<div class="gmail_quote">On Tue, Nov 3, 2009 at 7:12 AM, Oscar Esteban Sanz-Dranguet <span dir="ltr"><<a href="mailto:oesteban@die.upm.es">oesteban@die.upm.es</a>></span> wrote:<br><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Hi,<br>
<br>
I'm trying to perform intra-subject rigid registration between MR & SPECT images. I would like the MR to be the fixed image, BUT I've found that if I use the SPECT as fixed, the registration is more quick and accurate. Why does it happen?<br>
</blockquote><div><br>It should be quicker, using a lower resolution image as the fixed
image, for most metrics. The metric is evaluated at each sample point
on the fixed image, typically all voxels in the fixed image.<br>
<br>
Most metrics sample the whole fixed image. With Mattes, contrary to
what you report, one would expect the times to be roughly the same
(assuming that you have the same number of samples in both cases). <br>
<br>
The final resampling step though, should be a lot faster using the
fixed image as the low res image, since the moving image is resampled
to the fixed image grid, ie the iterator walks through every pixel in
the fixed image and computes the intensity of the moving voxel that
resamples onto this location.<br>
<br>
<br></div><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
<br>
The images are as follows:<br>
<br>
- SPECT: 128x128x43,<br>
3,32237mmx3,32237mmx3,32237<br>
less than 255 levels of gray<br>
<br>
- MR: 224x240x256<br>
0,85mmx0,9mmx0,85mm<br>
512 levels of gray<br>
<br>
<br>
Image preprocessing:<br>
- I use a IntensityWindowingImageFilter to rescale the intensities to the range 0,255.<br>
- I use a DiscreteGaussianImageFilter with deviation 2.0 to the two images, but I've tried using a lot of different values and combinations. It appears to be the same<br>
- I use ScalarImageToHistogramGenerator to find the first maximum of the two images histogram. With it, I use the metric's member SetFixedImageSamplesIntensityThreshold for the fixed image and I remove the background of the moving image with ThresholdImagefilter. The values used for the two thresholds is the value of the first maximum + 10% of overall frequencies.<br>
- Resampling SPECT to MR's spacing and size improves the registration, when using MR as fixed and adequate values of optimizer scales (they are in a very tiny range)<br>
<br>
Initialization:<br>
- Using Moments, the SPECT image starts a little lower (10mm) on the Z axis<br>
<br>
Metric:<br>
- 70 bins, 10000 samples<br>
<br>
Optimizer:<br>
- SPSA or Gradient Descent. I use a visualization pipeline with VTK to test that the scales and the step size make sense.<br>
<br>
<br>
Thanks in advance. Cheers.<br>
<br>
-- <br>
Oscar Esteban<br>
BIT - UPM (<a href="http://www.die.upm.es/im/" target="_blank">http://www.die.upm.es/im/</a>)<br>
+34 913 366 827 ext.4248<br>
<br>
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</blockquote></div><br><br clear="all"><br>