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Abstract #2440

Rapid high-resolution cranial bone MRI using deep-learning prior image reconstruction

Parna Eshraghi Boroojeni1, Paul Commean1, Cihat Eldeniz1, Weijie Gan1, Gary Skolnick1, Kamlesh Patel1, Ulugbek Kamilov1, and Hongyu An1
1Washington University in Saint Louis, Saint Louis, MO, United States


A high-resolution (HR) MRI capable of resolving the detail of bony structures at sub-millimeter resolution is desired. A short MR acquisition results in under-sampled k-space data below the Nyquist rate, leading to artifacts and high noise. We developed an HR reconstruction method regularized by a complex deep-learning prior (RECD). We achieved high-resolution MR (0.6x0.6x0.8mm3) with a one-minute acquisition time. Using images reconstructed from a 5-minute MR scan as the gold standard, we compared the peak signal to noise ratio (PSNR) and similarity index (SSIM) for 1-min RECD and 1-min compressed sensing (CS) reconstructed images. RECD outperformed CS.

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