Movement Artifact Correction
Movement Artifact Correction - Related Videos
10:07
Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact
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Cited by 39 •
2015
This report presents details of how to adopt the acromion marker cluster method of obtaining scapular kinematics when using a passive marker motion capture device. As has been described in the literature, this method provides a robust, non-invasive, three-dimensional, dynamic and valid measurement of scapular kinematics, minimizing skin movement artifact.
07:20
A Novel Approach to Overcome Movement Artifact When Using a Laser Speckle Contrast Imaging System for Alternating Speeds of Blood Microcirculation
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Cited by 11 •
2017
This study introduces a novel technique for the measurement and analysis of alternating speeds of blood microcirculation in a single experiment using a laser speckle contrast imager.
01:15
Distance Corrections
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2025
To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
10:04
Sample Drift Correction Following 4D Confocal Time-lapse Imaging
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Cited by 222 •
2014
Time-lapse microscopy allows the visualization of developmental processes. Growth or drift of samples during image acquisition reduces the ability to accurately follow and measure cell movements during development. We describe the use of open source image processing software to correct for three dimensional sample drift over time.
01:20
Power Factor Correction
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2024
The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit. A poor power factor means the inductive electrical load absorbs more reactive power, causing a low lagging power factor. This results in large voltage drops across the load...
FAQs
Detection relies on inconsistencies that motion introduces across images, signals, or time points. Motion tracking can provide direct evidence of displacement, while landmark comparison and image registration reveal whether anatomical features remain spatially aligned. Temporal irregularities can also identify corrupted segments. These signals help distinguish motion-induced changes from genuine physiological or anatomical variation before correction is applied.
Each method addresses a different aspect of the problem. Motion tracking measures movement, landmark comparison checks feature displacement, and image registration aligns measurements acquired in different positions. Filtering can reduce signal components associated with motion-related distortion, while corrupted-segment exclusion removes data that cannot be reliably repaired. Combining these approaches supports more consistent anatomical and physiological measurements.
Respiration, involuntary movement, and voluntary repositioning can alter measurements even when the underlying biology has not changed. If these effects are not recognized, a comparison may suggest a false anatomical or physiological difference. Identifying their spatial or temporal signatures allows correction or exclusion of affected data, which is especially important for quantitative analysis and longitudinal studies.
A typical workflow first identifies motion-related inconsistencies, then selects an appropriate response based on the data. Motion may be tracked, landmarks compared, or images registered to restore alignment. Filtering can reduce remaining distortion, and severely corrupted segments may be excluded. The corrected measurements are then assessed for improved anatomical detail, accuracy, or consistency.
The approach is relevant to MRI, CT, ultrasound, and related physiological measurement systems. The specific correction strategy depends on whether motion appears as spatial misalignment, temporal inconsistency, or corrupted data segments. Across these settings, correction can preserve anatomical detail and make measurements more dependable when voluntary movement, involuntary movement, or respiration affects acquisition.
Reducing motion-related distortion can increase diagnostic confidence by making anatomical features easier to evaluate. It also improves the reliability of quantitative measurements, allowing observed differences to better reflect biological variation rather than patient movement. In longitudinal studies, consistent correction helps distinguish genuine change over time from changes caused by differing motion conditions during separate measurements.