Fast CUBE imaging of pediatric pituitary with deep learning reconstruction algorithm at 3T

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.