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Abstract #0390

Hybrid Learning: A Novel Combination of Self-Supervised and Supervised Learning for MRI Reconstruction without High Quality Training Reference

Haoyang Pei1,2,3, Yao Wang3, Hersh Chandarana1,2, and Li Feng1,2
1Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of MedicineNew York University Grossman School of Medicine, New York, NY, United States, 2Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 3Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, New York, NY, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, AI/ML Image Reconstruction, Low-Field Imaging

Motivation: High-quality training references are not always available for deep learning reconstruction methods that rely on supervised learning. While self-supervised learning can be beneficial, but it can suffer from compromised performance at high acceleration rates.

Goal(s): We introduce a novel concept called hybrid learning for MRI reconstruction in cases where only low-quality reference images are available.

Approach: This was implemented in two training phases. Self-supervised learning is first employed to generate high-quality images from low-quality reference data. The obtained high-quality images in the first stage are subsquenetly used for supervised training.

Results: This enables high acceleration rates beyond the capabilities of standard self-supervised learning.

Impact: This study proposes a novel hybrid learning strategy to address challenges when obtaining high-quality reference data is difficult, which enables more accurate reconstruction at higher acceleration rates, which is beneficial in various applications where only low-quality reference images are available .

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