Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks

Lossau, Tanja; Nickisch, Hannes; Wissel, Tobias; Bippus, Rolf; Schmitt, Holger; Morlock, Michael M.; Graß, Michael

doi:10.1016/j.media.2018.11.003

Cited by 45 publications

(25 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For these reasons, it is a very interesting research topic to explore an absolute measure which can quantify the various appearance of the lung motion artifacts. For example, convolutional neural network (CNN) could be adopted for motion quantification 20 .…”

Section: Discussionmentioning

confidence: 99%

Motion correction for routine X-ray lung CT imaging

Kim

Choi

Lee

et al. 2021

Sci Rep

View full text Add to dashboard Cite

A novel motion correction algorithm for X-ray lung CT imaging has been developed recently. It was designed to perform for routine chest or thorax CT scans without gating, namely axial or helical scans with pitch around 1.0. The algorithm makes use of two conjugate partial angle reconstruction images for motion estimation via non-rigid registration which is followed by a motion compensated reconstruction. Differently from other conventional approaches, no segmentation is adopted in motion estimation. This makes motion estimation of various fine lung structures possible. The aim of this study is to explore the performance of the proposed method in correcting the lung motion artifacts which arise even under routine CT scans with breath-hold. The artifacts are known to mimic various lung diseases, so it is of great interest to address the problem. For that purpose, a moving phantom experiment and clinical study (seven cases) were conducted. We selected the entropy and positivity as figure of merits to compare the reconstructed images before and after the motion correction. Results of both phantom and clinical studies showed a statistically significant improvement by the proposed method, namely up to 53.6% (p < 0.05) and up to 35.5% (p < 0.05) improvement by means of the positivity measure, respectively. Images of the proposed method show significantly reduced motion artifacts of various lung structures such as lung parenchyma, pulmonary vessels, and airways which are prominent in FBP images. Results of two exemplary cases also showed great potential of the proposed method in correcting motion artifacts of the aorta which is known to mimic aortic dissection. Compared to other approaches, the proposed method provides an excellent performance and a fully automatic workflow. In addition, it has a great potential to handle motions in wide range of organs such as lung structures and the aorta. We expect that this would pave a way toward innovations in chest and thorax CT imaging.

show abstract

Section: Discussionmentioning

confidence: 99%

Motion correction for routine X-ray lung CT imaging

Kim

Choi

Lee

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Furthermore, non-invasive coronary angiography such as Computed Tomography Angiography (CTA) are already of significant value in the diagnostic procedure of patients. Our modeling approach can enhance the current literature on computer-based approaches for the interpretation of CTA images [8,7].…”

Section: Discussionmentioning

confidence: 99%

Network-Based Approach for Modeling and Analyzing Coronary Angiography

Ravandi¹,

Ravandi²

2020

Springer Proceedings in Complexity

View full text Add to dashboard Cite

Significant intra-observer and inter-observer variability in the interpretation of coronary angiograms are reported. This variability is in part due to the common practices that rely on performing visual inspections by specialists (e.g., the thickness of coronaries). Quantitative Coronary Angiography (QCA) approaches are emerging to minimize observer's error and furthermore perform predictions and analysis on angiography images. However, QCA approaches suffer from the same problem as they mainly rely on performing visual inspections by utilizing image processing techniques. In this work, we propose an approach to model and analyze the entire cardiovascular tree as a complex network derived from coronary angiography images. This approach enables to analyze the graph structure of coronary arteries. We conduct the assessments of network integration, degree distribution, and controllability on a healthy and a diseased coronary angiogram. Through our discussion and assessments, we propose modeling the cardiovascular system as a complex network is an essential phase to fully automate the interpretation of coronary angiographic images. We show how network science can provide a new perspective to look at coronary angiograms.

show abstract

“…In the past decade, image reconstruction algorithms have evolved in parallel with imaging hardware allowing for dramatic improvements in image qualitative and quantitative accuracy, resulting in a reduction in acquisition times and/or radiation exposure. In order to achieve such improved performance, different data corrections have been accurately modelled and incorporated [ 6 , 7 ], within iterative reconstruction algorithms, which may impact their computational time efficiency in cardiovascular imaging [ 8 – 11 ]. Although current iterative algorithms used in clinical practice provide excellent image quality, there are issues concerning the variability in convergence rate as a function of activity concentrations in the different tissues of interest.…”

Section: General Aspects Of Ai In Cardiovascular Imagingmentioning

confidence: 99%

Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

Slart

Williams

Juárez-Orozco

et al. 2021

Eur J Nucl Med Mol Imaging

View full text Add to dashboard Cite

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

show abstract

Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks

Cited by 45 publications

References 15 publications

Motion correction for routine X-ray lung CT imaging

Motion correction for routine X-ray lung CT imaging

Network-Based Approach for Modeling and Analyzing Coronary Angiography

Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

Contact Info

Product

Resources

About