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Abstract #0435

Rapid Quantitative Imaging Using Wave-Encoded Model-Based Deep Learning for Joint Reconstruction

Jaejin Cho1,2, Borjan Gagoski2,3, Tae Hyung Kim1,2, Qiyuan Tian1,2, Robert Frost1,2, Itthi Chatnuntawech4, and Berkin Bilgic1,2,5
1Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 2Department of Radiology, Harvard Medical School, Boston, MA, United States, 3Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States, 4National Nanotechnology Center, Pathum Thani, Thailand, 5Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States


We propose a wave-encoded model-based deep learning (wave-MoDL) method for joint multi-contrast image reconstruction with volumetric encoding using an interleaved look-locker acquisition sequence with T2 preparation pulse (3D-QALAS). Wave-MoDL enables a 2-minute acquisition at R=4x3-fold acceleration using a 32-channel array to provide T1, T2, and proton density maps at 1 mm isotropic resolution, from which standard contrast-weighted images can also be synthesized.

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