Hybrid Learning: A Novel Combination of Self-Supervised and Supervised Learning for MRI Reconstruction without High-Quality Training Reference
Journal:
arXiv
Published Date:
May 9, 2025
Abstract
Purpose: Deep learning has demonstrated strong potential for MRI
reconstruction, but conventional supervised learning methods require
high-quality reference images, which are often unavailable in practice.
Self-supervised learning offers an alternative, yet its performance degrades at
high acceleration rates. To overcome these limitations, we propose hybrid
learning, a novel two-stage training framework that combines self-supervised
and supervised learning for robust image reconstruction.
Methods: Hybrid learning is implemented in two sequential stages. In the
first stage, self-supervised learning is employed to generate improved images
from noisy or undersampled reference data. These enhanced images then serve as
pseudo-ground truths for the second stage, which uses supervised learning to
refine reconstruction performance and support higher acceleration rates. We
evaluated hybrid learning in two representative applications: (1) accelerated
0.55T spiral-UTE lung MRI using noisy reference data, and (2) 3D T1 mapping of
the brain without access to fully sampled ground truth.
Results: For spiral-UTE lung MRI, hybrid learning consistently improved image
quality over both self-supervised and conventional supervised methods across
different acceleration rates, as measured by SSIM and NMSE. For 3D T1 mapping,
hybrid learning achieved superior T1 quantification accuracy across a wide
dynamic range, outperforming self-supervised learning in all tested conditions.
Conclusions: Hybrid learning provides a practical and effective solution for
training deep MRI reconstruction networks when only low-quality or incomplete
reference data are available. It enables improved image quality and accurate
quantitative mapping across different applications and field strengths,
representing a promising technique toward broader clinical deployment of deep
learning-based MRI.
