Coping with real world data: Artifact reduction and denoising for motion-compensated cardiac C-arm CT

Authors

  • Taubmann Oliver,

    1. Pattern Recognition Lab, Computer Science Department, Friedrich-Alexander-University Erlangen-Nuremberg, 91058 Erlangen, Germany and Erlangen Graduate School in Advanced Optical Technologies (SAOT), 91052 Erlangen, Germany
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  • Maier Andreas,

    1. Pattern Recognition Lab, Computer Science Department, Friedrich-Alexander-University Erlangen-Nuremberg, 91058 Erlangen, Germany and Erlangen Graduate School in Advanced Optical Technologies (SAOT), 91052 Erlangen, Germany
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  • Hornegger Joachim,

    1. Pattern Recognition Lab, Computer Science Department, Friedrich-Alexander-University Erlangen-Nuremberg, 91058 Erlangen, Germany and Erlangen Graduate School in Advanced Optical Technologies (SAOT), 91052 Erlangen, Germany
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  • Lauritsch Günter,

    1. Siemens Healthcare GmbH, 91301 Forchheim, Germany
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  • Fahrig Rebecca

    1. Radiological Sciences Laboratory, Stanford University, Stanford, California 94305 and Siemens Healthcare GmbH, 91301 Forchheim, Germany
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Abstract

Purpose:

Detailed analysis of cardiac motion would be helpful for supporting clinical workflow in the interventional suite. With an angiographic C-arm system, multiple heart phases can be reconstructed using electrocardiogram gating. However, the resulting angular undersampling is highly detrimental to the quality of the reconstructed images, especially in nonideal intraprocedural imaging conditions. Motion-compensated reconstruction has previously been shown to alleviate this problem, but it heavily relies on a preliminary reconstruction suitable for motion estimation. In this work, the authors propose a processing pipeline tailored to augment these initial images for the purpose of motion estimation and assess how it affects the final images after motion compensation.

Methods:

The following combination of simple, direct methods inspired by the core ideas of existing approaches proved beneficial: (a) Streak reduction by masking high-intensity components in projection domain after filtering. (b) Streak reduction by subtraction of estimated artifact volumes in reconstruction domain. (c) Denoising in spatial domain using a joint bilateral filter guided by an uncompensated reconstruction. (d) Denoising in temporal domain using an adaptive Gaussian smoothing based on a novel motion detection scheme.

Results:

Experiments on a numerical heart phantom yield a reduction of the relative root-mean-square error from 89.9% to 3.6% and an increase of correlation with the ground truth from 95.763% to 99.995% for the motion-compensated reconstruction when the authors' processing is applied to the initial images. In three clinical patient data sets, the signal-to-noise ratio measured in an ideally homogeneous region is increased by 37.7% on average. Overall visual appearance is improved notably and some anatomical features are more readily discernible.

Conclusions:

The authors' findings suggest that the proposed sequence of steps provides a clear advantage over an arbitrary sequence of individual image enhancement methods and is fit to overcome the issue of lacking image quality in motion-compensated C-arm imaging of the heart. As for future work, the obtained results pave the way for investigating how accurately cardiac functional motion parameters can be determined with this modality.

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