MIDAS FAQ. 1) Supported MR Systems and SI Acquisition Protocols. 2) Recommended Computer Systems

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1 MIDAS FAQ Contents: 1) Supported MR Systems and SI Acquisition Protocols ) Recommended Computer Systems ) MIDAS Error Messages ) Miscellaneous Questions and Artifacts... 4 a) Why does my data look like a bunch of delta functions?... 4 b) Why do the spectra look good but the spectral fitting results look terrible?... 6 c) What to do when it doesn t work?... 6 d) Shading in the Image... 7 e) Streaks, black holes, and bright spots f) Multiple dropouts in the FITT result, even through the spectra look good g) The metabolite image is cut off and the water-si image is not registered h) The metabolite image only shows a small fraction of the brain i) The frequency map results from FITT has contours across it j) In a phantom study the peaks are negative ) Supported MR Systems and SI Acquisition Protocols MIDAS has been tested for data acquired on Siemens, Philips, and GE instruments at 1.5T and 3T MR systems. The suite of MIDAS applications can be adapted to support many MRSI protocols; however, there are several considerations: - The EPSI sequence (from U. Miami) is fully supported for 2D or 3D SI, and example processing files are available. - Other k-space waveforms, e.g. spiral, are not supported. This would require the user to develop the corresponding regridding method (i.e. EPSI3D replacement). - Conventional phase-encoded SI is supported. However, current experience on Siemens platforms indicates that it is difficult to export the raw k-space data, instead, only the data after spatial FT can be saved. While this can still be imported and processed by MIDAS, it limits the available processing options and there may be no advantage over using the SI reconstruction and analysis from the manufacturer. 2) Recommended Computer Systems MIDAS is distributed for Windows OS only. 1

2 A 64-bit CPU with at least 6 Gb RAM is required, and multiple cores recommended. The spectral fitting will take advantage of up to 16 cores (requires the full IDL license), in which case RAM should be at least 16 Gb. A large disk is required for these datasets. It is possible to use the free IDL Virtual (VM) license, but there are two limitations: 1) multicore processing is not supported. The speed improvement with a full IDL license on a multi-core CPU is substantial for the spectral fitting. 2) the IDL log window is not shown, meaning that some error messages get lost. 3) MIDAS Error Messages Most errors due to incorrect parameters or corrupted data are handled within each program with an error message being given to the screen if running from the GUI or logged to a file if running in batch mode. The error file will be located in the top level of the subject data directory. The naming is as _ErrorLog.txt. This is the first place to look if you get an error. Here are some other errors that can happen: IDLDE Error Message: Application(s): Description: Fix: Class edu.miami.midas.lib.midaslibrary not found Seen on starting IDL or MIDAStools. The PATH or Java bridge for IDL has not been set up. For Windows, this should be automatically set, in which case the installation may have failed or the system needs rebooting. See installation instructions for additional details. IDLDE Error Message: Application(s): Description: Fix: Refreshing Browser Seen on opening a study in the MIDAS Browser from any application. The study is locked by another application. This is a feature that is needed for parallel processing to avoid conflicts when updating the subject.xml file. This can also happen if an application had an error and exited without closing the subject.xml file. Go to the subject directory and delete the file subject.xml.lock 2

3 IDLDE Error Message: Application(s): Description: Cause: Fix: WIDGET_DRAW: Illegal keyword value for XSIZE. SID SID fails to start and IDL crashes. The MIDAS Toolbar must be closed and restarted. Incorrect default value for a window size parameter. This can occur when the option to save window settings on exit is set and the SID program was closed when one or more windows were absent or minimized. When trying to restore the window settings there is an invalid value. (Programming Note: The faulty parameters for the window placement is as shown on the 2 nd line here: im_base : im_draw : ctl_base : ) Delete the SID.INI file, which is located in your MIDAS home directory, e.g. C:\Documents and Settings\your_account\MIDAS\sid.ini Widget Error Message: Application(s): Description: Cause: Fix: Could not find trajectory file "." EPSI3D A EPSI k-space trajectory file cannot be found The EPSI data file you are processing was acquired with either the number of points or FOV that does not match with an existing trajectory file. 1) See EPSI3D_help file for details on trajectory files. 2) You can generally take the nearest equivalent FOV for the same number of sample points; however, processing in Batch mode is not possible unless the matching trajectory file is available. 2) Create a simulated trajectory (see help). 3) Measure the k-space trajectory and create this file. Error Message: Application(s): 3 Data File Not Found Any

