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Abstract #0248

Quantitative T1 Mapping from Incoherently Undersampled MR Images Using Self-Attention Convolutional Neural Networks

Yan Wu1, Yajun Ma2, Jiang Du2, and Lei Xing3
1Stanford University, Stanford, CA, United States, 2Radiology, University of California San Diego, La Jolla, CA, United States, 3Radiation Oncology, Stanford University, Stanford, CA, United States

The application of current quantitative MRI techniques is limited by the long scan time. In this study, we propose a deep learning strategy to derive quantitative T1 map and B1 map from two incoherently undersampled variable contrast images. Furthermore, radiofrequency field (B1) inhomogeneity is automatically corrected in the derived T1 map. The tasks are accomplished in two steps: joint reconstruction and parameter quantification, both employing self-attention convolutional neural networks. Significant reduction in data acquisition time has been successfully achieved, including an acceleration in variable contrast image acquisition caused by undersampling and a waiver of B1 map measurement.

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