Automated image quality evaluation of T 2 -weighted liver MRI utilizing deep learning architecture

Journal of Magnetic Resonance Imaging

Volumn 47, Issue 3, 2018, Pages 723-728

  Esses, Steven J ^a   Lu, Xiaoguang ^b   Zhao, Tiejun ^b   Shanbhogue, Krishna ^a   Dane, Bari ^a   Bruno, Mary ^a   Chandarana, Hersh ^a  

^a NEW YORK UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b Siemens Healthineers   (United States)

Author keywords

convolutional neuronal network; deep learning; image quality; liver MRI; machine learning; T2 weighted imaging

Indexed keywords

ADULT; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; CONVOLUTIONAL NEURAL NETWORK; FEMALE; HEPATOGRAPHY; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE QUALITY; LABORATORY AUTOMATION; MAJOR CLINICAL STUDY; MALE; NUCLEAR MAGNETIC RESONANCE IMAGING; PREDICTIVE VALUE; PRIORITY JOURNAL; RADIOLOGIST; RETROSPECTIVE STUDY; SENSITIVITY AND SPECIFICITY; VALIDATION PROCESS; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; LIVER; LIVER DISEASE; MIDDLE AGED; OBSERVER VARIATION; PROCEDURES;

ALGORITHMS; DEEP LEARNING; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LIVER; LIVER DISEASES; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; OBSERVER VARIATION; RETROSPECTIVE STUDIES; SENSITIVITY AND SPECIFICITY;

EID: 85020071856     PISSN: 10531807     EISSN: 15222586     Source Type: Journal    
DOI: 10.1002/jmri.25779     Document Type: Article

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