4 Description: Cause: Fix: The data is missing or not in the location specified in the subject.xml file. The operator may have manually moved the data to another location. Although all paths are relative, the directory layout under each subject directory, and organization of the type of data within each directory, must match the information in the subject.xml file. Generally, if data is moved to another location it should be done on the subject level. Care must then be taken to ensure that the user s Project.xml file contains the correct path to the top-level of that project data. Error Message: Application(s): Description: Cause: Fix: On starting the MIDAS toolbar the following error appears in the IDL window: Starting C:\Midas\Bin\MIDAStools_v81.sav % Class edu.miami.midas.lib.midaslibrary not found % Execution halted at: GET_DEFAULT_DIRECTORIES % MIDASTOOLS % $MAIN$ IDL> All Toolbar won t start On some Windows systems the installation leaves the environment variables used by MIDAS unknown to the system. This has also happened after Windows updates. Force Windows to Source the environment variables. On Windows, Go to Computer, Properties, Advanced System settings, Environment Variables, and exit using OK. Nothing needs to be changed. 4) Miscellaneous Questions and Artifacts a) Why does the NAA/Cho ratio not match the spectrum? Historically MRS analysis for clinical studies has been visual, which has led to rule of thumb statements like "if Choline-to-NAA is > 2 then it is tumor". This type of analysis uses peak heights, but our quantitative analysis methods, as well as programs like LCModel, return values that are proportional to the concentration of the metabolite. This results in different relative values from the peak areas because the peaks for NAA and Creatine come from 3 protons in one molecule but the peak from Choline comes from 9. To convert relative concentrations to values 4

5 equivalent to those obtained using peak heights or peak areas then wherever you see Cho multiply by 3. Here is an example for normal brain: From the results of spectral fitting we can read the following data values: Choline = 6002 Creatine = NAA = Cho/NAA (concentration) = Cho/Cr (concentration) = Calculating the relative area, we get Cho/NAA (area) = 3*6000/29600 = Cho/Cr (area) = 3*6000/23166 = The values for the ratio of the areas now agree qualitatively with what we can estimate from the spectrum. b) Why does my data look like a bunch of delta functions? Your results just look totally scrambled, and spectra or time data look something like this: 5

6 This is a byte-order (i.e. SUN vs Intel CPU byte order) problem. Most processing modules only support the byte order of the computer on which the processing is run. Exceptions to this include FDFT and SID. Solutions are: a) Check the Byte_Order parameter in the Subject.xml file. The entry under the Series level should match that used for data acquisition. If changes are done during processing, e.g. the data is shipped off to another computer for one stage of the processing, then the corresponding Process node parameter value should be set accordingly. In some situations it may be possible to edit this parameter (big-endian or little-endian). b) Do all processing on the same type of computer as the data was acquired; c) Apply your own byte-order swapping to the data, and edit the byte-order parameter in the subject.xml file appropriately. c) Why do the spectra look good but the spectral fitting results look terrible? Well this is a difficult one without any specific answers. A good place to start is to look at the spectra (SID or Viewer) and then go to the viewer widget in FITT. Here are a few things to look out for: - The complex conjugate and spectral flip settings in FDFT. These must be set to provide the correct relationship of the real and imaginary component of the spectrum so that it matches with the FITT spectral model. See the FDFT documentation for an additional description of this. - Check that the spectral selection is correct. This is also affected by the complex conjugate issue above. - If the spectra look OK, look at the integrated spectral image for one of the peaks in SID. SNR may look bad if the smoothing or LITE was not performed. - That the spectral basis functions are present and look right. Use the FITT viewer for this. d) What to do when it doesn t work? The processing pipeline is complex, and if something doesn t work it can be difficult to determine if it s the data; an incorrect processing parameter; a limitation of the processing; or a bug. Places to start are: 1) Look at the data in SID. SID can also display the SI data at the various preprocessing stages before FT reconstruction. Generally, you need to understand what processing has been done at each stage to interpret the result. E.g. the k-space data can be viewed by creating in integration image from the 1 st point of the FID. This should be maximum for the center point (N/2+1) of the center slice (e.g. slice 9 of 16). 6

