HI jesper,
Thanks very much for your reply.
Siewmin
Dear Siew-Min,
>
>>>
>>>
>>> I have to run BET with -F function to remove this problem to
>>> generate FA
>>> maps with no 0 intensity voxels, which is not the standard way of
>>> doing
>> BET on DTI data. May I ask again why is it that if I run BET this way
>> instead of the standard way -f, the resulting FA map will no longer
>> have
>> 0
>>> intensity voxels?
>
> not being a bet expert I am probably not the best person to reply,but
> I'll give it a stab.
>
> The -F switch is intended (mainly) for creating a brain-mask for fMRI
> analysis, i.e. to create the mask which determines which voxels to
> analyze and which to ignore. In that application it is important not
> to throw away any "good" voxels, and there isn't really any
> significant cost associated with including a few "bad" ones. Therefore
> one uses a quite liberal threshold (more liberal than the default 0.5)
> resulting in more of the brain being retained.
>
> I think that liberalness is what you see.
>
> Good Luck Jesper
>
>
>
>>>
>>>
>>> thanks
>>>
>>>
>>> Siemwin
>>> Dear Siew-Min,
>>>>
>>>> and everyone else concerned with FA<0 or >1.
>>>>
>>>> These values may seem disconcerting because we know that they are
>>>> "impossible", i.e. they come from voxels where the diffusion is
>>>> negative in some direction (indicating a black hole or some such
>>>> thing
>>>> to which things can diffuse and disappear).
>>>>
>>>> If instead you see it as having some set of data (your raw diffusion
>>>> data) affected by measurement error, and from these data you want to
>>>> estimate some parameters (let us say the diffusion in some
>>>> direction).
>>>> The true value will be positive (though it can be quite small in the
>>>> case of hindered diffusion in white matter), and then our estimate
>>>> will have some finite precision that depends on the quality of the
>>>> data (SNR, # of directions etc). So, typically we will not calculate
>>>> the "true" value, but some value that is drawn from a distribution
>>>> around the true value (known as the "sampling distribution").
>>>>
>>>> In some instances the true value is small (i.e. close to zero), and
>>>> then the sampling distribution (i.e. values that we may calculate)
>>>> will extend across zero. Then there is a chance/risk that we will
>>>> calculate/observe a negative diffusion value and subsequently an
>>>> FA>1.
>>>> This might happen e.g. in highly anisotropic areas in the brain.
>>>>
>>>> In other instances there isn't really a meaningful signal (e.g.
>>>> outside the brain) and the precision of our estimate will be very
>>>> poor, which again means that we may calculate/observe a diffusion
>>>> < 0.
>>>>
>>>> So, in short. We never get the "true" FA value. We get an estimate,
>>>> and that estimate can be more or less wrong (i.e. it can have better
>>>> or worse precision). Sometimes these "wrong" values are also
>>>> "impossible", but that doesn't really make them any more "wrong". It
>>>> just becomes a bit more obvious.
>>>>
>>>> By increasing the quality of our data (higher SNR and/or more
>>>> directions) the errors become smaller and smaller, and the chance/
>>>> risk
>>>> of calculating a negative diffusion becomes smaller. But it
>>>> doesn't go
>>>> away. It just becomes so small that it is very unlikely that we
>>>> calculate/observe one given that we are only looking at a few tens
>>>> of
>>>> thousands of voxels.
>>>>
>>>> Therefore, I would suggest stop worrying about negative FA<0 or FA>1
>>>> and accept them for what they are. Noisy estimates of some unknown
>>>> value in the range 0-1.
>>>>
>>>> Good luck Jesper
>>>>
>>>>
>>>> On 13 Mar 2009, at 15:11, Siew-Min Gan wrote:
>>>>
>>>>> Hi all,
>>>>> I noticed my FA map output from DTIFIT has white matter brain
>>>>> voxels of 0 and >1. I ran the following procedures to get the FA
>>>>> map.
>>>>> i.eddycorrect on the original DTI
>>>>> ii.bet the eddycorrected data.nii.gz (or the 1st B0) to derive the
>>>>> nodif_brain_mask.
>>>>> iii.feed the nodif_brain_mask, data.nii.gz,bvecs and bvals into
>>>>> DTIFIT.
>>>>>
>>>>> If the original data is ok, could a problem with the bvecs file
>>>>> result
>>>>> some brain voxels with 0 intensity (or intensity>1) in the FA map
>>>>> output
>>>>> from the DTIFIT procedure?
>>>>>
>>>>> Initially, I thought it may be a problem with the original DTI
>>>>> data,
>>>>> however, it is a standard MGH 30 dir sequence, and the gradient
>>>>> file
>>>>> is
>>>>> from the diffusion toolkit. I ran the data on another DTI software
>>>>> and
>>>>> the FA map output doesnt seem to contain 0 intensity voxels, but it
>>>>> may be
>>>>> a scaling issue??
>>>>>
>>>>> The solution I tried with FSL is to do BET using the -F function
>>>>> (normally
>>>>> used for 4D FMRI). The resulting FA map from DTIFIT only then
>>>>> will not
>>>>> contain 0 intensity voxels, but still have voxels of intensity>1. I
>>>>> notice
>>>>> this is not the standard way to process (BET) DTI data.
>>>>>
>>>>> I am concern about this as I have significant positive results from
>>>>> the
>>>>> tbss FA voxel analysis. I wonder if these results would be
>>>>> independent of
>>>>> the fact that some voxels in the FA maps are >1. Also, I could not
>>>>> analyse
>>>>> the tbss using the FA maps from the standard processing pipeline
>>>>> due
>>>>> to
>>>>> some brain voxels having 0 intensity.
>>>>>
>>>>> May I ask if using BET with -F is ok? Has anyone has come across
>>>>> this
>>>>> problem before, and may explain why there may be voxel with 0
>>>>> intensity or
>>>>> intensity>1 in the FA maps output?
>>>>>
>>>>> Many Thanks for some kind advice.
>>>>>
>>>>> Siewmin
>>>>>
>>>>
>>>
>>>
>>
>
>
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