On the Influence of Smoothness Constraints in Computed Tomography Motion Compensation

TL;DR

This paper investigates how spline-based smoothness constraints in motion models affect the ability to recover motion frequencies in CT images, emphasizing the importance of model choice for effective motion compensation.

Contribution

It provides a detailed analysis of the influence of spline-based motion models on frequency recovery limits in CT motion compensation, highlighting the importance of model tailoring.

Findings

Higher node counts can extend recoverable high frequencies.

Optimal motion model depends on anatomy and clinical protocol.

The algorithm accurately fits spline nodes up to Nyquist limits.

Abstract

Computed tomography (CT) relies on precise patient immobilization during image acquisition. Nevertheless, motion artifacts in the reconstructed images can persist. Motion compensation methods aim to correct such artifacts post-acquisition, often incorporating temporal smoothness constraints on the estimated motion patterns. This study analyzes the influence of a spline-based motion model within an existing rigid motion compensation algorithm for cone-beam CT on the recoverable motion frequencies. Results demonstrate that the choice of motion model crucially influences recoverable frequencies. The optimization-based motion compensation algorithm is able to accurately fit the spline nodes for frequencies almost up to the node-dependent theoretical limit according to the Nyquist-Shannon theorem. Notably, a higher node count does not compromise reconstruction performance for slow motion patterns, but can extend the range of recoverable high frequencies for the investigated algorithm. Eventually, the optimal motion model is dependent on the imaged anatomy, clinical use case, and scanning protocol and should be tailored carefully to the expected motion frequency spectrum to ensure accurate motion compensation.