Brenden T. Kadota

Optimized Multi-Contrast Self-Supervised MRI Reconstruction Using Learned K-Space Partitioning

This work proposes a multi-contrast self-supervised multi-contrast learned partitioning method that jointly trains on multiple under-sampled contrasts without requiring fully sampled k-space data as a reference, and improves reconstruction fidelity over single-contrast self-supervised MRI reconstructions.