todisplay these cases.

• The Default Zoom option selects the region covered by the classifiers'borders and displays the overview with its default scale.

• The Reset View option resets all kinds of tissue types and datasets inorder to display the overview with its default configuration.

• The Show Boundaries option allows to display the boundaries of theclassifier's classes. Each of these "super classes" can be composed byone or more tissue types.

When you select a particular case, you can choose which of the two datainspection panels will be used to display it. The case is then displayed as afilled symbol and a line appears linking its position in the overview to the datainspection panel selected. When choosing the Zoom in option, the cursor changes into a cross locatedin the intersection of two new lines, one vertical and one horizontal. You canselect the first corner if you press the mouse button and drag it over an areaof the overview (this will be marked with a yellow box). When you release themouse button, the overview zooms into the selected area. To return to theoriginal view, you can select the Default View or Reset View options in thepop-up menu.



Show Boundaries uses a specific background colour to mark regions definedby each classifier class. These areas can be used to evaluate if a case maybelong to a particular class. To clear the boundaries choose the Noboundaries option in the pop-up menu.

1.2.2 Classifier Plots

The Overview Classification Plots -- technical description: Each classifier is represented by a 2D space where you can visualise allavailable cases from the internal data set or from your personal collection (User cases). If the classifier uses linear discriminant analysis (LDA), eachaxis in this two-dimensional space corresponds to each discriminant function.If another classifiaction method is used, the accompanying information foundclicking on the i button will show the meaning of each axis. Below follows the technical description of each classifier: • Most common tumour types, short TE:

Classifies among low-grade meningiomas, low-grade glial tumours(astrocytomas, oligodendrogliomas and oligoastrocytomas of WHO grade II)and high-grade aggressive tumours (glioblastomas and metastases). It issimilar to the classifier described in NMR Biomed. 2006 Jun;19(4):411-434.However, other tumour types as well as normal brain tissue and abscessescan be evaluated with this tool, as described in the referenced publication. The classification was done with “Spectra Classifier” (http://gabrmn.uab.es/sc). Relevant features were selected in the 4.05 – 0.01 ppm range using theSequential Forward method. Features were validated using the correlation-based criterion. Classification was performed with Fisher Linear DiscriminantAnalysis and evaluation was performed with the bootstrap method. Mean accuracy for all classes: 89%,Mean standard error for all classes: 2.2%,Mean accuracy for low-grade meningiomas: 86.3%,Mean standard error for low-grade meningiomas: 4.4%,Mean accuracy for low-grade glial tumours: 88.6%,Mean standard error for low-grade glial tumours: 5.6%,Mean accuracy for high-grade aggressive tumours: 90.3%,Mean standard error for high-grade aggressive tumours: 2.7%.The variables used by the classifier are the following: 1.23, 2.19, 3.03,2.28, 3.62, 2.40 and 1.34 ppm.

11DSS INTERPRET 3.1


Uses multicentre training SV short TE data (20-32 ms) from the INTERPRETvalidated database, obtained from 1.5T scanners, as described in MAGMA.2006 Feb;19(1):22-33. The training dataset used is composed of 217 short TE spectra: 58 low-grade meningiomas, 35 low-grade glial tumours and 124 high-gradeaggressive tumours. Data were processed with the INTERPRET data manipulation softwareversion 6 and a subset of the training data were processed first with jMRUIin compatible conditions to the INTERPRET data manipulation softwareversion 6 and converted later into this format. This was done to correct forphasing and baseline problems, as well for processing data in very oldformats. The subset is: I0009, I0055, I0060, I0068, I0069, I0097, I0135,I0162, I0172, I0229, I0253, I0297, I0311, I0378, I0393, I0399, I1035,I1074, I1076, I1277, I1377, I1378, I1379, I1381. Validation of the classifier was performed with an independent test setfrom CDP-IAT and IDI-BAD in Barcelona, Spain (data not available with thissoftware), processed in the same conditions as above. The test set used iscomposed of 63 short TE spectra: 3 low-grade meningiomas, 20 low-gradeglial tumours and 40 high-grade aggressive tumours.

• Most common tumour types, long TE:

Classifies among low-grade meningiomas, low-grade glial tumours(astrocytomas, oligodendrogliomas and oligoastrocytomas of WHO grade II)and high-grade aggressive tumours (glioblastomas and metastases). Issimilar to the classifier described in NMR Biomed. 2006 Jun;19(4):411-434.However, other tumour types as well as normal brain tissue and abscessescan be evaluated with this tool, as described in the referenced publication. The classification was done with “Spectra Classifier” (http://gabrmn.uab.es/sc), Relevant features were selected in the 4.05 – 0.01 ppm range using theSequential Forward method. Features were validated using the correlation-based criterion. Classification was performed with Fisher Linear DiscriminantAnalysis and evaluation was performed with the bootstrap method. Mean accuracy for all classes: 84.2%,Mean standard error for all classes: 2.6%,Mean accuracy for low-grade meningiomas: 89.2%,Mean standard error for low-grade meningiomas: 4.1%,Mean accuracy for low-grade glial tumours: 96.7%,Mean standard error for low-grade glial tumours: 3.3%,Mean accuracy for high-grade aggressive tumours: 78.1%,Mean standard error for high-grade aggressive tumours: 3.9%.



