[Insight-users] mutual information registration

[Dill, John]

Hi Luis,

> 
> Averaging 500 iteration of the metric is an overkill.
> You shouldn't be needing to do this.

I sure hope so ;)

> Are you computing
> this average for the same set of transform paramters ?

I average over each transformation vector, so I evaluate the metric 500
times, and average the metric for each transformation vector.  In my range,
they go like

[x y r]
[-10 -10 -5] each one of these is 500 iter of metric for the average
[-10 -10 -4]
[-10 -10 -3]
etc...

I did a plot of the cost function with respect to a line in the
transformation space (neglicting rotation) and plotted the cost, and found
that it was very noisy (for one evaluation of the metric).  I then started
averaging  from 100 iterations, and kept increasing in increments of 100
until I found a nice curve.  At 500, the curve was very smooth.  Then I did
all my registrations with those parameters, and got good results for about
90-95% of the test cases.  I decreased the avg iterations down to 400, and
evaluated over a couple of patients and got results that are noticeably
different, which differed from the original between 2-6 pixels in either
direction (and worse registrations than 500 avg iterations).  It's a bit
unusual why I seem to need this many also.

However the image registrations are quite more complicated than the trivial
test cases given in the toolkit.  There are several sources of errors and
problems which make this more difficult.  The first is that since these
images are projections, there are out of plane rotations which can not be
corrected for.  Since patient immobilization is not perfect, a 1 or 2 degree
out of plane rotation can cause difficulties for determining what the
correct registration is.  I might get the top half of the skull lined up,
but the jaw is off due to this out of plane rotation.

Second, there are large artifacts present in the portfilm that are not their
in the CT due to patient immobilization.  There is typically a large high
intensity white artifact coming from the table underneath the patient in the
portfilm, sometimes taking up 30 to 40 pixels in height, and almost the
whole width in the portfilm (size of images is about 280x280).

Lastly, there is misinformation along the borders of the images.  Since the
drr and portfilm are not aligned, any misregistration in taking the film
introduces misinformation along the borders.  I have seen this occur within
about 5-8 pixels (~3-4mm) of alignment error, which introduces up to 16
pixel-width of misinformation (8 from the drr's left side, and 8 from the
portfilm's right side for example).  In extreme cases, the tumor is close to
the edge of the ct volume, thus my drr is cut off, in some cases up to half
of my drr is not there!

> 
> Are you doing this with the Viola-Wells MutualInformation
> or with the Mattes MutualInformation metric ?
> 

Viola-Wells

> Have you plotted the values that you get from the metric ?

I've plotted the x, y, and rotation with respect to iteration number.  With
just the translation, I have found values for learning rate and variance for
which they converged, but do not converge to a registration that is
acceptable.  The noise stays within one pixel from what it converges to, but
the majority of my cases, they converge to unacceptable registrations.

I am new to using CenteredRigid2DTransform, and from my test adding rotation
to the search, the rotation does not converge yet at all, (and since the
rotation doesn't converge, the translation does not either).

> 
> It is to expect that the Viola-Well metric will be quite
> noisy, while the Mattes implementation will be smoother
> (still noisy...).
> 
> The response to this noise level is to use an appropriate
> optimizer and tune its parameters.

Are you saying Viola-Wells is not appropriate?  I will try out Mattes
algorithm.

> 
> --
> 
> Note that the scaling parameters that you pass to the
> optimizer are a critical piece of the registration process.
> The scaling for the translation paramters  should be on
> the order 1.0 / (image diagonal measured in millimeters).

Any scale value suggestion for the rotation, is it an implied 1.0?

> 
> If you want to get familiar with the CenteredRigid2DTransform
> you can play with the examples in:
> 
>      Insight/Examples/Registration/ImageRegistration5.cxx
>      Insight/Examples/Registration/ImageRegistration6.cxx
>      Insight/Examples/Registration/ImageRegistration7.cxx
>      Insight/Examples/Registration/ImageRegistration8.cxx

I am continuing to play with them, although I've only looked at 5 so far.

> 
> Note that a key piece for using this class, is to initialize
> it with the CenteredTransformInitializer.
> 
> http://www . itk . org/Insight/Doxygen/html/classitk_1_1CenteredTr
> ansformInitializer.html
> 
> In your case, you want to use the initializer in "Moments"
> mode. You will find details about this in the SoftwareGuide
> 
>    http://www . itk . org/ItkSoftwareGuide . pdf
> 
> 
> Section 8.5.l, pdf-page 263, paper-page 289.
> 
> 
> 
> Please let us know if you have further questions.
> 

I appreciate your advice.

Best regards,
John