[Insight-users] Registering US -> CT

[Luis Ibanez]

Hi Neilson,

No,
if you read a 2D image and you apply a 3D blurring, what you
will get is an Exception thrown. A 2D image is considered
to be a degenerate 3D image and therefore it is not suitable
for being the input of a 3D filter.

What you can do, however, is to read the 2D image, declare
a 3D image with thickness 5 pixels (or 2N+1...)  copy the
slice that you read into the middle slice of the 2N+1
image.  Then, apply a blurring filter.

You can set the pixel spacing Dz in this new 3D image in such
a way that Dz * (2N+1) = the physical thicknes of the sensitive
plane of your Ultra Sound acquisition probe.

In that way this new 3D image will be a better representation
of the physical reality that your Ultra Sound 2D image captured.


   Regards,


     Luis


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

> That sounds like a good idea.
> 
> If I read in the 2d image as 3D and applied a blurring filter would that 
> cause a 3D blurring ( blur the pixels outside of the plane )?  If so, I 
> could set the "thickness" by the setting the radius of the blurring mask.
> 
> Just a thought.
> Neilson
> 
> On Sep 16, 2004, at 6:47 PM, Luis Ibanez wrote:
> 
>>
>> 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
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>
>>
>>
>>
> 
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