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Abstract #4002

UNet-based self-attention network for accurate T1/T2 mapping by magnetic resonance fingerprinting

Anbang Chen1, Yuning Gu2, Yuran Zhu3, Yong Chen4, Dinggang Shen2, and Xin Yu3,4,5
1Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH, United States, 2School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, 3Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 4Department of Radiology, Case Western Reserve University, Cleveland, OH, United States, 5Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, OH, United States

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Magnetic Resonance Fingerprinting

Motivation: Deep learning (DL)-assisted magnetic resonance fingerprinting (MRF) provides the opportunity for accurate parameter mapping with highly accelerated data acquisition.

Goal(s): To develop a DL-assisted approach for high-resolution T1 and T2 mapping of the entire rodent brain using 3D MRF.

Approach: A U-Net-based network was developed and trained on synthetic data generated from a mouse brain atlas. Its performance was evaluated with both phantom and in vivo experiments.

Results: The network demonstrated robust, accurate T1 and T2 mapping at high undersampling rates, suggesting that whole-brain MRF can be achieved at reduced acquisition time without compromising accuracy.

Impact: We present a novel deep learning-based 3D MRF method for accurate T1 and T2 mapping of the entire rodent brain using highly undersampled data, enabling dynamic MRF acquisition at higher temporal resolution.

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