Keywords: Machine Learning/Artificial Intelligence, AI/ML Image Reconstruction, joint pre- and post-contrast acceleration, joint multi-contrast reconstruction, 3D brain tumor imaging, multi-scale Transformer
Motivation: The efficiency of brain tumor MRI can be improved by exploiting correlations between pre- and post-contrast repetitions.
Goal(s): Develop a deep joint reconstruction network to exploit correlations between pre- and post-contrast 3D T1-weighted without explicit k-space data consistency for highly-accelerated brain tumor MRI.
Approach: A multi-scale Transformer was employed to exploit the attention mechanism for long-distance correlations within and across image contrasts. 8-fold accelerated brain tumor patient data was used to train and evaluate the network.
Results: The joint-attention transformer reconstruction outperformed convolutional Unet and transformer trained on individual contrasts.
Impact: A joint-attention deep learning reconstruction method can exploit correlations across sequences and enable significant reductions in MRI protocols.
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