Diaphragm displacement estimation based on subspace mapping from markerless abdominal surface measurement

Abstract

Respiratory induced organ and tumor motion has great influence in radiation therapy. In order to estimate the inner structures’ movement by external observation, a novel two-step subspace mapping (TSSM) algorithm is proposed to estimate the diaphragm displacement from markerless abdominal surface measurement. We first incorporate 3D image segmentation to measure the displacement of the inner diaphragm and the external abdominal surface on 4D CT images. TSSM constructs eigenspaces by PCA and projects the two organs’ displacement data into their corresponding subspaces. Then, TSSM makes a mapping between the subspaces by a Ridge regression optimization to effectively characterize their correlation. Based on the trained correlation model, the diaphragm displacement can be estimated by the abdominal surface without markers. TSSM is further extended to kernel TSSM (kTSSM) to investigate the non-linear correlation. Experiments show the proposed method can accurately estimate the displacement of the internal diaphragm by the external abdominal surface.