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Reza Nezafat, Ph.D.

Co-Author

This page shows the publications co-authored by Reza Nezafat and Ahmed Samir Fahmy Amin Emam.
Connection Strength

3.930
  1. Multi-domain convolutional neural network (MD-CNN) for radial reconstruction of dynamic cardiac MRI. Magn Reson Med. 2021 03; 85(3):1195-1208.
    View in: PubMed
    Score: 0.867
  2. Nonrigid active shape model-based registration framework for motion correction of cardiac T1 mapping. Magn Reson Med. 2018 08; 80(2):780-791.
    View in: PubMed
    Score: 0.719
  3. Automated Myocardial T2 and Extracellular Volume Quantification in Cardiac MRI Using Transfer Learning-based Myocardium Segmentation. Radiol Artif Intell. 2020 Jan 29; 2(1):e190034.
    View in: PubMed
    Score: 0.415
  4. Three-dimensional Deep Convolutional Neural Networks for Automated Myocardial Scar Quantification in Hypertrophic Cardiomyopathy: A Multicenter Multivendor Study. Radiology. 2020 01; 294(1):52-60.
    View in: PubMed
    Score: 0.409
  5. Non-contrast myocardial infarct scar assessment using a hybrid native T1 and magnetization transfer imaging sequence at 1.5T. Magn Reson Med. 2019 05; 81(5):3192-3201.
    View in: PubMed
    Score: 0.384
  6. Automated Cardiac MR Scar Quantification in Hypertrophic Cardiomyopathy Using Deep Convolutional Neural Networks. JACC Cardiovasc Imaging. 2018 12; 11(12):1917-1918.
    View in: PubMed
    Score: 0.375
  7. Gray blood late gadolinium enhancement cardiovascular magnetic resonance for improved detection of myocardial scar. J Cardiovasc Magn Reson. 2018 03 22; 20(1):22.
    View in: PubMed
    Score: 0.365
  8. Cardiovascular magnetic resonance feature tracking strain analysis for discrimination between hypertensive heart disease and hypertrophic cardiomyopathy. PLoS One. 2019; 14(8):e0221061.
    View in: PubMed
    Score: 0.201
  9. Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks. J Cardiovasc Magn Reson. 2019 01 14; 21(1):7.
    View in: PubMed
    Score: 0.193
Connection Strength
The connection strength for co-authors is the sum of the scores for each of their shared publications.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.