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Abstract #0501

EigenMRI: Data-driven shallow-learning reconstruction of undersampled MRI data

Ricardo Otazo1,2, Ramin Jafari1, and Masoud Zarepisheh1
1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 2Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States


Machine learning, and specifically deep learning, has recently demonstrated high-performance reconstruction of undersampled k-space data. However, training of deep learning reconstruction methods does not take advantage of the thousands of previously acquired images that are stored in clinical databases. Inspired by the eigenfaces method from computer vision, where an image model derived from thousands of pre-existing images is used to identify new faces, this work presents an image reconstruction method named EigenMRI that learns a data-driven regularization approach using thousands of images extracted from a clinical database to reconstruct 3D brain images with 2D acceleration

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