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

Burhaneddin Yaman^1,2, Seyed Amir Hossein Hosseini^1,2, Steen Moeller², Jutta Ellermann², Kâmil Uğurbil², and Mehmet Akçakaya^1,2
¹University of Minnesota, Minneapolis, MN, United States, ²Center for Magnetic Resonance Research, Minneapolis, MN, United States

The proposed multi-mask self-supervised physics-guided learning technique significantly improves upon its previously proposed single-mask counterpart for highly accelerated MRI reconstruction.

Figure 3. A representative test brain MRI slice showing reconstruction results using CG-SENSE, SSDU PG-DL and proposed multi-mask SSDU PG-DL at R=8, as well as CG-SENSE at acquisition acceleration R=2. CG-SENSE suffers from major noise amplification at R=8, whereas SSDU PG-DL at R=8 achieves similar reconstruction quality to CG-SENSE at R=2. The proposed multi-mask SSDU PG-DL further improves the reconstruction quality compared to SSDU PG-DL.

Figure 2. a) Reconstruction results on a representative test slice at R = 8 using CG-SENSE, supervised PG-DL, SSDU PG-DL and proposed multi-mask SSDU PG-DL. CG-SENSE suffers from major noise amplification and artifacts. SSDU PG-DL also shows residual artifacts (red arrows) at this high acceleration rate. Proposed multi-mask SSDU PG-DL further suppresses these artifacts and achieve artifact-free reconstruction, removing artifacts that are still visible in supervised PG-DL.