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The gradient-based search method is not limited in the registration parameter search either.
The optimal registration parameter, given by, is one which gives minimum value of between the transformed test image and.
where is the maximum expected offset for a single registration parameter in positive or negative direction, where we use the offset which gives the best similarity score.
One possible reason is that for all 20 data sets, we use similar registration parameter settings (e.g., the number of radial basis functions and the total resolution levels); in particular, we fixed the weight of the constraint term, α, to a value of 0.3.
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The equation for finding the best registration parameters is (1).
Finally, mean PET-MR registration parameters were measured.
Using this function, we can determine the new registration parameters.
To obtain the four registration parameters, this means that we have to select five starting points.
We also use a derivative-based optimizer (Thévenaz's method) to determine the optimal registration parameters.
During the subsequent stage, the non-rigid registration parameters are optimized.
In SHR, we try to find the best registration parameters, by maximizing a similarity function.
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