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Abstract #0699

Unsupervised Diffeomorphic Registration for Even and Odd Echo Images with Applications to Point-of-Care MRI Reconstruction

Jo Schlemper1, Neel Dey2, Seyed Sadegh Mohseni Salehi1, Kevin Sheth3, W. Taylor Kimberly4, Lorraine Cullen5, and Michal Sofka1
1Hyperfine, Guilford, CT, United States, 2New York University, New York City, NY, United States, 3Yale University, New Haven, CT, United States, 4Massachusetts General Hospital, Boston, MA, United States, 5Gaylord Specialty Healthcare, Wallingford, CT, United States


We present an unsupervised deep image registration framework for MR image reconstruction. Specifically, even and odd echo images from fast spin echo-based sequence are nonlinearly registered using a convolutional network that estimates the deformation field. The registered echo images are then averaged for noise reduction. The proposed framework was evaluated across four imaging contrasts (T1w, T2w, FLAIR, and DWI) from a low-field MR scanner and was found to outperform nonlinear registration from advanced normalization tools, yielding sharper image quality and preserving important pathology features.

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