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Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model
In this work, we formulate a novel transformer (Gformer) based iterative modelling approach for the synthesis of images with arbitrary contrast enhancement that corresponds to different dose levels.
One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputation
In this work, we formulate missing data imputation as a sequence-to-sequence learning problem and propose a multi-contrast multi-scale Transformer (MMT), which can take any subset of input contrasts and synthesize those that are missing.
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