Paper
20 March 2015 Image-based compensation for involuntary motion in weight-bearing C-arm cone-beam CT scanning of knees
Mathias Unberath, Jang-Hwan Choi, Martin Berger, Andreas Maier, Rebecca Fahrig
Author Affiliations +
Abstract

We previously introduced four fiducial marker-based strategies to compensate for involuntary knee-joint motion during weight-bearing C-arm CT scanning of the lower body. 2D methods showed significant reduction of motion- related artifacts, but 3D methods worked best.

However, previous methods led to increased examination times and patient discomfort caused by the marker attachment process. Moreover, sub-optimal marker placement may lead to decreased marker detectability and therefore unstable motion estimates. In order to reduce overall patient discomfort, we developed a new image-based 2D projection shifting method.

A C-arm cone-beam CT system was used to acquire projection images of five healthy volunteers at various flexion angles. Projection matrices for the horizontal scanning trajectory were calibrated using the Siemens standard PDS-2 phantom. The initial reconstruction was forward projected using maximum-intensity projections (MIP), yielding an estimate of a static scan. This estimate was then used to obtain the 2D projection shifts via registration.

For the scan with the most motion, the proposed method reproduced the marker-based results with a mean error of 2.90 mm +/- 1.43 mm (compared to a mean error of 4.10 mm +/- 3.03 mm in the uncorrected case). Bone contour surrounding modeling clay layer was improved. The proposed method is a first step towards automatic image-based, marker-free motion-compensation.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mathias Unberath, Jang-Hwan Choi, Martin Berger, Andreas Maier, and Rebecca Fahrig "Image-based compensation for involuntary motion in weight-bearing C-arm cone-beam CT scanning of knees", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94130D (20 March 2015); https://doi.org/10.1117/12.2081559
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Cited by 7 scholarly publications.
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KEYWORDS
Motion models

Computed tomography

Bone

Image registration

Sensors

3D image processing

Image segmentation

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