Meeting Banner
Abstract #2572

Motion-tolerant super-resolution reconstruction from multi-stack MR data

Sachin Jambawalikar1,2, Daniel Litwiller3, Michael Liu1, Rami Vanguri4, Simukayi Mutasa5,6, Zhengchao Dong7,8, and Hiram Shaish1

1Radiology, Columbia University Medical Center, New York, NY, United States, 2Radiology, New York Presbyterian Hospital, New York, NY, United States, 3Global MR Applications and Workflow, GE Healthcare, New York, NY, United States, 4Biomedical Informatics, Columbia University, New York, NY, United States, 5Radiology, Columbia University Medical Center, NY, NY, United States, 6Radiology, New York Presbyterian Hospital, NY, NY, United States, 7New York State Psychiatric Institute, New York, NY, United States, 8Psychiatry, Columbia University, 10032, NY, United States

Image super resolution reconstruction (ISRR) is a technique that may be useful for generating fast, motion tolerant 3D reconstructed images from multi stack data. We provide initial results of a multi-step ISRR approach using patch-to-volume reconstruction(PVR) followed by a slice-by-slice convolutional neural network to further improve spatial resolution. Our methods provide improved measures of peak-SNR, and could be used to rapidly generate 3D volumes from multiple 2D stacks in fetal and abdominal imaging where constant motion requires short scan times as well as in pelvic imaging where high SNR requirements lead to long scan times and motion artifact. Motion artifact is a significant obstacle in these MRI applications resulting in image quality degradation and potentially limited diagnostic ability.

This abstract and the presentation materials are available to members only; a login is required.

Join Here