[Mristudio-users] LDDMM for Hematoma

[susumu mori]

Hi Jeff,

Here is what I meant;

Suppose you have 10 images to normalize and there is 1 image failed to
linearly normalize. Also suppose you used b0 images for linear normalization
but it somehow didn't work well for the failed one. Then, it's ok to try
skull strip, mutual information, or use dwi for re-process the failed one,
because the linear normalization is just for the initialization.

On the other hand, for LDDMM, if you decide to use FA/trace, you need to use
the same contrasts for all 10 images and it's not good idea to change the
contrasts such as FA/b0, trace/b0, etc.

Susumu

On Thu, Sep 8, 2011 at 11:02 PM, Jeff Sadino <jsadino.queens at gmail.com>wrote:

> Hi Dr. Mori,
>
> Thank you for your quick and detailed reply.  It is good to know I am on
> the right track by using the DWI image.  I am confused by this line: "You
> can't change the contrast ad hoc for LDDMM."  Does this mean that I can only
> use the B0, Trace, and FA images whenever I do the LDDMM registration?  I
> skullstripped both the template and subject DWI images.  My thinking was to
> use the skullstripped DWI images to drive the linear transformation, and
> then (assuming a good AIR registration) use the linearly-registered subject
> DWI image and the template DWI image to get a Kimap file that I could apply
> to the rest of the linearly-registered subject images.  I would do this for
> this entire cohort of 17 subjects.
>
> Or am I to read your response to mean that I should only do a linear
> registration on the subjects and then stop there without doing an LDDMM
> transformation?  Or do I use the DWI image for the AIR, and then use the FA
> and Trace for the LDDMM?  Some of the subjects have hematomas in about
> 10-15% of the brain, so it is a significant region.
>
> Thank you very much!
> Jeff
>
> On Thu, Sep 8, 2011 at 3:33 PM, susumu mori <susumu at mri.jhu.edu> wrote:
>
>> Hi Jeff,
>>
>> Before you start LDDMM, you have to make sure the linear registration
>> (AIR) is very good. This is similar to non-liner fitting of a XY plot, which
>> always requires you to do linear fitting first to get a good initial
>> estimate of the non-linear fitting parameters.
>>
>> There are cases when AIR fails often due to high intensity inside or
>> outside the brains of patients. In this case, changing the contrast, as you
>> did could be one solution. You can't change the contrast ad hoc for LDDMM
>> but for linear transformation, it is just an initialization. So, all you
>> want to do is to bring the two brains as close as possible. So, it's ok to
>> use different approach for different data. What we often do is, 1) try
>> mutual information,  2) use landmarks, or 3) do skull strip.
>>
>> Most important things is, to make sure that the linear transformation is
>> good. Be careful about the angle of the brain and the brainstem. The
>> sagittal rotation is one of the most difficult parameters, while X, Y, Z
>> translation and coronal / axial rotation is usually easy to register. If the
>> sagittal angle is not satisfactory, you should try different approaches.
>>
>> Also, we recently start to use 9-mode affine (no shearing) very often,
>> which is now available in the latest DiffeoMap.
>>
>> Susumu
>>
>> On Thu, Sep 8, 2011 at 7:59 PM, Jeff Sadino <jsadino.queens at gmail.com>wrote:
>>
>>> Hello again DTI Experts,
>>>
>>> I have a group of patients that have suffered trauma to the head that I
>>> am putting through the LDDMM pipeline.  A problem I ran across during AIR in
>>> DiffeoMap is that since the trauma is causing a hematoma in the B0 image,
>>> the registration is very poor.  On one subject, I performed DiffeoMap AIR
>>> using the Mean DWI image and the registration came out pretty good.  I am
>>> wondering if this is a valid approach to perform AIR registration.
>>>
>>> For the LDDMM registration, usually I use the FA and the Trace image to
>>> drive the Kimap.  However, I think I will get the same poor results.  Should
>>> I use just the DWI image to drive the LDDMM registration?  There would be no
>>> need to do "Intensity Normalization" on the DWI image, correct?
>>>
>>> Thank you very much,
>>> Jeff Sadino
>>>
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>>
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