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Abstract #0332

Self-supervised Deep Learning for Rapid Quantitative Imaging

Fang Liu1 and Li Feng2
1Radiology, Harvard Medical School, Boston, MA, United States, 2Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States

The purpose of this work was to develop a model-guided self-supervised deep learning MRI reconstruction framework called REference-free LAtent map eXtraction (RELAX) for rapid quantitative MR parameter mapping. RELAX eliminates the need for full sampled reference datasets that are required in standard supervised learning. Meanwhile, RELAX also enables direct reconstruction of MR parameter maps from undersampled k-space. Our results demonstrated that the proposed framework produced accurate and robust T1/T2 mapping in accelerated and low-SNR MRI. The good quantitative agreement to the reference method suggests that RELAX allows accelerated quantitative imaging without training with reference data.

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