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Coping with real world data: Artifact reduction and denoising for motion‐compensated cardiac C‐arm CT
Author(s) -
Taubmann Oliver,
Maier Andreas,
Hornegger Joachim,
Lauritsch Günter,
Fahrig Rebecca
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4939878
Subject(s) - artificial intelligence , computer vision , noise reduction , undersampling , iterative reconstruction , computer science , motion compensation , motion estimation , imaging phantom , smoothing , streak , nuclear medicine , medicine , physics , optics
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.