2.B. Principal Component Analysis (PCA) for motion modeling

Salam Dhou; John Lewis; Weixing Cai; Dan Ionascu; Christopher Williams

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2.B. Principal Component Analysis (PCA) for motion modeling

SD Salam Dhou

JL John Lewis

WC Weixing Cai

DI Dan Ionascu

CW Christopher Williams

This method is extracted from research article: Biomed Phys Eng Express, Apr 2020

Quantifying day-to-day variations in 4DCBCT-based PCA motion models

DOI: 10.1088/2057-1976/ab817e

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Building PCA motion models from a set of 4DCBCT images captured at each fraction requires performing the following two steps:

where $\bar{DVF}$ is the mean DVF. u_m are the eigenvectors obtained from PCA and are defined in space, while the parameters w_m (t) are PCA coefficients and are defined in time. M is the number of eigenmodes.

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