The variables useed by the classifier are the following: 1.23, 1.49, 3.01,1.15, 3.74, 2.32, 1.19 ppm. Uses multicentre SV long TE data (135-136 ms) from the INTERPRETvalidated database, obtained from 1.5T scanners, as described in MAGMA.2006 Feb;19(1):22-33. The training dataset used is composed of 195 short TE spectra: 55 low-grade meningiomas, 31 low-grade glial tumours and 109 high-gradeaggressive tumours. Data were processed with the INTERPRET data manipulation softwareversion 6 and a subset of the training data were processed first with jMRUIin compatible conditions to the INTERPRET data manipulation softwareversion 6 and converted later into this format. This was done to correct forphasing and baseline problems, as well for processing data in very oldformats. The subset is: I0009, I0037, I0045, I0098, I0105, I0135, I0143,I0145, I0155, I0163, I0169, I0173, I0174, I0175, I0176, I0177, I0180,I0184, I0185, I0190, I0194, I0195, I0197, I0198, I0205, I0206, I0207,I0208, I0211, I0213, I0214, I0217, I0219, I0220, I0223, I0224, I0240,I0241, I0351, I0354, I0357, I0358, I0359, I0362, I0365, I0370, I0372,I0373, I0376, I0391, I0402, I0409, I0415, I0417, I0423, I0427, I1046,I1047, I1048, I1054, I1067, I1071, I0172, I1073, I1075, I1090, I1097,I1108, I1237, I1278, I1377, I1381, I1385, I1388, I1458, I1459, I1460,I1461, I1473. Validation of the classifier was performed with an independent test setfrom CDP-IAT and IDI-BAD in Barcelona, Spain (data not available with thissoftware), processed in the same conditions as above. The test set used iscomposed of 63 short TE spectra: 3 low-grade meningiomas, 20 low-gradeglial tumours and 40 high-grade aggressive tumours.

• Most common tumour types, short and long TE:

Classifies among low-grade meningiomas, low-grade glial tumours(astrocytomas, oligodendrogliomas and oligoastrocytomas of WHO grade II)and high-grade aggressive tumours (glioblastomas and metastases). The classification was done with “Spectra Classifier” (http://gabrmn.uab.es/sc). Relevant features were selected in the 4.05 – 0.01 ppm range using theSequential Forward method. Features were validated using the correlation-based criterion. Classification was performed with Fisher Linear DiscriminantAnalysis and evaluation was performed with the bootstrap method. Mean accuracy for all classes: 89.2%,Mean standard error for all classes: 2.2%,Mean accuracy for low-grade meningiomas: 90.9%,

13DSS INTERPRET 3.1


Mean standard error for low-grade meningiomas: 3.9%,Mean accuracy for low-grade glial tumours: 90.3%,Mean standard error for low-grade glial tumours: 5.4%,Mean accuracy for high-grade aggressive tumours: 88.0%,Mean standard error for high-grade aggressive tumours: 3.1%. The variables useed by the classifier are the following: Short TE 1.23, 2.19,3.53, 2.28, 1.26 ppm and Long TE 1.15, 3.01, 1.51 and 3.74 ppm. Uses multicentre SV short (20-32 ms) and long TE data (135-136 ms) fromthe INTERPRET validated database, obtained from 1.5T scanners, asdescribed in MAGMA. 2006 Feb;19(1):22-33.The spectra were concatenated (put one after the other), first short TEand second long TE, for performing the analysis.The training dataset used is composed of 195 short TE spectra: 55 low-grade meningiomas, 31 low-grade glial tumours and 109 high-gradeaggressive tumours. For processing details, refer to the classifiers: "Mostcommon tumour types, short TE" and "Most common tumour types, longTE".

• Glioblastomas vs. Metastases (Vellido, NMR Biomed, 2012):

Classifies between glioblastomas and metastases, as described in NMRBiomed. 2012 Jun ;25(6):819-28.

The classifier was obtained using a robust method for feature (spectralfrequency) selection coupled with a linear-in-the-parameters single-layerperceptron classifier. For the test set, a parsimonious selection of fivefrequencies yielded an area under the receiver operating characteristiccurve of 0.86, and an area under the convex hull of the receiver operatingcharacteristic curve of 0.91.

The classification uses features from long and short echo time and theclassification formula used is the following linear combination:

a(x) = 1.383 - 0.088(L3.01 ppm) - 0.377(L2.32 ppm) + 0.250(L2.29ppm) + 0.153(L2.02 ppm) - 0.261(S2.17 ppm)

Where S or L represents either short of long echo time and "n.nn ppm" thatfollows the S or L letter is the peak height of the specified ppm value.

This formula is then used for classification by expressing the Single LayerPerceptron output (prediction) y for a given case as:

y(x) = tanh [a(x)]

Given a mid-range classification threshold of y(x) = 0, a value of y(x)>0



would correspond to a metastasis diagnostic prediction; therefore, anoutput of unity would correspond to a fully confident metastasis prediction.

Uses multicentre SV short (20-32 ms) and long TE data (135-136 ms) fromthe INTERPRET validated database, obtained from 1.5T scanners, asdescribed in MAGMA. 2006 Feb;19(1):22-33. Both spectrum for each casewere put one after the other for analysis.

The training dataset used is composed of 195 short TE spectra: 55 low-grade meningiomas, 31 low-grade glial tumours and 109 high-gradeaggressive tumours. For processing details, refer to the classifiers: "Mostcommon tumour types, short TE" and "Most common tumour types, longTE".

