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Abstract #3628

Derivation of quantitative T1 map from a single MR image using a self-attention deep neural network

Yan Wu1, Yajun Ma2, Jiang Du2, and Lei Xing1
1Radiation Oncology, Stanford University, Stanford, CA, United States, 2Radiology, University of California San Diego, La Jolla, CA, United States

The application of quantitative MRI is limited by additional data acquisition for variable contrast images. Leveraging from the unique ability of deep learning, we propose a data-driven strategy to derive quantitative T1 map and proton density map from a single qualitative MR image without specific requirements on the weighting of the input image. The quantitative parametric mapping tasks are accomplished using self-attention deep convolutional neural networks, which make efficient use of local and non-local information. In this way, qualitative and quantitative MRI can be attained simultaneously without changing the existing imaging protocol.

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