Meeting Banner
Abstract #4448

Deep learning based motion estimation from highly under-sampled EPI volumetric navigators

Mykhailo Hasiuk1,2, Kamlesh Pawar1,3, Shenjun Zhong1, Richard McIntyre1, Zhaolin Chen1,4, and Gary Egan1,3

1Monash Biomedical Imaging, Monash University, Melbourne, Australia, 2Department of Material Sciences and New Technologies, Vasyl Stefanyk Precarpathian National University, Ivano-Frankivsk, Ukraine, 3School of Psychological Sciences, Monash University, Melbourne, Australia, 4Department of Electrical and Computer System Engineering, Monash University, Melbourne, Australia

Dynamic EPI volumetric navigators are widely used to track head motion in MRI, and accurate motion estimation requires EPI volumes to be inserted in every several seconds or even less. However, the use of dynamic EPI volumes to track motion significantly degrades the overall data acquisition efficiency. To address this issue, in this work we introduce a deep learning based motion estimation method from highly under-sampled (i.e. acceleration factor of 16) EPI volumetric navigators. The method directly estimates motion parameters from the under-sampled data, and does not require reconstruction of images.

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

Join Here