Deep autofocus with cone-beam CT consistency constraint

TL;DR

This paper introduces a deep learning method that combines a CBCT consistency constraint with motion estimation to improve image quality in interventional C-arm cone-beam CT, effectively suppressing motion artifacts.

Contribution

It extends a recent learning-based motion compensation approach by integrating a CBCT consistency constraint, enabling detection and correction of both in-plane and out-of-plane patient motions.

Findings

Achieves 93% artifact suppression on average

Outperforms entropy-based autofocus by a significant margin

Effectively detects and compensates for complex patient motions

Abstract

High quality reconstruction with interventional C-arm cone-beam computed tomography (CBCT) requires exact geometry information. If the geometry information is corrupted, e. g., by unexpected patient or system movement, the measured signal is misplaced in the backprojection operation. With prolonged acquisition times of interventional C-arm CBCT the likelihood of rigid patient motion increases. To adapt the backprojection operation accordingly, a motion estimation strategy is necessary. Recently, a novel learning-based approach was proposed, capable of compensating motions within the acquisition plane. We extend this method by a CBCT consistency constraint, which was proven to be efficient for motions perpendicular to the acquisition plane. By the synergistic combination of these two measures, in and out-plane motion is well detectable, achieving an average artifact suppression of 93 [percent]. This outperforms the entropy-based state-of-the-art autofocus measure which achieves on average an artifact suppression of 54 [percent].