Meeting Banner
Abstract #1966

Multi-Mask Self-Supervised Deep Learning for Highly Accelerated Physics-Guided MRI Reconstruction

Burhaneddin Yaman1,2, Seyed Amir Hossein Hosseini1,2, Steen Moeller2, Jutta Ellermann2, Kâmil Uğurbil2, and Mehmet Akçakaya1,2
1University of Minnesota, Minneapolis, MN, United States, 2Center for Magnetic Resonance Research, Minneapolis, MN, United States

Self-supervised physics-guided deep learning (PG-DL) approaches enable training neural networks without fully-sampled data. These methods split the available k-space measurements into two sets. One is used in the data consistency units of the unrolled network, while the other is used to define the loss. Although self-supervised learning performs well at moderately high acceleration rates, scarcity of acquired data at high acceleration rates degrades the reconstruction performance. In this work, we propose a multi-mask self-supervised learning approach, which retrospectively splits acquired measurement into multiple 2-tuples of disjoint sets. Proposed multi-mask self-supervised learning method outperforms its single-mask counterpart at high acceleration rates.

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

Join Here