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Abstract #2850

Generalizable synthetic multi-contrast MRI generation usingĀ  physics-informed convolutional networks

Luuk Jacobs1,2, Stefano Mandija1,2, Hongyan Liu1,2, Cornelis AT van den Berg1,2, Alessandro Sbrizzi1,2, and Matteo Maspero1,2
1Department of Radiotherapy, Division of Imaging and Oncology, UMC Utrecht, Utrecht, Netherlands, 2Computational Imaging Group for MR Diagnostics and Therapy, Center for Image Sciences, UMC Utrecht, Utrecht, Netherlands


Synthetic MRI aims to reconstruct multiple MRI contrasts from short measurements of tissue properties. Here, a generalizable physics-informed deep learning-based approach for synthetic MRI was investigated. Acquired data were mapped to effective quantitative parameter maps, here named q*-maps, which are fed to a physical signal model synthesizing four contrasts-weighted images. We demonstrated that from q*-maps, MRI contrasts unseen during training could be synthesized. The proposed method is benchmarked to a standard end-to-end deep learning approach. The two deep learning methods generated similar brain images for healthy subjects and patients with different pathologies.

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