Self super-resolution for magnetic resonance images

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9902 LNCS, Issue , 2016, Pages 553-560

  Jog, Amod ^a   Carass, Aaron ^a,b   Prince, Jerry L ^a  

^a Department of Electrical and Computer Engineering ^*  (United States)

^b Johns Hopkins University   (United States)

Author keywords

MRI; Self generated training data; Super resolution

Indexed keywords

IMAGE ACQUISITION; IMAGE PROCESSING; IMAGE SEGMENTATION; MAGNETIC LEVITATION VEHICLES; MAGNETIC RESONANCE; MEDICAL IMAGING; OPTICAL RESOLVING POWER;

FLUID ATTENUATED INVERSION RECOVERIES; HIGHER RESOLUTION; ILL POSED PROBLEM; MAGNETIC RESONANCE IMAGES (MRI); POST-PROCESSING TECHNIQUES; SEGMENTATION ACCURACY; SUPER RESOLUTION; TRAINING DATA;

MAGNETIC RESONANCE IMAGING;

ALGORITHM; HUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; VALIDATION STUDY;

ALGORITHMS; HUMANS; MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84996524106     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46726-9_64     Document Type: Conference Paper

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