PCA-based lung motion model

TL;DR

This paper provides a detailed analysis and theoretical justification of a PCA-based lung motion model, demonstrating its effectiveness and equivalence to a 5D physiological model with high accuracy in clinical data.

Contribution

It offers a deeper understanding of why PCA effectively models lung motion and proves its equivalence to a 5D physiological model under certain conditions.

Findings

Average 3D error below 1 mm on clinical data

Theoretical justification for PCA's effectiveness

Equivalence of PCA model to 5D physiological model

Abstract

Organ motion induced by respiration may cause clinically significant targeting errors and greatly degrade the effectiveness of conformal radiotherapy. It is therefore crucial to be able to model respiratory motion accurately. A recently proposed lung motion model based on principal component analysis (PCA) has been shown to be promising on a few patients. However, there is still a need to understand the underlying reason why it works. In this paper, we present a much deeper and detailed analysis of the PCA-based lung motion model. We provide the theoretical justification of the effectiveness of PCA in modeling lung motion. We also prove that under certain conditions, the PCA motion model is equivalent to 5D motion model, which is based on physiology and anatomy of the lung. The modeling power of PCA model was tested on clinical data and the average 3D error was found to be below 1 mm.