• Tumour vs. pseudotumoural disease (AJNR 2009):

Classifies among tumours (glial of low and intermediate grade,Astrocytoma WHO grade II, Oligodendroglioma WHO grade II,Oligoastrocytoma WHO grade II, Astrocytoma WHO grade III,Oligoastrocytoma WHO grade III) and pseudotumours (Acute infarct,Multiple sclerosis, Acute disseminated encephalomyelitis, and no specificpseudotumoural disease), as described in AJNR Am J Neuroradiol. 2009Mar;30(3):544-551. The classification is done using the m-Ino/NAA ratio at short TE(30ms) and the Cho/NAA ratio at long TE (136ms).An AUC of 0.95 was obtained for values of the m-Ino/NAA ratio > 0.90 anda Cho/NAA ratio > 1.90. Uses data acquired at the Institut de Diagnòstic per la Imatge (IDI) of theBellvitge Hospital, in Barcelona, Spain. These are SV short TE spectra (30ms) and long TE (136 ms). Number of cases: Tumours, 19 Astrocytoma WHO grade II, 4Oligodendroglioma WHO grade II, 3 Oligoastrocytoma WHO grade II, 16Astrocytoma WHO grade III, 6 Oligoastrocytoma WHO grade III.Pseudotumours, 1 Acute infarct, 3 Multiple sclerosis, 1 Acute disseminatedencephalomyelitis, and 3 cases were no of specific pseudotumoural disease.

• Tumour vs. pseudotumoural disease, short TE:

Classifies among pseudotumoural disease (Acute infarct, Multiple sclerosis,Acute disseminated encephalomyelitis, and no specific pseudotumouraldisease), tumours (Glial low and intermediate grade: Astrocytoma WHOgrade II, Oligodendroglioma WHO grade II, Oligoastrocytoma WHO gradeII, Astrocytoma WHO grade III, Oligoastrocytoma WHO grade III) andnormal brain tissue.

15DSS INTERPRET 3.1


The classification was done with “Spectra Classifier” (http://gabrmn.uab.es/sc). Relevant features were selected in the 4.05 – 0.01 ppm range usingthe Sequential Forward method. Features were validated using thecorrelation-based criterion. Classification was performed with Fisher LinearDiscriminant Analysis and evaluation was performed with the bootstrapmethod. Mean accuracy for all classes: 85.5%,Mean standard error for all classes: 4.3%,Mean accuracy for pseudotumoural disease: 83.3%,Mean standard error for pseudotumoural disease: 11.2%,Mean accuracy for normal brain: 100.0%,Mean standard error for normal brain: 0%,Mean accuracy for tumours: 84.4%,Mean standard error for tumours: 5.3%. The variables used by the classifier are the following: 1.99, 2.01, 3.53,1.97, 0.86 and 1.23 ppm. Uses SV short TE data from patients that were explored at IDI-Bellvitge(Barcelona, Spain). Data were obtained from a Philips 1.5T scanner. Thevolume of interest (VOI) ranged between (1.5 cm)3 and (2 cm)3. VOI sizeand location for tumours and pseudotumours were determined with the aimof positioning the largest possible voxel within the abnormal area, withminimal contamination from the surrounding nontumoural tissue.Acquisition conditions were (2000ms/30ms/96-192) (TR/TE/averages). The training dataset used is composed of 70 short TE spectra:pseudotumoural disease, 19 cases; tumours, 46 cases and normal braintissue, 5 cases.Data were processed with jMRUI in compatible conditions to the INTERPRETdata manipulation software version 6 and converted later into INTERPRETformat. Validation of the classifier was performed with an independent test setfrom the same centre (data not available with this software), processed inthe same conditions as above. The test set used is composed of 19 short TEspectra: 7 pseudotumoural disease cases and 12 tumours.

• Tumour vs. pseudotumoural disease, long TE:

Classifies among pseudotumoural disease (Acute infarct, Multiple sclerosis,Acute disseminated encephalomyelitis, and no specific pseudotumouraldisease), tumours (Glial low and intermediate grade: Astrocytoma WHOgrade II, Oligodendroglioma WHO grade II, Oligoastrocytoma WHO gradeII, Astrocytoma WHO grade III, Oligoastrocytoma WHO grade III) and



normal brain tissue. The classification was done with “Spectra Classifier” (http://gabrmn.uab.es/sc). Relevant features were selected in the 4.05 – 0.01 ppm range using theSequential Forward method. Features were validated using the correlation-based criterion. Classification was performed with Fisher Linear DiscriminantAnalysis and evaluation was performed with the bootstrap method. Mean accuracy for all classes: 81.1%,Mean standard error for all classes: 4.9%,Mean accuracy for pseudotumoural disease: 66.7%,Mean standard error for pseudotumoural disease: 14.1%,Mean accuracy for normal brain: 100.0%,Mean standard error for normal brain: 0%,Mean accuracy for tumours: 82.9%,Mean standard error for tumours: 5.5%. The variables used by the classifier are the following: 2.01, 2.45, 2.40,0.71, 1.99, and 1.38 ppm. Uses SV long TE data from patients that were explored at IDI-Bellvitge(Barcelona, Spain). Data were obtained from a Philips 1.5T scanner. Thevolume of interest (VOI) ranged between (1.5 cm)3 and (2 cm)3. VOI sizeand location for tumours and pseudotumours were determined with the aimof positioning the largest possible voxel within the abnormal area, withminimal contamination from the surrounding nontumoural tissue.Acquisition conditions were (2000ms/30ms/96-192) (TR/TE/averages). The training dataset used is composed of 70 long TE spectra:pseudotumoural disease, 19 cases; tumours, 46 cases and normal braintissue, 5 cases.Data were processed with jMRUI in compatible conditions to the INTERPRETdata manipulation software version 6 and converted later into INTERPRETformat. Validation of the classifier was performed with an independent test setfrom the same centre (data not available with this software), processed inthe same conditions as above. The test set used is composed of 19 short TEspectra: 7 pseudotumoural disease cases and 12 tumours.

