Motion Estimation and Correction

ScanImage® can continuously detect XYZ motion of the currently acquired image relative to a reference volume during an active acquisition.

The motion correction can be used to


For 3D motion correction, the GpuMotionEstimator is recommended. This estimator requires the Matlab Parallel Computing Toolbox and a Nvidia CUDA enabled GPU.


If not already done once, perform a stage-scanner alignment.

Collect a reference Stack. Right click on the volume in the channel view window and select ‘Set as Motion Correction Reference’.

Motion Estimators

ScanImage® ships with 3 Motion Estimators. All estimators uses basic slice-wise phase correlation to find the best match between the acquired slice and the reference volume.


System Requirements






Requires no additional toolboxes. Not well suited for 3D motion correction due to performance issues.



Best suited for 3D Motion Correction.


Processing data on the GPU is fast, but transferring data to the GPU is a bottleneck. When imaging with low resolution, the SimpleMotionEstimator might perform better.



Alternative to the GpuMotionEstimator if no GPU is present. This estimator uses parallel workers for precessing and dows not slow down the acquisition. The tasks are queued for processing. The queue size is a user settable property.

Motion Correctors

ScanImage® ships with 1 Motion Corrector.




This motion corrector averages the motion estimates of the last N seconds. If average motion vector is greater than the correction threshold, a correction event is triggered. The minimum time in between correction events is settable by the property correctionInterval_s.

Match Current FOV with Previous Session

Output Files

If data logging and motion correction are both enabled, a motion correction output file will be generated with a [File name stem] + “_Motion_” + [File counter] filename.

This file will contain the following attributes: timestamp, frameNumber, success, quality, xyMotion, roiUuid, motionMatrix, z, and channel.


Motion Estimators

Motion estimators derive from the class


The reference volume and the image data are handed to the Motion Estimator as instance of the class


scanimage.mroi.RoiData contains information about the ROI geometry (hRoi), the channels (channels) and the currently imaged zs (zs). The image data is stored in the property imageData. imageData is a cell array, where the first index is the channelIdx, and the second index is the z index.

The function

motion_estimator_result = estimateMotion(obj,roiData)

does not return the motion estimate directly, but instead returns an object of type scanimage.interfaces.IMotionEstimatorResult. ScanImage then polls this class to obtain the estimation results. The purpose of this class is to enable asynchronous processing.

Motion Correctors

Motion estimators derive from the class


When a new motion estimate is available, ScanImage® populates the estimate by calling the function updateMotionHistory. This hands the entire motion history to the corrector. The corrector can then analyze the history and determine if a correction is required. When the corrector wants to initiate a correction, it notifies its event correctNow. ScanImage® then queries the function getCorrection to get the correction value.


if the corrector returns an invalid value (e.g. values outside the allowable correction range), ScanImage discards the correction event.

After a correction is performed, ScanImage® calls the function correctedMotion.

Using Averaged frames

ScanImage 2023.0.0 introduces two checkboxes to the Motion Display window allowing use of averaged live frames to compare against the reference (which can also be an averaged frame). All that is required is to set a Frame Rolling Average Factor` > 1, and then place a check in the Average checkbox.

There is also a throttle checkbox. Checking this have motion detected every n frames where n is the Frame Rolling Average Factor.