Keywords: AI/ML Image Reconstruction, Normal development, Pituitary
Motivation: 3D CUBE imaging of pediatric pituitary is time-consuming, and thus presents difficulties for children with limited patience and cooperation. A vendor-provided deep learning reconstruction (DLR) algorithm, proposed for high image SNR, may allow for MR imaging with shortened scan time.
Goal(s): Explore if DLR allowed for rapid CUBE imaging in pediatric pituitary while maintaining the image quality and precise measurement of pituitary height.
Approach: The imaging quality, scan time, and pituitary height measured were compared between DLR-CUBE and conventional CUBE.
Results: Relative to conventional CUBE, DLR-CUBE showed improved SNR, comparable image quality, accurate measurement of pituitary height, and only half the scan time.
Impact: DLR-CUBE can dramatically shorten the acquisition time while maintaining the image quality and accurate measurement for pituitary height, demonstrating the potential of DLR-CUBE in clinical examinations of pediatric pituitary.
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