[Insight-users] Registering US -> CT

[Luis Ibanez]

Hi Neilson,

That's a good point. You are right,
in this case, since you have scattered slices
from Ultra Sound it is not possible to make sure
that every point will be inside an image.

One option could be to associate a "thickness"
to every US slice, you can probably figure out
one that makes sense from the point of view of
the physical acquisicion process.

That thickness could be used for defining
regions of space where a point will be considered
to be "inside" one of the US images.

The more US image you have, the more chances
there are that this could lead to a reasonable
registration.

Note that this requires you to do more modifications
on the ImageMetric class.




    Regards,


       Luis



--------------------------------
N.E. Mackenzie Mackay wrote:

> I was thinking the same thing.
> 
> The only thing I was worried about is using the method in 3D.  If some 
> of the points don't map onto the the US image will the registration 
> method ignore those points or will it throw an error?
> 
> On Sep 14, 2004, at 10:33 PM, Luis Ibanez wrote:
> 
>>
>> Hi Neilson,
>>
>> MutualInformation is mostly a region-based image metric.
>> This means that its value gets better when the overlap
>> between matching regions of the image is large. Mutual
>> Information is not particularly well suited for matching
>> thin structures since thir random sampling is unlikely
>> to select many pixels belonging to those structures.
>>
>> In that sense you probably shouldn't expect much from Mutual
>> Information for registering Bone, since bone structures are
>> mostly shell-like and they don't fill large regions of space.
>> E.g. large bones have their layers of cortical bone with high
>> calcifications but their width usually cover just a couple of
>> pixels in a CT scan.
>>
>> Given that you seem to have segmented the bone from the CT scan,
>> it is probably worth to try a Model-to-Image registration approach.
>> This can be done by taking points on the surface of your bone
>> segmentation, and/or from a band around that surface, and using
>> them to match the intensities (and structure) of the same bone as
>> seen in the UltraSound images.
>>
>> Could you post a couple of the US images ?
>>
>> (e.g. you could put them in www.mypacs.net and let us know their
>>  image ID).
>>
>>
>> Depending on how the bone structures look like on the US image
>> there may be different possible metrics to try in a PointSet to
>> Image registration.
>>
>>
>> BTW, when you start working in 3D, don't attempt to use Rigid
>> transforms until you have manage to tune all the other parameters
>> of the registration to work with simple translation transforms.
>> It is more effective to deal with a single issue at a time.
>>
>>
>>
>> Regards,
>>
>>
>>     Luis
>>
>>
>>
>> -------------------------------
>> N.E. Mackenzie Mackay wrote:
>>
>>> Hi,
>>>     I have tried for the last while to get a single ultrasound image 
>>> to register to a CT volume.  Specifically try to get the bone of an 
>>> ultrasound image and bone of the CT to register together.  Up to now 
>>> I am having quite some trouble.
>>> I have been able to segment the bone from CT and give an estimate on 
>>> where the bone is in the ultrasound.  I am now trying to register 
>>> those two images.
>>> This is what I am using:
>>> MattesMutualInformationImageToImageMetric - decided to use this 
>>> because the registration was of two different modalities.  Couldn't 
>>> use feature registration becuase to hard to segment ultrasound correctly
>>>     - using 50 bins and %20-%100 of samples still doesn't give 
>>> adequate results.
>>> linearInterpolateImageFunction
>>> RegularSetGradientDecentOptimizer
>>> Euler3DTransform
>>> Both images ( 3D CT and US ) are normalized before the registration.
>>> I have used a maximum step ranging from 0.5-6.  And a min step of 
>>> 0.005 - 1.
>>> I have a good initial guess ( maximum 2cm away from correct with 
>>> about 0- 30degrees of rotation)> I tested out the registration method 
>>> in 2D and have had success.  When I use the exact same variables 
>>> applied in 3D the registration is poor.
>>> Does anyone have any suggestions?  I would be happy to provide a 
>>> couple of images or actual code to show you what I am dealing with.
>>> Neilson
>>> _______________________________________________
>>> Insight-users mailing list
>>> Insight-users at itk.org
>>> http://www.itk.org/mailman/listinfo/insight-users
>>
>>
>>
>>
>>
> 
>