Making a user-friendly pipeline that performs the typical steps in preprocessing is a goal of many projects and grants. Unfortunately, preprocessing is still a fairly involved process, which is part of why this module's tutorials don't directly address it. There are many, MANY things that can go wrong or simply be performed incorrectly. There are preprocessing pipelines that exist (BRAINS Autoworkup, Freesurfer, SPM, etc) but they rely on the user having a substantial amount of neuroimaging knowledge and can't be described as "Idiot-proof". Alternately, there are modules / programs that can be used to perform most of the steps, but again, they may not be ready for clinical / non-expert use.

1) Many typical preprocessing steps can be performed from Slicer, however, there is no official or unofficial preprocessing pipeline in Slicer3.

2) The lesion applications don't currently support dicom, so yes, they need to be converted from dicom to something else such as nrrd.

3) Brain masks are a normal output of preprocessing pipelines. However, there are many applications that can create a brain mask.
a) The SkullStrip module which should be available as a Slicer extension (from within Slicer, View-> Extension Manager->Next->Select SkullStrip->Download&Install->Finish.
b) FSL has the Brain Extraction Tool (BET and BET2): http://www.fmrib.ox.ac.uk/fsl/bet2/index...
c) SPECTRE, which is being integrated into Slicer but to use it now is an involved process.
d) The BRAINS tools out of Iowa include a command line skull stripping tool called BRAINSMush. There's currently no binary releases but a stand-alone version can be built (Look for BRAINSMush on that page)

4) The model files consists of the lesion and non-lesion centroids, the distributions of the lesion and non-lesion data sets after distance thresholding, and finally the Support Vectors separating the two classes.

The model files (lesionSegmentation.model, svm.model), trained solely on lupus data, are available in the tutorial data set: http://www.nitrc.org/frs/download.php/86... It has never been tested on TBI data, or anything but lupus. It may work for you, but I have no data one way or the other.

If you want a new model file trained on TBI data that is, unfortunately, an involved process. The source code used to do it has not been released, mainly because it is a combination of scripts and custom programs with minimal documentation. It requires at least 8 patients (preferably 10) with T1, T2, FLAIR, and hand traced lesions (tracings should be as good as humanly possible). It then requires a large amount of processing. Originally the plan was to release model files for multiple disorders but funding was not approved. Support for more disorders may happen at some point, but likely not within 6 months.

A much more user friendly option would be the white matter lesion (WML) segmentation module. It is available for slicer, allows you to train your own classifier, and has a tutorial on its use. I don't know how it's segmentations compare on the same data as I am currently working on the comparison. Their published results are good.

5) The reference subject in the Intensity Standardization is just the scans whose intensity profile everything is being matched to. If you are going to use the lupus model file then it's best to continue to use the lupus002 files.

6) I will point out that when dealing with longitudinal data the longitudinal images should always be co-registered to the baseline T1.




The input to the longitudinal lesions comparison is the two time points lesion masks. The masks do not have to be the output of PredictLesions; they could be hand traced or produced by a different segmentation method. They DO have to be aligned.

7) Change tracker is specifically for tumors. It also includes some segmentation functionality that is not present in the Longitudinal Lesion Comparison (LLC). All the LLC does is take two label maps and produce a new label map with 3 possible values. One for gained, one for lost, and one for unchanged. Then the Compare View functionality in slicer can be used to examine multiple images and slices with that label map on top.

8) This is also a very involved question. GTRACT has tools that will register a B0 image from a DWI sequence to a T1 and output a transform, which you can then apply to the lesion image (assuming the lesion image was generated from images coregistered to that T1) which will put the lesion image in the same space as the diffusion data. However, you need to apply motion correction and eddy current correction to your DWI data, and you need to throw out bad gradients (Something DTIPrep can do). Working with diffusion data can be complicated. There are a LOT of issues that may come up, which is part of why there are no "fire and forget" tools.

9) If you mean within the tools I've written then no group comparison is possible. If you load the longitudinal difference images and the images you want statistics from into something like matlab, python, or ruby, a group comparison is possible but you have to write it. Alternately you can write the data out in a form SPSS can read and analyze it that way.