Mitigating synthetic T2-FLAIR artifacts in 2D MAGiC using keyhole and deep learning based image reconstruction
Sudhanya Chatterjee1, Naoyuki Takei2, Rohan Patil1, Sugmin Gho3, Suchandrima Banerjee4, Florian Wiesinger5, and Dattesh Dayanand Shanbhag1
1GE Healthcare, Bengaluru, India, 2GE Healthcare, Tokyo, Japan, 3GE Healthcare, Seoul, Korea, Republic of, 4GE Healthcare, Menlo Park, CA, United States, 5GE Healthcare, Munich, Germany
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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