Difference between revisions of "Projects:RegistrationImprovement:XFormConcatenation"
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| − | Transform Concatenation: Multiple spatial transforms applied to the same volume appear as nested MRML entries, which could be collapsed. Important to apply before a volume resampling is executed to avoid accumulation of interpolation effects. Affine is simple matrix multiplication, Bspline would have to send the gridpoints through all Xforms and reparameterize. | + | Transform Concatenation: Multiple spatial transforms applied to the same volume appear as nested MRML entries, which could be collapsed. Important to apply before a volume resampling is executed to avoid accumulation of interpolation effects. Affine is simple matrix multiplication, Bspline would have to send the gridpoints through all Xforms and reparameterize. |
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| + | Affine is just matrix multiplication. Non-rigid is trickier, BSpline would have to send the grid-points thru and reparameterize. But in any event to limit interpolation effects something of this nature would be key. It also moves toward what in digital photography circles is known as "non-destructive image processing", a powerful concept esp. for slicer as a single-case experimental platform, and at least in principle well positioned with the MRML tree data structure. | ||
Revision as of 18:37, 9 October 2009
Home < Projects:RegistrationImprovement:XFormConcatenationTransform Concatenation: Multiple spatial transforms applied to the same volume appear as nested MRML entries, which could be collapsed. Important to apply before a volume resampling is executed to avoid accumulation of interpolation effects. Affine is simple matrix multiplication, Bspline would have to send the gridpoints through all Xforms and reparameterize.
Affine is just matrix multiplication. Non-rigid is trickier, BSpline would have to send the grid-points thru and reparameterize. But in any event to limit interpolation effects something of this nature would be key. It also moves toward what in digital photography circles is known as "non-destructive image processing", a powerful concept esp. for slicer as a single-case experimental platform, and at least in principle well positioned with the MRML tree data structure.
