Motion Correction

[Anonymous]

[stnava]

you can use ANTsR for a one line call ... see

antsMotionCalculation

in antsr

http://stnava.github.io/ANTsR/

otherwise, read the ants documentation a bit ... the parameters, in
general, control how the metrics are calculated and how the
optimization is performed. if you read the page

http://stnava.github.io/fMRIANTs/

you will see comments like " change -i parameters to something larger
and Regular, 0.1to Regular, 0.2 for 'real' data."

you can the do:

antsMotionCorr --help

and see below for the result:

COMMAND:

 antsMotionCorr

      antsMotionCorr = motion correction. This program is a

user-level registration

      application meant to utilize ITKv4-only classes. The user

can specify any number

      of "stages" where a stage consists of a transform; an image

metric; and

      iterations, shrink factors, and smoothing sigmas for each

level. Specialized for

      4D time series data: fixed image is 3D, moving image should

be the 4D time

      series. Fixed image is a reference space or time slice.

OPTIONS:

 -d, --dimensionality 2/3

      This option forces the image to be treated as a

specified-dimensional image. If

      not specified, the program tries to infer the dimensionality

from the input

      image.


 -l, --use-estimate-learning-rate-once

      turn on the option that lets you estimate the learning rate

step size only at

      the beginning of each level. * useful as a second stage of fine-scale

      registration.


 -n, --n-images 10

      This option sets the number of images to use to construct

the template image.

 -m, --metric

CC[fixedImage,movingImage,metricWeight,radius,<samplingstrategy={regular,random}>,<samplingpercentage=[0,1]>]</samplingpercentage=[0,1]></samplingstrategy={regular,random}>

MI[fixedImage,movingImage,metricWeight,numberOfBins,<samplingstrategy={regular,random}>,<samplingpercentage=[0,1]>]</samplingpercentage=[0,1]></samplingstrategy={regular,random}>

Demons[fixedImage,movingImage,metricWeight,radius,<samplingstrategy={regular,random}>,<samplingpercentage=[0,1]>]</samplingpercentage=[0,1]></samplingstrategy={regular,random}>

GC[fixedImage,movingImage,metricWeight,radius,<samplingstrategy={regular,random}>,<samplingpercentage=[0,1]>]</samplingpercentage=[0,1]></samplingstrategy={regular,random}>

      Four image metrics are available--- GC : global correlation, CC: ANTS

      neighborhood cross correlation, MI: Mutual information, and

Demons: Thirion's

      Demons (modified mean-squares). Note that the metricWeight

is currently not

      used. Rather, it is a temporary place holder until

multivariate metrics are

      available for a single stage.


 -u, --useFixedReferenceImage

      use a fixed reference image instead of the neighor in the time series.


 -e, --useScalesEstimator

      use the scale estimator to control optimization.


 -t, --transform Affine[gradientStep]

                 Rigid[gradientStep]

GaussianDisplacementField[gradientStep,updateFieldSigmaInPhysicalSpace,totalFieldSigmaInPhysicalSpace]

SyN[gradientStep,updateFieldSigmaInPhysicalSpace,totalFieldSigmaInPhysicalSpace]

      Several transform options are available. The gradientStep

orlearningRate

      characterizes the gradient descent optimization and is

scaled appropriately for

      each transform using the shift scales estimator. Subsequent

parameters are

      transform-specific and can be determined from the usage.


 -i, --iterations MxNx0...

      Specify the number of iterations at each level.


 -s, --smoothingSigmas MxNx0...

      Specify the amount of smoothing at each level.


 -f, --shrinkFactors MxNx0...

      Specify the shrink factor for the virtual domain (typically

the fixed image) at

      each level.


 -o, --output

[outputTransformPrefix,<outputwarpedimage>,<outputaverageimage>]</outputaverageimage></outputwarpedimage>

      Specify the output transform prefix (output format is

.nii.gz ).Optionally, one

      can choose to warp the moving image to the fixed space and,

if the inverse

      transform exists, one can also output the warped fixed image.


 -a, --average-image

      Average the input time series image.


 -w, --write-displacement

      Write the low-dimensional 3D transforms to a 4D displacement field


 -h

      Print the help menu (short version).

      <VALUES>: 0


 --help

      Print the help menu.

      <VALUES>: 1, 0

brian

On Thu, Feb 5, 2015 at 3:59 PM, nobody itsjustnotme@users.sf.net wrote:

I typically use AFNI for motion correction (the 3dVolreg function). I simply
register all subsequent volumes to the first volume (instead of using an
average). I have heard that ANTs does a great job with motion correction,
and I would like to use it. I see the example given at
https://stnava.github.io/fMRIANTs/ but am having a hard time following it
compared to one-line motion-correction in AFNI. Would somebody be able to
explain what's going on, and what the given arguments in that example mean?

---

Motion Correction

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