FDT utilities

Vecreg - registration of vector images

After running dtifit or bedpostx, it is often useful to register vector data to another space. For example, one might want to represent V1 for different subjects in standard space. vecreg is a command line tool that allows to perform such registration.

Vector images cannot be registered by simply applying a transformation (as calculated by, say, FLIRT) to every voxel's coordinates. The corresponding vectors have to be reoriented accordingly (see D. Alexander 2001, IEEE-TMI 20:1131-39).

vecreg performs this operation for you. The image below shows the effect of applying vecreg (right) to the V1 image on the left, compared to simply applying voxelwise transformation (e.g. using applyxfm4D) to the vectors (centre).

Info

vecreg does not calculate a transformation, but simply applies a given transformation to the input vector field. vecreg can apply a linear transformation calculated with FLIRT, or a non-linear transformation calculated by FNIRT.

Types of input that may be used for vecreg from DTIFIT: V1,V2,V3,tensor from BEDPOSTX: dyads1, dyads2, etc.

Command line options

vecreg-i<input4D>-o<output4D>-r<refvol>[-t<transform>]


Compulsoryarguments(YouMUSTsetoneormoreof):
-i,--inputfilenameforinputvectorortensorfield
-o,--outputfilenameforoutputregisteredvectorortensorfield
-r,--reffilenameforreference(target)volume

Optionalarguments(Youmayoptionallyspecifyoneormoreof):
-v,--verboseswitchondiagnosticmessages
-h,--helpdisplaythismessage
-t,--affinefilenameforaffinetransformationmatrix
-w,--warpfieldfilenamefor4Dwarpfieldfornonlinearregistration
--rotmatfilenameforsecondaryaffinematrix
 ifset,thiswillbeusedfortherotationofthevector/tensorfield
--rotwarpfilenameforsecondarywarpfield
 ifset,thiswillbeusedfortherotationofthevector/tensorfield
--interpinterpolationmethod:nearestneighbour,trilinear(default),sincorspline
-m,--maskbrainmaskininputspace
--refmaskbrainmaskinoutputspace(usefulforspeedupofnonlinearreg)

Qboot

qboot is a command line tool that allows estimation of diffusion ODFs and fibre orientations from them. Its output can be used as an input for probtrackx in order to perform probabilistic tractography.

ODF estimation is performed using a real spherical harmonics basis. Fibre orientations are estimated as the local maxima of the ODFs. Both deterministic and probabilistic estimation can be performed. For the latter, residual bootstrap is performed to infer on the ODF shape and obtain a distribution of fibre orientations. For more details on the implementation see Sotiropoulos2011 (S.N. Sotiropoulos, I. Aganj, S. Jbabdi, G. Sapiro, C. Lenglet and T.E. Behrens, "Inference on Constant Solid Angle Orientation Distribution Functions from Diffusion-Weighted MRI", p.609, Quebec, Canada, OHBM, 2011).

qboot allows reconstruction of q-ball ODFs (Tuch DS, MRM 2004), CSA ODFs (Aganj I et al, MRM, 2010) and variants of them, obtained via Laplacian sharpening and Laplace-Beltrami regularization (Descoteaux et al, MRM, 2007). Both spherical harmonic coefficients of the reconstructed ODFs and fibre orientation estimates may be returned as output. A real spherical harmonic basis is employed (Aganj I et al, MRM, 2010).

Input files for qboot : Similar to dtifit and bedpostx, qboot needs a 4D data file, a binary mask_file, a bvecs and a bvals file.

Usage

qboot--help(forlistofoptions)


Compulsoryarguments(YouMUSTsetoneormoreof):
-k,--dataDatafile
-m,--maskMaskfile
-r,--bvecsbvectorsfile
-b,--bvalsbvaluesfile

Optionalarguments(Youmayoptionallyspecifyoneormoreof):
--ld,--logdirOutputdirectory(defaultislogdir)
--forcedirUsetheactualdirectorynamegiven-i.e.don't add + to make a new directory
  --q File provided with multi-shell data. Indicates the number of directions for each shell
  --model Which model to use. 1=Tuch'sODFs,2=CSAODFs(default),3=multi-shellCSAODFs
--lmaxMaximumsphericalharmonicoderemployed(mustbeeven,default=4)
--npeaksMaximumnumberofODFpeakstobedetected(default2)
--thrMinimumthresholdforalocalmaximatobeconsideredanODFpeak.ExpressedasafractionofthemaximumODFvalue(default0.4)
--ns,--nsamplesNumberofbootstrapsamples(defaultis50)
--lambdaLaplace-Beltramiregularizationparameter(defaultis0)
--deltaSignalattenuationregularizationparameterformodels=2,3(defaultis0.01)
--alphaLaplaciansharpeningparameterformodel=1(defaultis0,shouldbesmallerthan1)
--seedSeedforpseudo-randomnumbergenerator
--gfaComputeageneralisedFA,usingthemeanODFineachvoxel
--savecoeffSavetheODFcoefficientsinsteadofthepeaks.WARNING:Thesecanbehugefiles,pleaseuseafewbootstrapsamplesandalowlmax!
--savemeancoeffSavethemeanODFcoefficientsacrossallsamples
-V,--verboseSwitchondiagnosticmessages
-h,--helpDisplaythismessage