7 The following figure shows the integrated image result and the FID, with complex plot option set: 2) Look at the Subject.xml file. Using any XML reader, e.g. Explorer, Firefox etc, this file can be viewed and the parameter values examined. Most are self-explanatory. In some situations it may be necessary to edit the parameters using any text editor. One package we like for this is Notepad++. 3) Check the processing file(s) and parameters. For applications like FDFT, which has many options, it is worth spending time looking over the processing parameters in the widget and looking at the help file for a description of their use. e) Shading in the Image E.g. It should look like the image on the left but you get the one on the right: This finding can occur from several sources, including RF receive or transmit variations, eddy currents, or B0 inhomogeneity. Another cause is an error in the phased-array coil combination, which can occur if there is a mismatch in spatial coordinates between the SI_Ref water reference image and the metabolite images. In the result shown above there was a mismatch in the spatial flip parameters (in X dimension) between the SI_Ref and SI spatial FT, resulting in the Phase and Magnitude maps being swapped Left-Right relative to the SI data. 7

8 f) Streaks, black holes, and bright spots. Observation: E.g., the following shows the choline fit result, the integral over a region near choline, and a plot from one of the bright spots: Reason: Water suppression and subject motion. Water may be coming from out-of-slice regions and be significantly frequency shifted, and particularly from the mouth and eyes where motion often occurs. This can cause large baseline variations and result in failure of the spectral fitting. Solutions: i) Improve use of the spatial saturation slabs. See the sequence document for some guidance on this. ii) Improve shimming. Some guidelines for whole-brain shimming are provided in the EPSI documentation and in one of the MIDAS newsletters. iii) Rerun the FITT program in interactive mode to redo fitting at the bad fit results. See the FITT help document for how to do this. Sometimes, by rerunning the fitting several times even large baseline artifacts can be well fit. g) Multiple dropouts in the FITT result, even through the spectra look good. Observation: E.g. a NAA image with holes and a spectrum from one of the black spots: Reason: The spectral fitting may fail when the phase of the spectrum is very close to 180 o. Solutions: 8

9 1) The FT processing should be changed to result in the phase of the SI data being roughly positive. This can be affected by multiple steps, including the use of ECC processing, and if ECC is used the phase of the SI_Ref data, and the scaling or phase correction used in the FDFT program. The recommended approach is to change the sign of the Scaling_Factor parameter in the SI_Spectral processing step. Typically this is 1.0 or ) The FITT program includes an option to perform a phase correction after the 2 nd iteration. This is under Global fitting Parameters Apply Phase0 Unwrap on 2 nd Iteration, and this should be turned on and the spectral fitting rerun (however, see below for floating point error with the phase unwrap function). h) The metabolite image is cut off and the water-si image is not registered. Reason: The between-series spatial registration likely failed (MSREG) and the brain mask used for spectral fitting is incorrect. Possible causes for registration failing include significant differences in the bias field or appearance of the SI_Ref and MRI_T1, an incorrect FDFT parameter, and use of a sagittal T1 acquisition. For this last cause see the item on Sagittal acquisitions for details. Possible Solutions: i) Check the Transpose and Transpose Parameters options in the FDFT spatial FT processing parameters (transpose_params in the Proc file). For the volumetric EPSI (Siemens) the Transpose should be set to swap X and Y, but the Transpose_Parameters is NO. ii) Run MSREG with the Initial estimate option turned off. Before reprocessing, it is necessary to rerun the SI processing. iii) Check your MRI_T1 image contrast. This needs to be suitable for the inter-series registration and segmentation. The image quality successfully used at the Miami site is shown on the right: The following is an example that caused problems: 9

