Literature DB >> 31670597

Deep Learning for Quantitative Cardiac MRI.

Qian Tao1, Boudewijn P F Lelieveldt1, Rob J van der Geest1.   

Abstract

OBJECTIVE. The recent advancement of deep learning techniques has profoundly impacted research on quantitative cardiac MRI analysis. The purpose of this article is to introduce the concept of deep learning, review its current applications on quantitative cardiac MRI, and discuss its limitations and challenges. CONCLUSION. Deep learning has shown state-of-the-art performance on quantitative analysis of multiple cardiac MRI sequences and holds great promise for future use in clinical practice and scientific research.

Keywords:  artificial intelligence; cardiac MRI; deep learning; quantitative MRI

Mesh:

Year:  2019        PMID: 31670597     DOI: 10.2214/AJR.19.21927

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  4 in total

1.  AI Based CMR Assessment of Biventricular Function: Clinical Significance of Intervendor Variability and Measurement Errors.

Authors:  Shuo Wang; Hena Patel; Tamari Miller; Keith Ameyaw; Akhil Narang; Daksh Chauhan; Simran Anand; Emeka Anyanwu; Stephanie A Besser; Keigo Kawaji; Xing-Peng Liu; Roberto M Lang; Victor Mor-Avi; Amit R Patel
Journal:  JACC Cardiovasc Imaging       Date:  2021-10-13

2.  Radiomics and nomogram of magnetic resonance imaging for preoperative prediction of microvascular invasion in small hepatocellular carcinoma.

Authors:  Yi-Di Chen; Ling Zhang; Zhi-Peng Zhou; Bin Lin; Zi-Jian Jiang; Cheng Tang; Yi-Wu Dang; Yu-Wei Xia; Bin Song; Li-Ling Long
Journal:  World J Gastroenterol       Date:  2022-08-21       Impact factor: 5.374

3.  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

4.  Reference Values of Native T1 at 3T Cardiac Magnetic Resonance-Standardization Considerations between Different Vendors.

Authors:  Liliana Tribuna; Pedro Belo Oliveira; Alba Iruela; João Marques; Paulo Santos; Tiago Teixeira
Journal:  Diagnostics (Basel)       Date:  2021-12-11
  4 in total

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