Rank Constrained Diffeomorphic Density Motion Estimation for Respiratory Correlated Computed Tomography

TL;DR

This paper introduces a novel low-rank constrained diffeomorphic motion estimation algorithm for 4D respiratory CT imaging, enabling efficient analysis of organ motion during breathing cycles.

Contribution

The paper proposes a new low-rank constrained diffeomorphic motion estimation method specifically designed for RCCT, improving statistical analysis and treatment planning.

Findings

Effective low-rank motion representation for RCCT

Enhanced statistical analysis of respiratory motion

Applicable to various medical imaging motion estimation tasks

Abstract

Motion estimation of organs in a sequence of images is important in numerous medical imaging applications. The focus of this paper is the analysis of 4D Respiratory Correlated Computed Tomography (RCCT) Imaging. It is hypothesized that the quasi-periodic breathing induced motion of organs in the thorax can be represented by deformations spanning a very low dimension subspace of the full infinite dimensional space of diffeomorphic transformations. This paper presents a novel motion estimation algorithm that includes the constraint for low-rank motion between the different phases of the RCCT images. Low-rank deformation solutions are necessary for the efficient statistical analysis and improved treatment planning and delivery. Although the application focus of this paper is RCCT the algorithm is quite general and applicable to various motion estimation problems in medical imaging.