ftm: Fast Template Matching via Cross-Correlation
In dcemri: A Package for Medical Image Analysis

Description Usage Arguments Details Value Author(s) References See Also

Motion correction and/or co-registration of three-dimensional arrays (medical imaging data) are performed by applying a user-defined mask of voxels. Normalized cross-correlations (in 3D) are computed using the FFT.

1	ftm(input, mask, template, plot=FALSE, ...)

`input`	is a four-dimensional array of signal intensities.
`mask`	is a three-dimensional array of logical values (voxels to be included are `TRUE`, and voxels to be excluded are `FALSE`).
`template`	is a three dimensional array (the same structure as `mask`) that contains the signal intensities associated with the reference data.
`plot`	is a flag that determines if a plot of the motion correction as a function of the fourth dimension (default = `FALSE`).
`...`	Additional variables passed to the `plot` function.

An extremely basic method of motion correction/co-registration is implemented by estimating “local” cross-correlations based on a binary mask that is a subset of the original three-dimensional volume. All convolutions are preformed via the FFT (fft) and repetitive calculations are minimized where possible.

Only whole-voxel translations are considered. This does not begin to capture the true effects of motion in soft tissue, but we assume that the object of interest (e.g., tumor) is a fairly rigid structure. Potential extensions include rigid-body, affine and nonlinear registration techniques along with interploation schemes in order to capture intra-voxel manipulations of the data.

A list of objects are returned:

`out`	Motion-corrected version of the four-dimensional array.
`offset`	Translations (in 3D) for each volume in the 4D array.
`t.center`	Estimated center of the binary mask.