[Insight-users] FEMRegistrationFilter: multi-resolution techniques

[Jens Fisseler]

Hi everybody!

I'm quite new to ITK and I'm currently using the FEMRegistrationFilter 
to register two MRI liver volume datasets. The results this far are
quite promising, but I've a question regarding the use of the
multi-resolution technique.

My datasets have the resolution 512x384x64 and I'm using a three level
pyramid:

===================
 level	resolution
-------------------
 0	128x96x16
 1	256x192x32
 2	512x384x64
===================

As Luis Ibanez wrote in
http://www.itk.org/pipermail/insight-users/2003-November/005704.html :
"If a level has not converged, there are few chances that the next level
will compensate.", I'm trying to optimise the parameters for each level
before going up to the next. But this is where my problems start. I
don't quite know how to get access to the deformed volume at a lower
level in order to assess the registration. Let me explain: If I want to
assess the deformation at level 0, I would make the registration filter
stop after this level by setting the second line in my parameter file to
"1". When I write the deformed image to a file it has the expected
dimensions but is all black, probably because a small portion of the
original volume gets deformed, which is black in this area because the
liver has been segmented.

Has anybody an idea how I can get access to downsampled deformed volume?
Is my approach a good idea anyway?

This is a part of the parameter file I'm using:


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Parameters for the single- or multi-resolution techniques
%

3        % Number of levels in the multi-res pyramid (1 = single-res)
1        % Highest level to use in the pyramid
4 4 4    % Scaling at lowest level of pyramid
4 4 4    % Number of pixels per element
1.e5 1.e4 1.e4 % Elasticity (E)
1.e4 1.e4 1.e4 % Density x capacity (RhoC)
1 1 1    % Image energy scaling (gamma) - sets gradient step size
2 2 2    % NumberOfIntegrationPoints
4 4 4    % WidthOfMetricRegion
10 1 1   % MaximumIterations

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Parameters for the registration
%

0 0.99 % Similarity metric (0=mean sq, 1 = ncc, 2=pattern int, 3=MI,
5=demons)
1.0    % Alpha
0      % DescentDirection (1 = max, 0 = min)
0      % DoLineSearch (0=never, 1=always, 2=if needed)
1.e1   % TimeStep
0.5    % Landmark variance
0      % Employ regridding / enforce diffeomorphism ( >= 1 -> true)

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Information about the image inputs
%

512               % Nx (image x dimension)
384               % Ny (image y dimension)
64                % Nz (image z dimension - not used if 2D)
./liver_1.mhd     % ReferenceFileName
./liver_2.mhd     % TargetFileName


Regards,

	Jens

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Institute for Robotics and Cognitive Systems -- University Luebeck
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