This study presents a new Deep-Learning (DL) based strategy for background computation in MR images. 3-plane Localizer scans have been used for background-subtraction in all subsequent scans of same MR Examination. This is accomplished by obtaining foreground-background masks for Localizer images using U-Net model and applying Image Resampling techniques on the obtained mask to compute background for subsequent scans. Comparison with existing algorithms demonstrates that proposed method prevails in accuracy, effectiveness and provides improved visual contrast. It can also be used universally across anatomies and MR pulse-sequences as opposed to other methods requiring anatomy/sequence-specific tuning and adaptive parameter adjustments.
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