"
Can semi-quantitative evaluation of uncertain (type II) time-intensity curves improve diagnosis in breast DCE-MRI?"
written by Roberta Fusco, Salvatore Filice, Vincenza Granata, Ylenia Mandato, Annamaria Porto, Massimiliano D’Aiuto, Massimo Rinaldo, Maurizio Di Bonito, Mario Sansone, Carlo Sansone, Antonio Rotondo, Antonella Petrillo Petrillo,
published by
Journal of Biomedical Science and Engineering,
Vol.6 No.3A, 2013
has been cited by the following article(s):
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Characterization of breast lesion using T1-perfusion magnetic resonance imaging: Qualitative vs. quantitative analysis
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Integration of DCE-MRI and DW-MRI Quantitative Parameters for Breast Lesion Classification
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Discrimination of Benign and Malignant Suspicious Breast Tumors Based on Semi-Quantitative DCE-MRI Parameters Employing Support Vector Machine
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Frontiers in Biomedical Technologies,
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A secure, scalable and versatile multi-layer client–server architecture for remote intelligent data processing
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Models and methods for analyzing DCE‐MRI: A review
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ANALYSIS OF CONTRAST-ENHANCED MEDICAL IMAGES
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Doctoral dissertation, University of Louisville,
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Models and methods for analyzing DCE-MRI: A review
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Medical physics,
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Quantitative perfusion parameters of benign inflammatory breast pathologies: A descriptive study
Clinical Imaging,
2020
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[2]
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Characterization of breast lesion using T 1 -perfusion magnetic resonance imaging: Qualitative vs. quantitative analysis
Diagnostic and Interventional Imaging,
2018
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Integration of DCE-MRI and DW-MRI Quantitative Parameters for Breast Lesion Classification
BioMed Research International,
2015
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A secure, scalable and versatile multi-layer client–server architecture for remote intelligent data processing
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2015
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Models and methods for analyzing DCE-MRI: A review
Medical Physics,
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Models and methods for analyzing DCE‐MRI: A review
Medical Physics,
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Use of Tracer Kinetic Models for Selection of Semi-Quantitative Features for DCE-MRI Data Classification
Applied Magnetic Resonance,
2013
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