Separating complex compound patient motion tracking data using independent component analysis
2014
In SPECT imaging, motion from respiration and body motion can reduce image quality by introducing motion-related
artifacts. A minimally-invasive way to track patient motion is to attach external markers to the patient’s body and record
their location throughout the imaging study. If a patient exhibits multiple movements simultaneously, such as respiration
and body-movement, each marker location data will contain a mixture of these motions. Decomposing this complex
compound motion into separate simplified motions can have the benefit of applying a more robust motion correction to
the specific type of motion. Most motion tracking and correction techniques target a single type of motion and either
ignore compound motion or treat it as noise. Few methods account for compound motion exist, but they fail to
disambiguate super-position in the compound motion (i.e. inspiration in addition to body movement in the positive
anterior/posterior direction). We propose a new method for decomposing the complex compound patient motion using an
unsupervised learning technique called Independent Component Analysis (ICA). Our method can automatically detect
and separate different motions while preserving nuanced features of the motion without the drawbacks of previous
methods. Our main contributions are the development of a method for addressing multiple compound motions, the novel
use of ICA in detecting and separating mixed independent motions, and generating motion transform with 12 DOFs to
account for twisting and shearing. We show that our method works with clinical datasets and can be employed to improve
motion correction in single photon emission computed tomography (SPECT) images.
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