Titlebook: Machine Learning for Medical Image Reconstruction; Third International Farah Deeba,Patricia Johnson,Jong Chul Ye Conference proceedings 20

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Learning Bloch Simulations for MR Fingerprinting by Invertible Neural NetworksMRF based on dictionary matching is slow and lacks scalability. To overcome these limitations, neural network (NN) approaches estimating MR parameters from fingerprints have been proposed recently. Here, we revisit NN-based MRF reconstruction to jointly learn the forward process from MR parameters t

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Extending LOUPE for K-Space Under-Sampling Pattern Optimization in Multi-coil MRIMRI was extended in three folds: firstly, fully sampled multi-coil k-space data from the scanner, rather than simulated k-space data from magnitude MR images in LOUPE, was retrospectively under-sampled to optimize the under-sampling pattern of in-vivo k-space data; secondly, binary stochastic k-spac

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AutoSyncoder: An Adversarial AutoEncoder Framework for Multimodal MRI Synthesislem of modality synthesis in multimodal MRI and propose an efficient, multiresolution encoder-decoder network trained like an autoencoder that can predict missed inputs at the output. This can help in avoiding the acquisition of redundant information, thereby saving time. We formulate and demonstrat

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