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Abstract #4610

Joint attention deep learning reconstruction of highly-accelerated pre- and post-contrast T1-weighted 3D images of brain tumors

Anthony Mekhanik1 and Ricardo Otazo1,2
1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 2Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States

Synopsis

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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