• Tumour vs. pseudotumoural disease, short and longTE:

Classifies among pseudotumoural disease (Acute infarct, Multiple sclerosis,Acute disseminated encephalomyelitis, and no specific pseudotumouraldisease), tumours (Glial low and intermediate grade: Astrocytoma WHOgrade II, Oligodendroglioma WHO grade II, Oligoastrocytoma WHO gradeII, Astrocytoma WHO grade III, Oligoastrocytoma WHO grade III) and

17DSS INTERPRET 3.1


normal brain tissue. The classification was done with “Spectra Classifier” (http://gabrmn.uab.es/sc). Relevant features were selected in the 4.05 – 0.01 ppm range using theSequential Forward method. Features were validated using the correlation-based criterion. Classification was performed with Fisher Linear DiscriminantAnalysis and evaluation was performed with the bootstrap method. Mean accuracy for all classes: 92.1%,Mean standard error for all classes: 3.4%,Mean accuracy for pseudotumoural disease: 83.5%,Mean standard error for pseudotumoural disease: 11.4%,Mean accuracy for normal brain: 100.0%,Mean standard error for normal brain: 0%,Mean accuracy for tumours: 93.5%,Mean standard error for tumours: 3.7%. The variables used by the classifier are the following: Short TE, 1.99 and0.84. Long TE: 2.45, 2.40, 0.71, 1.99, 1.38, 3.20 and 3.32 ppm. The spectra were concatenated (put one after the other), first short TEand second long TE, for performing the analysis. Patients that were explored at IDI-Bellvitge (Barcelona, Spain).Data wereobtained from a Philips 1.5T scanner. The volume of interest (VOI) rangedbetween (1.5 cm)3 and (2 cm)3. VOI size and location for tumours andpseudotumours were determined with the aim of positioning the largestpossible voxel within the abnormal area, with minimal contamination fromthe surrounding nontumoural tissue. Acquisition conditions were(2000ms/30ms/96-192) (TR/TE/averages). The training dataset used is composed of 70 long TE spectra:pseudotumoural disease, 19 cases; tumours, 46 cases and normal braintissue, 5 cases. Data were processed with jMRUI in compatible conditions to the INTERPRETdata manipulation software version 6 and converted later into INTERPRETformat. Validation of the classifier was performed with an independent test setfrom the same centre (data not available with this software), processed inthe same conditions as above. The test set used is composed of 19 short TEspectra: 7 pseudotumoural disease cases and 12 tumours.



1.2.3 Manual Overview

Database exploration with the overview using ppm intensities: When you select Make your own Overview as your visualisation tool fromthe list shown on the top of the Overview plot, you will see the overviewcontrols tab in the options panel. Clicking on it, you can see a simple scatterplot of the selected data sets using intensities at given ppm values on the xand y axes. These ppm values can be selected using the sliders.For example, if you would like to distinguish between "tumours" and "healthybrain tissue", you can choose X=2.01 and Y=3.03 in the respective sliders, asshown below. This is the default when you select the Make your ownOverview option.

You can also make a scatter plot of intensity ratios both in the X and the Yaxes with the sliders on the right side. If the user wants to turn back to singleintensities scatter plot, just move the cursor of the right X/Y sliders to the4.3-5.1 zone. The scatter plot carried out will remain with the same values if you switchbetween short and long echo time using the top buttons named Using ShortEcho Time and Using Long Echo Time. Be careful with this representation and keep in mind that the intensitieshave been calculated as the height of the selected ppms over thebaseline plus the minimum value of all spectra of the same TE of allloaded cases.

1.3 Options Panel

How do I modify what the overview panel shows? The Options Panel below the Overview Panel is composed by four tabs:Classifier classes, Available tissues, Cases Set and Own overview.

19DSS INTERPRET 3.1


• The Classifier classes tab shows how the composition of the classifierclases ("super classes") and which are the colours of their boundaries(coloured left square next to the classifier class) and glyphs. The user canalso change the colour used to represent any background or any class byclicking on their respective rectangles on the left of the classifier classname.

For example, below is shown the case of three classifier classes: Aggressive,Low Grade and Meningioma. The Aggressive classifier class is composed ofGlioblastoma and Metastasis. The background is light grey, and the colour ofthe glyphs representing each case is red.

• The Available tissues tab shows what tissue types you can find in the

database, which is the colour of their glyphs and whether they aredisplayed or not in the Overview.

In the example below, you will find the following ones in the Overview:Astrocytoma 2 in sky blue, Glioblastoma in red, Meningioma in white,Metastasis in pink, Oligoastrocytoma in seagreen and Oligodendroglioma inblue

Although the active overview plot does not use all tissue types, you can showthe position of cases from other tissue types in the active plot by clicking onthe checkbox next to their name. You can also change the colour used torepresent any particular disease by clicking on the coloured circles next to thecheckbox. • The Cases Set tab can be used to customize dataset visibility, its

correspondence with the glyph type, size and width. In the example below, you see that we selected the INTERPRET dataset, andthat it is represented by circles of size 2 and width 1.



1.4 Data Inspection Panel

The Case browser The Data Inspection Panel provides the access point to the particularinformation about each case.The ID and tissue type of the selected case are written.You should also see four 'tabs' ( MRS / MRI / Clinical Record / Case Notes).Click the MRS tab to view the spectrum of a case.Click MRI to view any available images.Click Clinical Record to view clinical information. Clinical record and MRImay not be available for some cases.Click Case Notes to add any notes you wish to make on any case.

