Abstract #4448

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

Mykhailo Hasiuk^1,2, Kamlesh Pawar^1,3, Shenjun Zhong¹, Richard McIntyre¹, Zhaolin Chen^1,4, and Gary Egan^1,3

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.

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