Keywords: AI/ML Image Reconstruction, Vessels, Artificial Vessel Suppression
Motivation: Contrast-enhanced Gradient Echo (GRE) sequences offer good lesion visualization but enhancing blood vessels hinder delineation from actual pathology.
Goal(s): Develop a retrospective post-processing algorithm to perform vessel suppression
Approach: We synthesize a spin echo (SE) sequence from GRE sequences using a deep learning (DL) network and use the difference image to create a vessel map. Using the vessel map, the vessel signals are suppressed in the input GRE image.
Results: Quantitative and qualitative results show that the vessel suppressed images have the same contrast enhancement inside lesions, and reduced contrast enhancement in the vessel signals when compared to non-vessel suppressed GRE images.
Impact: The proposed method paves the way for a novel post-processing way of achieving vessel suppression without having to rescan with specialized imaging protocols. This algorithm will be impactful for cases with small lesions like metastases.
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