21DSS INTERPRET 3.1


Click MRS to view the spectrum (no spectrum is shown if no case is selected).As you move the cursor over the spectrum window you will notice that theppm value in the yellow box (top right) changes. The box shows the ppmvalue at the current position of the cursor. Press the right mouse button withthe cursor in the spectrum window to obtain a popup menu with the optionsavailable. The zoom works in the same way as it does for the overview - you have todrag the mouse button and then you have to release the button: You candrag out of the window to zoom into portions of the spectrum not shown inthe overview. The maximum range is 7 to-2ppm.• Fill window scales up the spectrum so that the highest peak fits in the

window.• To scale shows all spectra in the dataset in the same scale.• The scale on the y-axis is the same in both the top and the bottom MRS

viewers.• Show metabolites shows marks indicating the expected positions of some

significant metabolites. The interrogation symbol means that you shouldnot take these assignments as a rule. These assignments correspond tothe most important contribution at a certain ppm. However, if you are anexpert spectroscopist you might find that "Cho" is inexact, since the so-called Choline peak in fact has contributions from several metabolites, suchas Choline, Phosphocholine and Glycerophosphocholine.



• Click the show other MRS button to view the other spectrum (if it isavailable). For example, if you are on a short TE display, analysing a shortTE spectrum, you might want to see how the long TE of this case looks like.This option allows you to do so.

When show other MRS is active, the MRS viewer area is divided into twosections. If the active classifier selected in the Overview Panel uses theshort or the spectra at two TEs, the short TE spectrum is the first spectrumshown (left) and the long TE spectrum is the second one (right). If the activeclassifier uses long TE spectra, then the long TE spectrum is the first shown. For showing typical spectral patterns for a variety of tissue types, use theoverlay mean+/- SD menu. This shows (as a grey outline) the range for themean spectrum plus or minus one standard deviation for each tissue type(means and standard deviation are generated automatically from all availablespectra of the given tissue type in each dataset).

1.4.1 MRI Viewer

The MR image viewer is fairly self-explanatory, if any images are availablefor a case they are shown here. On the right side, there are mini-previews ofall available images for the case. Click on one of these to view it at a largersize on the left hand side of the panel.Use the menu on the top left to enlarge the chosen image up to 200%.

23DSS INTERPRET 3.1


1.4.2 Clinical Record Viewer

This viewer displays some clinical information available for a case.For the INTERPRET dataset, check http://gabrmn.uab.es/INTERPRET forfurther information about a case.



1.4.3 Case Notes Viewer

This area allows users to write any notes they may wish to make on a case. Ifthe case has been added by the user, he/she can edit or delete their notesstored previously. You may find that there are cases cases that already have notes the first timeyou start using the system. These notes have been added by the developersin order to clarify unexpected positions of cases in the Overview Space orany data acquisition issues.

25DSS INTERPRET 3.1


1.5 Test Cases

A few typical cases are available for user practice in the Test Cases menu.These are validated INTERPRET cases unused for classifier development, forboth long and short echo times. To show the test cases select one among the available ones in the TestCases menu. The respective case will appear in the top Data InspectionPanel.

1.6 New Cases

You can upload new cases and evaluate them, with the aim of characterisingthe disease or tissue type the SV spectra of the case belongs to. First, you need to collect all the information and data for the case before youupload it.



You can match two different spectra at two different TE (one short TE withone long TE) for each case. Be careful to select the correct TE in the spacesprovided in order to find the correct files. Second, you can proceed to analyse and evaluate each uploaded case. Third, you can decide whether to store permanently your uploaded cases intothe embedded database. You will be able to edit any stored case at any time. Remember that if you do not store new cases into the embedded database,the next time that you run the DSS they will not be available and you willhave to upload them again.

1.6.1 Load New Cases

New cases can be added through the Load new case option in the menu ontop of the screen. A new window will appear for selecting the casefiles.

27DSS INTERPRET 3.1


Two types of MRS data can be entered into the system: 1) “Raw" data or time domain data: Unprocessed MRS data from thefollowing manufacturers and formats can be entered into the system:

1. Raw data directly exported from Philips scanners (sdat/spar).2. Raw data directly exported from a GE scanner up to version 9X, in

SAGE format (Pxxxx file with an accompanying shf file oralternatively a pair of files sdf/shf).

3. Raw data directly exported from Siemens scanners (.rawextension)

2) Processed data or frequency domain data: Processed spectra. Filesprocessed with the INTERPRET DMS (data manipulation software) (*.art or *.txt) extension or with jMRUI (*.txt). For both MRS types, you should provide enough information in order tocorrectly process and load the case.Please be careful with the options. On top of the window, you can see a text box used to set the identificationlabel of the case that you want to load. Please provide a unique name forevery case you load.



If the case you would like to load has spectra at two TEs (short and long),please select each one in the appropiate spaces provided. If you do notupload the correct pair or only select one spectrum, do not worry, you canalways upload the case again. The uploaded cases will be shown on the Visual overview of Case Database asa yellow glyph; their spectrum will be shown on the top right panel, theSpectrum Viewer. Although automated alignment check-up is carried out by the processingalgorithm, you may want to double check it in case of uncommon spectralpatterns. The creatine resonance should appear at 3.034+ 0.004 ppm. Ifcreatine is not visible, total choline should appear at 3.210 + 0.004 ppm. Ifnot, manual realignment of the processed file may be needed. Please contactDSS providers for support on that.

1.6.1.1 Siemens scanners

In order to process and classify raw data files from Siemens, please checkthat you have both metabolite (water suppressed) and water (unsuppressedwater) .raw data files, and ensure that they are located in the samedirectory. This is mandatory for the INTERPRET data manipulation software(DMS) to process your files. The second issue is the file name: your filesmust be renamed with the following convention: [filename_metabolite].rawfor the metabolite file and [filename_water].raw for the water file. The“filename” should not contain any blank spaces or characters such as “_”.