10 i) The metabolite image only shows a small fraction of the brain. Observation: The metabolite image is incomplete, and looks like the image shown here: If you look at the Brain Mask map you will likely see the same shape. Reason: This can be caused by inaccurate segmentation, similar to the previous example. Solution: As above, modify the MRI_T1 sequence to image quality and image contrast. j) I have a sagittal T1 and the segmentation and registration failed. Observation: The tissue segmentation results are grossly incorrect and the spatial registration failed. Reason: The BET algorithm used in the FSL/FAST segmentation has failed because the sagittal acquisition includes a large region from the neck in the image. Solution: i) Include the TRUNCATENECK procedure in the processing pipeline. This is applied immediately after the volumizer. This deletes signal from the neck region. ii) Try the IDLSEG program for segmentation (instead of the SEG_FSL4_T1 function) and include Iterative BET. This uses a published method for improving the BET result. k) The frequency map results from FITT has contours across it Observation: The frequency maps have sharp edges within the image, as shown in the figure on the left, whereas I would expect it to be smooth, as on the right. Note: These frequency maps can be displayed in SID but you need to use a very narrow lookup table width and set the level very high to see the details. 10

11 Reason: The frequency map result, e.g. the NAA_Freq data, from spectral fitting gives the ppm value used for the final fit, after correction for B0 inhomogeneity. If the B0 correction for the initial values is turned on, then the data will already have been frequency corrected to the nearest spectral point, and the result will then be the difference between the fitted frequency and the position AFTER b0 correction. The result is that it appears to have been discretized, as in the 1 st image. If you want to see the actual frequency, then the B0 initial value should be OFF. With the standard processing where the ECC correction is used to do the B0 correction, this is a viable setting. A second way this can happen even if the B0 correction in FITT is turned off is if FITT was run twice and the correct spectral data for B0 shifts option had already been applied on the first run. Once again, the B0 correction applied in FITT is discrete, i.e. to the nearest frequency point, and the second run essentially determined differences from actual frequency to the previous correction to the discrete sample point values. Normally this observation is of no importance. Perhaps the only example where this should be considered is if the frequency shifts are being used for temperature measurement. Solution: Turn of initial value B0 correction in FITT and rerun the complete processing so that the FT for the SI is repeated to recreate the.sid data with B0 shifts intact. l) In a phantom study the peaks are negative Observation: In a study of a phantom object some of the peaks, typically choline, appear of opposite phase to the other peaks. Reason: The data was taken with the lipid inversion nulling pulse on. This needs to be turned off for phantom studies unless the T1s of the phantom have been appropriately adjusted. Solution: See the pulse sequence documentation for information on how to turn off the inversion pulse and retake the data. Note that TR values may also have to be increased for phantom studies. m) What are the horizontal/verticle striations in my metabolite maps? Observation: Here are some examples: 11

12 Reason: These are always associated with unsuppressed water or lipid signal, and typically indicate motion that is enhanced by the reduced k-space and GRAPPA reconstruction. The lipid artifacts can also be associated with a spatial error in the lipid mask. Problems from lipids (left image) are often seen coming from the back of the head, where there is a strong subcutaneous lipid signal. The middle image is likely due to unsuppressed water around the eyes coupled with eye movement. The pattern seen in right image appears to be due to horizontal motion, exaggerated by the GRAPPA reconstruction that occurs in that dimension. Solution: The solution is, as always, to get better shimming and less head motion. 12