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

Author:

Lossau T.,Nickisch H.ORCID,Wissel T.,Bippus R.,Schmitt H.,Morlock M.,Grass M.

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference27 articles.

1. Nonrigid registration-based coronary artery motion correction for cardiac computed tomography;Bhagalia;Med. Phys.,2012

2. Diagnostic accuracy of noninvasive 64-row computed tomographic coronary angiography (CCTA) compared with myocardial perfusion imaging (MPI): the PICTURE study, a prospective multicenter trial;Budoff;Acad. Radiol.,2017

3. Prognostic performance of coronary computed tomography angiography in asymptomatic individuals as compared to symptomatic patients with an appropriate indication;Camargo;J. Cardiovasc. Comput. Tomogr.,2017

4. Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs;Chen;IEEE Trans. Pattern Anal. Mach. Intell.,2016

5. Motion estimation in coronary CT angiography images using convolutional neural networks;Elss;Med. Imaging Deep Learn. (MIDL),2018

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