Literature DB >> 31737539

Artificial intelligence in pediatric and adult congenital cardiac MRI: an unmet clinical need.

Arghavan Arafati1, Peng Hu2, J Paul Finn2, Carsten Rickers3, Andrew L Cheng4,5, Hamid Jafarkhani6, Arash Kheradvar1.   

Abstract

Cardiac MRI (CMR) allows non-invasive, non-ionizing assessment of cardiac function and anatomy in patients with congenital heart disease (CHD). The utility of CMR as a non-invasive imaging tool for evaluation of CHD have been growing exponentially over the past decade. The algorithms based on artificial intelligence (AI), and in particular, deep learning, have rapidly become a methodology of choice for analyzing CMR. A wide range of applications for AI have been developed to tackle challenges in various aspects of CMR, and significant advances have also been made from image acquisition to image analysis and diagnosis. We include an overview of AI definitions, different architectures, and details on well-known methods. This paper reviews the major deep learning concepts used for analyses of patients with CHD. In the end, we have summarized a list of open challenges and concerns to be considered for future studies. 2019 Cardiovascular Diagnosis and Therapy. All rights reserved.

Entities:  

Keywords:  Cardiac MRI (CMR); artificial intelligence (AI); cardiac segmentation; congenital heart disease (CHD); deep learning

Year:  2019        PMID: 31737539      PMCID: PMC6837938          DOI: 10.21037/cdt.2019.06.09

Source DB:  PubMed          Journal:  Cardiovasc Diagn Ther        ISSN: 2223-3652


  60 in total

1.  Generalized autocalibrating partially parallel acquisitions (GRAPPA).

Authors:  Mark A Griswold; Peter M Jakob; Robin M Heidemann; Mathias Nittka; Vladimir Jellus; Jianmin Wang; Berthold Kiefer; Axel Haase
Journal:  Magn Reson Med       Date:  2002-06       Impact factor: 4.668

2.  B-spline explicit active surfaces: an efficient framework for real-time 3-D region-based segmentation.

Authors:  Daniel Barbosa; Thomas Dietenbeck; Joel Schaerer; Jan D'hooge; Denis Friboulet; Olivier Bernard
Journal:  IEEE Trans Image Process       Date:  2012-01       Impact factor: 10.856

3.  Distance regularized level set evolution and its application to image segmentation.

Authors:  Chunming Li; Chenyang Xu; Changfeng Gui; Martin D Fox
Journal:  IEEE Trans Image Process       Date:  2010-08-26       Impact factor: 10.856

Review 4.  Statistical shape models for 3D medical image segmentation: a review.

Authors:  Tobias Heimann; Hans-Peter Meinzer
Journal:  Med Image Anal       Date:  2009-05-27       Impact factor: 8.545

5.  Fast automatic myocardial segmentation in 4D cine CMR datasets.

Authors:  Sandro Queirós; Daniel Barbosa; Brecht Heyde; Pedro Morais; João L Vilaça; Denis Friboulet; Olivier Bernard; Jan D'hooge
Journal:  Med Image Anal       Date:  2014-06-19       Impact factor: 8.545

6.  A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI.

Authors:  M R Avendi; Arash Kheradvar; Hamid Jafarkhani
Journal:  Med Image Anal       Date:  2016-02-06       Impact factor: 8.545

7.  Evaluating reinforcement learning agents for anatomical landmark detection.

Authors:  Amir Alansary; Ozan Oktay; Yuanwei Li; Loic Le Folgoc; Benjamin Hou; Ghislain Vaillant; Konstantinos Kamnitsas; Athanasios Vlontzos; Ben Glocker; Bernhard Kainz; Daniel Rueckert
Journal:  Med Image Anal       Date:  2019-02-14       Impact factor: 8.545

8.  A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images.

Authors:  Avan Suinesiaputra; Brett R Cowan; Ahmed O Al-Agamy; Mustafa A Elattar; Nicholas Ayache; Ahmed S Fahmy; Ayman M Khalifa; Pau Medrano-Gracia; Marie-Pierre Jolly; Alan H Kadish; Daniel C Lee; Ján Margeta; Simon K Warfield; Alistair A Young
Journal:  Med Image Anal       Date:  2013-09-13       Impact factor: 8.545

9.  Learning a variational network for reconstruction of accelerated MRI data.

Authors:  Kerstin Hammernik; Teresa Klatzer; Erich Kobler; Michael P Recht; Daniel K Sodickson; Thomas Pock; Florian Knoll
Journal:  Magn Reson Med       Date:  2017-11-08       Impact factor: 4.668

10.  Dictionary learning and time sparsity for dynamic MR data reconstruction.

Authors:  Jose Caballero; Anthony N Price; Daniel Rueckert; Joseph V Hajnal
Journal:  IEEE Trans Med Imaging       Date:  2014-04       Impact factor: 10.048

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  5 in total

1.  Generalizable fully automated multi-label segmentation of four-chamber view echocardiograms based on deep convolutional adversarial networks.

Authors:  Arghavan Arafati; Daisuke Morisawa; Michael R Avendi; M Reza Amini; Ramin A Assadi; Hamid Jafarkhani; Arash Kheradvar
Journal:  J R Soc Interface       Date:  2020-08-19       Impact factor: 4.118

Review 2.  Applications of artificial intelligence in cardiovascular imaging.

Authors:  Maxime Sermesant; Hervé Delingette; Hubert Cochet; Pierre Jaïs; Nicholas Ayache
Journal:  Nat Rev Cardiol       Date:  2021-03-12       Impact factor: 32.419

3.  Fully‑automated deep‑learning segmentation of pediatric cardiovascular magnetic resonance of patients with complex congenital heart diseases.

Authors:  Saeed Karimi-Bidhendi; Arghavan Arafati; Andrew L Cheng; Yilei Wu; Arash Kheradvar; Hamid Jafarkhani
Journal:  J Cardiovasc Magn Reson       Date:  2020-11-30       Impact factor: 5.364

4.  More slices, less truth: effects of different test-set design strategies for magnetic resonance image classification.

Authors:  Mila Glavaški; Lazar Velicki
Journal:  Croat Med J       Date:  2022-08-31       Impact factor: 2.415

Review 5.  Artificial Intelligence Advances in the World of Cardiovascular Imaging.

Authors:  Bhakti Patel; Amgad N Makaryus
Journal:  Healthcare (Basel)       Date:  2022-01-14
  5 in total

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