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To evaluate an iterative learning approach for enhanced performance of robust artificial‐neural‐networks for k‐space interpolation (RAKI), when only a limited amount of training data (auto‐calibration signals [ACS]) are available for… Click to show full abstract
To evaluate an iterative learning approach for enhanced performance of robust artificial‐neural‐networks for k‐space interpolation (RAKI), when only a limited amount of training data (auto‐calibration signals [ACS]) are available for accelerated standard 2D imaging.
Keywords:
training;
space interpolation;
iterative training;
training robust
Journal Title: Magnetic Resonance in Medicine
Year Published: 2022
Link to full text (if available)
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Journal Title: Magnetic Resonance in Medicine
Year Published: 2022
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!