[Mristudio-users] gradient table rotation for multiplerepetition data set

[susumu mori]

On Wed, Jul 28, 2010 at 4:01 PM, Jan Tillema <Jan.Tillema at cchmc.org> wrote:

>  I have a similar question.
> We have dataset of subjects before/after intervention, we have 15 dir DTI
> and 3D T1 data (GE) with same parameters both times, but orientation not
> exactly the same (clinical protocol). The brains are not normal at baseline
> with enlarged ventricles that may have changed minimally (due to treatment,
> e.g. smaller size). Normalization is not a possibility d/t abnormalities on
> brain to begin with, we just want to compare before/after. We are
> investigating the effect on white matter and would like to get best approach
> to compare basic measurement before/after. We have manual (blinded) ROI
> showing differences before/after, but want to make sure we are in the exact
> same ROI. To co-register seems the best way, but tricky as the structure of
> the brain is not exactly the same. Any suggestions?
>
> 1) would it be best to co-register DTI to T1 at both time points and then
> to co-register T1 in time point A to B followed by change of DTI with same
> matrix? If so, I assume this would have to be done with DiffeoMap, what
> would be the steps to do so?
>

Unless there is a reason that you want T1-DTI be registered (e.g. ROI drawn
in T1, FA measured in DTI), I think you can directly register DTI to DTI.
Yes, DiffeoMap should be able to do it.


> 2) is co-registration to "dangerous" for bias as there is the possibility
> that the brain is not exactly the same before and after (ventricle size
> change)
>

First you have to decide if you want only linear transformation adjusting
the overall head positions or do non-linear to remove the tissue deformation
due to the surgery. I suspect you want the former (I think the latter is
difficult).

Linear transformation is mostly driven by brain boundary. So, you can assume
that as long as the tissue deformation is mostly inside the brain and the
brain boundary shape remains the same, the linear transformation should be
able to match the two brains. However, we have seen not-so-perfect linear
registration even between two images from the same person. Reason could be;
the initial alignment was way off, there is an intense sinus signal in one
of them, etc. In the end, it is your subjective judgment if the linear
registration is satisfactory. If there is a good quantitative way to judge
the alignment, we would have used such a metric to drive the linear
registration.

In your case, you may want to try the landmark function of DiffeoMap. You
need at least 3 point landmarks to define the corresponding structures
between the two images. Unfortunately there are not many point landmarks
inside the brain. The exceptions are AC and PC. The third point could be
some easy-to-define structures in the brainstem at the mid-sagittal level
such as the junction of the pons and midbrain. If you can clearly find these
3 points, place these landmarks in both images and use rigid alignment.

Let us know if this approach works.


Thanks,
>
> JMT
>
> >>> susumu mori <susumu at mri.jhu.edu> 7/28/2010 3:23 PM >>>
>
> Usually, we first calculate tensor and then register the tensor maps with
> proper reorientation, rather than register the raw DWIs with rotated
> gradient tables.
>
> It is possible to do your approach, but tensor registration would be
> easier, I think.
>
> On Wed, Jul 28, 2010 at 2:40 PM, Rajagopalan, Venkateswaran <
> rajagov2 at ccf.org> wrote:
>
>>
>> Dear All,
>>
>> We have diffusion data set acquired with 5 trails/repetitions(in single
>> time point/cross sectional) for each and every subject. After eddy current
>> correction i would like to do gradient table rotation. I am registering all
>> the 5 trails to the b0 image. Since after eddy/motion correction i have
>> affine matrix for all the 5 trails i.e i have 13*5 affine matrices so I am
>> wondering which set of affine matrices i.e first 13 affine matrices or
>> affine matrices from second trail/repetition should i use to rotate my
>> gradient table.
>>
>> Thanks
>>
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