1.6.1.2 GE scanners

Raw data directly exported from GE scanners (up to 9X version)· For the Sage formats that have metabolite (suppressed water) and water

(unsuppressed water) and sdf/shf pairs of files, be sure that the files arein the same directory and are named according to the followingconvention: [filename_metabolite].[sdf/shf] and [filename_water].[sdf/shf].

· For the PROBE files, be sure that both the PROBE file and theaccompanying shf file are in the same directory and that have the samefilename (P[filename] and P[filename].shf). The “filename” should notcontain any blank spaces or characters such as “_”.

29DSS INTERPRET 3.1


1.6.1.3 Philips scanners

In order to process and classify raw data files with the DSS, please check thatyou have both metabolite (water suppressed) and water (unsuppressedwater) acquisitions and that you have the corresponding spar/sdat pairs foreach of these. Ensure that all are located in the same directory. This ismandatory for the INTERPRET data manipulation software (DMS) to be able toprocess your files. Secondly, you have to name your files in a specific way:[filename_metabolite].[spar/sdat] for the metabolite acquisitions and[filename_water].[spar.sdat] for the unsuppressed water acquisitions. The“filename” should not contain any blank spaces or characters such as “_”.

1.6.1.4 jMRUI and jDMS

Data processed with jMRUI: Some data formats cannot be processed yet by the INTERPRET datamanipulation software (DMS), which is embedded into the DSS. For thesecases to be classified, they have to be adapted previously to the DMSformat, for example in the number of points and ppm range. VERY IMPORTANT: In order to enter data into the DSS and that the resultsobtained are comparable to those obtained with cases processedautomatically by the DMS, the same processing parameters as in theINTERPRET DSS must be used. The processing parameters that are equivalent to those in the DMS are:• Correct phase with reference file dividing the metabolite file by the water

file.• Zero filling (add 308 points) for Philips files. NO zero filling is needed for

GE or Siemens files. This is only valid for files acquired according toconsensus INTERPRET protocols (NMR Biomed. 2006 Jun;19(4):411-434),namely (points/SW) Philips: 512/1000Hz; Siemens 1024/1000Hz; GeneralElectric 2048/2500Hz. For other acquisition conditions, check [email protected] for possible help.

• Adjust the carrier frequency (0 ppm) to the water ppm (4.75ppm).• Water filtering (HLSVD) between 4.3 and 5.1 ppm with 10 lorentzians.• Line broadening of 1Hz.



Process the file with jMRUI with using these parameters and save it as a *.txtfile. In the same way as in raw data files, if you load a case that is not of thesame TE (short or long) as the overview that you have selected (short or longTE), the case will not appear in the overview unless you switch to the correctTE overview.

The case will now appear in the overview as a yellow filled glyph and thespectrum will be displayed on the top right panel. Data processed with the DMS: Data processed with the DMS should have either “.art” or ".txt" extensions.You have to choose and upload the aligned ".art" file (aligned stands for theprocessed spectrum after automatic alignment check and correction ifnecessary). Be careful with this step because the classification results mightchange significantly if spectrum is misaligned. In the same way as in raw data files, if you load a case that is not of thesame TE (short or long) as the overview that you have selected (short or longTE), the case will not appear in the overview unless you switch to the correctTE overview.

The case will now appear on the overview as a yellow filled glyph and thespectrum will be displayed on the top right panel.

1.6.2 Decision Support

So, what is the possible classification of the case? - Well, that is up toyou, this is a Decision-Support System, which means that the system doesnot produce an answer but helps you to decide. Consider the following:· Where is the case in the overview plot. Does it appear inside a cluster of

cases of a particular disease type?· Which is the nearest pathology group centroid (crosses in the overview

plot)?· What is the class of cases particularly close to your case in the overview, do

spectra from these cases look similar?

Does the new case/s spectrum fit well with any of the 'typical' spectra (usethe overlay menu in MRS display)?

31DSS INTERPRET 3.1


Additionally, note that you can now use two echo times to help you to decidewhich the diagnosis can be. If you have acquired SV spectra both at short(20-32ms) or long (135-136ms) echo times you can examine where they areplaced in the overview plot at both TE. In a study by Majós et al (AJNR Am JNeuroradiol. 2004 Nov-Dec;25(10):1696-1704), it was shown that the use oftwo TEs significantly improves classification accuracy: basically, if theclassification of the case is the same for the two TEs then the chances thatthis classification is the correct one are higher than with using only one echotime. Our practical advice is that you use both TE to decide: if the use of thesystem leads to the same conclusions at both TE, this reassures you in yourdiagnosis. In contrast, if the conclusions that you reach after using thesystem are different for the two TE, your certainty about the diagnosis shouldprobably be lower than in the first situation. In other study made by García-Gómez et al. (NMR Biomed. 2008 Nov;21(10):1112-1125), they provide more information about the effect ofcombining two TE. There is the basal idea to develop classifiers that combinesthe data from short and long TE spectra. Keep in mind the label "short"means spectra acquired at 20-32 ms of TE and the label long means TEbetween 135 and 136 ms. Although automated alignment check-up is carried out by the processingalgorithm, you may want to double check it in case of uncommon spectralpatterns. The creatine resonance should appear at 3.034+ 0.004 ppm. Ifcreatine is not visible, total choline should appear at 3.210 + 0.004 ppm. Ifnot, manual realignment of the processed file may be needed. Please contactDSS developers for support on that.

1.6.3 Save Loaded Cases

Once you have performed all analyses, or when you have an especiallyinteresting case that you want to save for later, you can store it permanentlyinto the embedded database. Select the Save loaded cases option from theUser cases menu and a new window will appear.



