Keywords: Machine Learning/Artificial Intelligence, Data Processing, Reconstruction
Artificial intelligence (AI) applications in the field of magnetic resonance imaging have been implemented in routine clinical practice. However, MRI still faces a practical and persistent challenge: its long acquisition time. This has led to two prominent issues in health care: high cost and poor patient experience. Long acquisition time is also a source of degraded imaging quality (e.g., motion artifacts). In the study, we propose to develop novel Deep Learning (DL) architectures combined with transfer learning capabilities to address the above challenge and apply this newly developed AI technique for image reconstruction in MRI with very fast imaging acquisition.
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