[Camino-users] Difference between tensor fitting models

[Philip A Cook]

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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