You are able to:• View the short and long TE spectra loaded.• Select the appropriate pathology: Select one of the available pathologies

on the list.• Add the voxel image and/or any other available images: Select the MR

Images tab and choose the add Voxel Image in order to attach therespective voxel image. You can also add more reference images clickingon the button below.

Keep in mind that spectra and images can not be edited once the casehas been saved.

33DSS INTERPRET 3.1


• Add clinical record information. Select the Clinical Record tab and

complete the data you want to store with the case.



When you are sure that all information is correct you can store the case usingthe Save current case button. Not all cases loaded must be stored. You can navigate through the casesusing the Next button. If you do not want to store any of the loaded cases,simply press the Cancel button.

35DSS INTERPRET 3.1


1.6.4 Edit User Cases

Once you have at least one saved case in the embedded database, you canedit the information in its clinical record in order to enrich the case withinformation.In future releases the user may be allowed to change the spectral information(like TR, TE or sequence type) and the images provided.



1.7 Reference Material

a) Expected ppm position for different metabolites:· Aminoacids: resonances between 0.8-1.1 ppm, inverted 180º at long echo

time.· Mobile lipids: major resonances at 0.9 ppm, 1.29 ppm, 2.0 ppm and 5.3

ppm.· Lactate: doublet centered at 1.3 ppm, inverted 180º at long echo time.· Alanine: doublet centered at 1.4 ppm, inverted 180º at long echo time.· Acetate : singlet peak at 1.92 ppm.· NAC: N-acetylaspartate and N-acetyl containing compounds (e.g. sialic acid

linked to macromolecules, Candiota et al., 2004) singlet peak at 2.02ppm.· Glutamate/Glutamine: resonances between 2.1-2.4 ppm, partially inverted

at long echo time.· Creatine: singlet peak at 3.03ppm.· Choline-Containing compounds: singlet peak at 3.21ppm.· Taurine: signals at 3.27 ppm (generally overlapping with choline-containing

compounds) and at 3.43 ppm. These resonances are partially inverted atlong echo time.

· Glycine/Myo-Inositol: peaks overlapped at 3.56 ppm, being partiallydistinguished in the following way:

· Glycine is a singlet peak at 3.56 ppm that does not change substantiallyits intensity at long echo time in comparison to creatine, for example.

· Myo-inositol is a more complex resonance with one of its signalsoverlapping with that of glycine at 3.56 ppm. Because of J-modulation,this signal is attenuated at long echo time by a factor of about 5 incomparison to creatine, for example.

b) Bibliography Follow the links only if your computer has an internet connection available.

1.Julià-Sapé M, Coronel I, Majós C, Candiota AP, Serrallonga M, Cos M,Aguilera C, Acebes JJ, Griffiths JR, Arús C. "Prospective diagnosticperformance evaluation of single-voxel 1H MRS for typing and grading ofbrain tumours". NMR Biomed. 2012 Apr;25(4):661-73.DOI: 10.1002/nbm.1782. Epub 2011 Sep 23.

2.Vellido A, Romero E, Julià-Sapé M, Majós C, Moreno-Torres Á, Pujol J,Arús C. "Robust discrimination of glioblastomas from metastatic braintumors on the basis of single-voxel (1)H MRS". NMR Biomed. 2012Jun;25(6):819-28. doi: 10.1002/nbm.1797. Epub 2011 Nov 13. NMRBiomed. 2012 Jun ;25(6):819-28.

37DSS INTERPRET 3.1


3.Barba, I., Moreno, A., Martínez-Pérez, I., Tate, A.R., Cabañas, M.E.,Baquero, M., Capdevila, A., Arús, C., "Magnetic resonance spectroscopyof brain hemangiopericytomas: high myoinositol concentrations anddiscrimination from meningiomas". J. Neurosurg. 94: 55-60, 2001.http://www.ncbi.nlm.nih.gov/pubmed/11147898.

4.Candiota AP, Majós C, Bassols A, Cabañas ME, Acebes JJ, Quintero MR,Arús C. "Assignment of the 2.03 ppm resonance in in vivo 1H MRS ofhuman brain tumour cystic fluid: contribution of macromolecules". Magn.Reson. Mater. Phy., MAGMA.,17:36-46, 2004. http://www.springerlink.com/content/u97uef5xj3w6d5rm/.

5.García-Gómez JM, Tortajada S, Vidal C, Julià-Sapé M, Luts J, Moreno-Torres A, Van Huffel S, Arús C, Robles M. "The effect of combining twoecho times in automatic brain tumor classification by MRS";. NMRBiomed. 2008 Nov;21(10):1112-25.http://www3.interscience.wiley.com/journal/121392313/abstract?CRETRY=1&SRETRY=0DOI: 10.1002/nbm.1288

6.Julià-Sapé M, Acosta D, Majós C, Moreno-Torres A, Wesseling P, AcebesJJ, Griffiths JR, Arús C. "Comparison between neuroimagingclassifications and histopathological diagnoses using an internationalmulticenter brain tumor magnetic resonance imaging database". JNeurosurg. 105:6-14, 2006. http://www.ncbi.nlm.nih.gov/pubmed/16874886

7.Julià-Sapé M, Acosta D, Mier M, Arús C, Watson D; INTERPRETconsortium. "A multi-centre, web-accessible and quality control-checkeddatabase of in vivo MR spectra of brain tumour patients". Magn. Reson.Mater. Phy., MAGMA, 19:22-33, 2006. http://www.springerlink.com/content/elq1x6776h8p261k/

