motion estimation; (cyclic) B-spline transformation; variance over last dimension
More details about the data are described in .
Motion estimation from dynamical medical imaging data.
4D chest CT: before registration (left), and after registration (right)
4D cardiac CT
3D carotid US
3D pediatric lung MR
For parameter files see the Elastix Model Zoo repository on GitHub.
elastix version: 4.305
The zip file (see Github) contains all the parameter files used in . The directory naming should be self-explanatory.
Two situations can be distinguished:
This Python script (see Github) combines the forward and inverse transformations to make a transformation relative to a chosen reference time point. Syntax:
combine.py point TransformParameters.0.txt TransformParameters.1.txt Combined.0.txt Combined.1.txt 12 transformix -tp Combined.1.txt -def -out
Note that contains the x, y, z coordinates of the point to transform and the t coordinate of the time point where you want to transform the points to. So given the above example with reference time point 12, you can transform point from time point 12 to time point 5 by specifying coordinates in the format x y z 5 in the .
combine.py image TransformParameters.0.txt TransformParameters.1.txt Combined.0.txt Combined.1.txt 12 transformix -tp Combined.1.txt -in -out
The code for the 4D registration might not work correctly when the axes of the data are not equal to the coordinate system axes (direction cosines).
The method and experiments are published in:
 Nonrigid registration of dynamic medical imaging data using nD+t B-splines and a groupwise optimization approach, C.T. Metz, S. Klein, M. Schaap, T. van Walsum and W.J. Niessen, Medical Image Analysis, in press
© 2020 Viktor van der Valk