Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction, Transformer, Multiple Instance LearningDespite its proven clinical value, Diffusion-weighted Imaging (DWI) suffers from several technical limitations associated with prolonged echo trains in single-shot sequences. Parallel Imaging with sufficiently high under-sampling enabled by Deep Learning-based reconstruction may mitigate these problems. Newly emerged architectures relying on transformers demonstrated high performance in this context. This work aims at developing a transformer-based reconstruction method tailored to DWI by utilizing the availability of multiple image instances for a given slice. Redundancies are exploited by jointly reconstructing images using attention mechanisms which are performed across the set of instances. Benefits over reconstructing images separately from each other are demonstrated.
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