8.Julià-Sapé M, Coronel I, Majos C, Castañer S, Aguilera C, Arús C. "AProspective Study on the Added Value of MRS in Brain Tumor Diagnosis".RSNA 2005, Chicago, IL.http://rsna2005.rsna.org/rsna2005/V2005/conference/event_display.cfm?em_id=4407124

9.Majós, C., Julià-Sapé, M., Alonso, J., Serrallonga, M., Aguilera, C., Acebes, J.J., Arús, C., Gili, J. "Brain tumor classification by proton MRspectroscopy: comparison of diagnostic accuracy at short and long echotimes". AJNR Am J Neuroradiol, 25:1696-1704, 2004.http://www.ajnr.org/cgi/content/full/25/10/1696

10.Quintero, M.R., Cabañas, M.E., Arús, C. "A possible cellular explanation



for the NMR-visible mobile lipid (ML) changes in cultured C6 glioma cellswith growth". http://www.ncbi.nlm.nih.gov/pubmed/17150408

11.Tate AR, Underwood J, Acosta DM, Julià-Sapé M, Majós C, Moreno-Torres A, Howe FA, van der Graaf M, Lefournier V, Murphy MM,Loosemore A, Ladroue C, Wesseling P, Luc Bosson J, Cabañas ME,Simonetti AW, Gajewicz W, Calvar J, Capdevila A, Wilkins PR, Bell BA,Rémy C, Heerschap A, Watson D, Griffiths JR, Arús C. "Development of adecision support system for diagnosis and grading of brain tumoursusing in vivo magnetic resonance single voxel spectra". NMR Biomed.19:411-434, 2006.

12.Tate AR, Majós C, Moreno A, Howe FA, Griffiths JR, Arús C. "Automatedclassification of short echo time in in vivo 1H brain tumor spectra: amulticenter study".Magn Reson Med. 49:29-36, 2003http://www3.interscience.wiley.com/cgi-bin/abstract/102521951/ABSTRACT

13.Tate AR, Griffiths JR, Martínez-Pérez I, Moreno A, Barba I, Cabañas ME,Watson D, Alonso J, Bartumeus F, Isamat F, Ferrer I, Vila F, Ferrer E,Capdevila A, Arús C. "Towards a method for automated classification of1H MRS spectra from brain tumours". NMR Biomed. 11:177-191, 1998.

14.Martínez-Pérez, I., Moreno, A., Alonso, J., Aguas, J., Conesa, G.,Capdevila, A., Arús,C: "Diagnosis of Brain Abscess by MagneticResonance Spectroscopy. Report ot two cases". J. Neurosurg. 86: 708 –713, 1998.http://www3.interscience.wiley.com/cgi-bin/abstract/10006323/ABSTRACT

15.Underwood J, Tate AR, Luckin R, Majós C, Capdevila A, Howe F,Griffiths J, Arús C. "A prototype decision support system for MRspectroscopy-assisted diagnosis of brain tumours". http://www.ncbi.nlm.nih.gov/pubmed/11604803

16.van der Graaf M, Julià-Sapé M, Howe FA, Ziegler A, Majós C, Moreno-Torres A, Rijpkema M, Acosta D, Opstad KS, van der Meulen YM, Arús C,Heerschap A. "MRS quality assessment in a multicentre study on MRS-based classification of brain tumours". NMR Biomed. 2007 Apr 26; [Epubahead of print].http://www3.interscience.wiley.com/cgi-bin/abstract/114224252/ABSTRACT?CRETRY=1&SRETRY=0 This software uses 'ImageJ' to view MRI (Wayne Rasband [email protected] ). ImageJ is open-source.

39DSS INTERPRET 3.1


1.8 Disclaimer

WARNING - ACADEMIC PRODUCT FOR RESEARCH PURPOSES ONLY This software is an academic product and is not designed or intended foruse in the diagnosis or classification of any medical complaint or for anyother medical use. The user represents and warrants that it will not use orredistribute the Software for such purposes. This product is for medicalresearch purposes only. This software is provided AS IS, without a warrantyof any kind. ALL EXPRESS OR IMPLIED CONDITIONS, REPRESENTATIONS ANDWARRANTIES, INCLUDING ANY IMPLIED WARRANTY OFMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE OR NON-INFRINGEMENT, ARE HEREBY EXCLUDED. THE PROVIDERS SHALL NOT BELIABLE FOR ANY DAMAGES SUFFERED BY USERS AS A RESULT OF USING,MODIFYING OR DISTRIBUTING THE SOFTWARE OR ITS DERIVATIVES. INNO EVENT WILL THE PROVIDERS BE LIABLE FOR ANY LOST REVENUE,PROFIT OR DATA, OR FOR DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL,INCIDENTAL OR PUNITIVE DAMAGES, HOWEVER CAUSED ANDREGARDLESS OF THE THEORY OF LIABILITY, ARISING OUT OF THE USE OFOR INABILITY TO USE SOFTWARE, EVEN IF THE PROVIDERS HAVE BEENADVISED OF THE POSSIBILITY OF SUCH DAMAGES. The owners of theinitial prototype are the 10 partners of the EU project INTERPRET (IST-1999-10310), International Network for Pattern Recognition of Tumoursusing Magnetic Resonance.

Development after December 31st, 2002 is owned by the UniversitatAutònoma de Barcelona (UAB).

BY CLICKING THE ACCEPT BUTTON YOU ARE AGREEING TO THEABOVE STATED CONDITIONS

This software uses 'ImageJ' to view MRI (@author Wayne Rasband<[email protected]>). ImageJ is open-source.

HelpAndManual_unregistered_evaluation_copy

decision support system - interpret 3 -...

Documents