Differences

This shows you the differences between two versions of the page.

--- fmriquality [2010/06/17 14:50] – added runtime concerns for some options jochen
+++ fmriquality [2010/06/17 19:09] – integrated the information in the QA page jochen
@@ Line 14: / Line 14: @@
 ===== Requirements =====
-To run the fMRI quality checking function, the images need to be in one of the functional imaging data formats currently supported by the xff class (Analyze/NIftI, FMR/STC, VTC).
+To run the fMRI quality checking function, the images need to be in one of the functional imaging data formats currently supported by the xff class:
+  * BrainVoyager's FMR (STC) format
+  * BrainVoyager's VTC format
+  * SPM's Analyze or NIftI (HDR/NII) formats (either as series of 3D volumes or a single 4D file)
 ===== Function reference ('help fmriquality') =====
@@ Line 47: / Line 50: @@
 ===== Usage =====
 The most basic (and pre-configured) way of running fmriquality is by simply passing in the filename(s) or object of the run to check:
-  * using Analyze files: <code matlab fmriquality_example1.m>qas = fmriquality(findfiles(sessionfolder, '*.img'));</code>
+  * using Analyze files: <code matlab fmriquality_example1.m>qas = fmriquality(findfiles(sessionfolder, '*.img', 'depth=1'));</code>
   * using a BrainVoyager QX FMR file: <code matlab fmriquality_example2.m>fmr = xff('*.fmr', 'Select FMR for which you want to check the data quality...');
 qas = fmriquality(fmr);</code>
@@ Line 62: / Line 65: @@
 % running fmriquality
 fmriquality(qafiles, qaopts);</code>
+  * create a quality assessment struct for all VTCs in an MDM, applying robust temporal filtering: <code matlab fmriquality_example4.m>% loop over VTCs in MDM
+for study = 1:size(mdm.XTC_RTC, 1)
+    % perform quality assessment
+    q = fmriquality(mdm.XTC_RTC{study, 1}, struct('robfilt', true));
+    % store as VTCNAME_qasheet.mat
+    save([mdm.XTC_RTC{study, 1}(1:end-4) '_qasheet.mat'], 'q');
+end</code>
+In case the QA sheet is not shown by the function (flag ''.qasheet'' set to ''false'', which is the default), the returned struct can be visualized via a call to ''fmriqasheet'':
+<code matlab fmriqasheet_example.m>% load a QA sheet (contains variable/struct q !)
+load CPU4212_run3_qasheet.mat
+% open QA sheet
+fmriqasheet(q);</code>
 If the ''.qasheet'' option is not set to true, the returned variable can later be passed to [[fmriqasheet]] manually (for scripted QA-ing). This variable is of type struct and contains (at least) the following fields:
@@ Line 77: / Line 98: @@
 Please be aware that the first argument **must** be a list of filenames; so, even for a single NIftI ([[xff - NII format|NII]]) file, a cell array must be passed in:
-<code matlab fmriquality_example4.m>% using a single NII file
+<code matlab fmriquality_example5.m>% using a single NII file
 qas = fmriquality({'vols.nii'});</code>
 Also, please note that motion parameter estimation requires between one and several minutes (depending of the length of the run and whether or not robust motion estimation is employed). The **runtime also easily increases tenfold when robust temporal filtering is used**. In other words, these options are more useful and meant for situations where the fmriquality function is part of a larger script that automatically checks the quality of all incoming data instead of being used from the command line in a more interactive way!
+Finally, at the moment, the "problem detection" thresholds (to mark a volume as outliers) are fixed, but are likely to change in a future version.