Magnetic resonance image compilation (MAGiC) is a single click scan that provides multiple contrast-weighted images in around 5 mins. This makes it an effective diagnostic option in clinical settings. However, synthetic T2-FLAIR is known to have partial volume artifacts which impacts its diagnostic performance. In this work, we propose a method to mitigate partial-volume artifacts in synthetic T2-FLAIR using a separately acquired fast T2-FLAIR contrast information combined with keyhole and deep learning-based image reconstruction.
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