[Camino-users] Difference between tensor fitting models

[Ed Gronenschild]

Hi Phil,

Yesterday I wrote a reply e-mail to Danny. Unfortunately, I forgot to  
sent
it to the user list. So I repeat it here.

-----------
Dear Danny,

I'm somewhat confused now: according to the flow diagram in Fig 1
in the publication on RESTORE (Chang et al, Magn. Reson. Med, 53 ,
1088-1095 (2005)) the final tensor fit is a non-linear least-squares
fit with constant weight 1/sd**2. When using modelfit the sd is
supplied as argument in case of model = restore. But what about
model = nldt? I have not supplied sd as argument in that case.
Should I have done?
May that possibly explain the large differences in the results?
--------------

If have used modelfit with model = nldt which is also unconstrained
just like RESTORE. The contrained one you are referring to is
model = nldt_pos. So that will not explain the differences I observe.
That brings me back to the reply to Danny: should I supply sd as
option with model = nldt because RESTORE uses the sd in the final fit?

Ed


On 8 Feb 2012, at 18:43, Philip A Cook wrote:

> Hi,
>
> There's one other difference - restore uses an unconstrained  
> nonlinear fitting algorithm, whereas nldt will use an optimization  
> constrained such that the eigenvalues must be positive. This might  
> cause small differences in the result, but I would expect restore  
> and nldt to be very similar when the number of outliers is zero.
>
> You can check what restore is doing by looking at the exit code. If  
> you run dt2nii on your tensor data, it will produce an exit code  
> image. The code will be 0 if no outliers were removed, and 1000+  
> where outliers (code - 1000) outliers have been removed.
>
>
> Phil
>
> On Feb 8, 2012, at 10:17 AM, Daniel Alexander wrote:
>
>> That sounds right.  Although restore will often decide that one or  
>> two data points are outliers, so results won't be exactly the same.
>>
>> Danny
>>
>>
>> On 7 Feb 2012, at 13:45, Ed Gronenschild wrote:
>>
>>> Hi,
>>>
>>> I'm using modelfit to estimate the single tensor in the DWI images.
>>> Three options for the model were used:
>>>
>>> 1. model = ldt
>>> 2. model = nldt
>>> 3. model = restore
>>>
>>> I noticed differences between all results. My question concerns
>>> the difference between nldt en restore. To my opinion, restore is
>>> based on a non-lineair least squares technique to estimate the
>>> tensor and should be the same as nldt in the absence of outliers.
>>> Is that right?
>>>
>>> Kind regards,
>>> Ed
>>>
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>>
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