arXiv Open Access 2023

SubZero: Subspace Zero-Shot MRI Reconstruction

Heng Yu Yamin Arefeen Berkin Bilgic
Lihat Sumber

Abstrak

Recently introduced zero-shot self-supervised learning (ZS-SSL) has shown potential in accelerated MRI in a scan-specific scenario, which enabled high-quality reconstructions without access to a large training dataset. ZS-SSL has been further combined with the subspace model to accelerate 2D T2-shuffling acquisitions. In this work, we propose a parallel network framework and introduce an attention mechanism to improve subspace-based zero-shot self-supervised learning and enable higher acceleration factors. We name our method SubZero and demonstrate that it can achieve improved performance compared with current methods in T1 and T2 mapping acquisitions.

Topik & Kata Kunci

Penulis (3)

H

Heng Yu

Y

Yamin Arefeen

B

Berkin Bilgic

Format Sitasi

Yu, H., Arefeen, Y., Bilgic, B. (2023). SubZero: Subspace Zero-Shot MRI Reconstruction. https://arxiv.org/abs/2311.17251

Akses Cepat

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Informasi Jurnal
Tahun Terbit
2023
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