Structure and Smoothness Constrained Dual Networks for MR Bias Field Correction
Journal:
arXiv
Published Date:
Jul 2, 2025
Abstract
MR imaging techniques are of great benefit to disease diagnosis. However, due
to the limitation of MR devices, significant intensity inhomogeneity often
exists in imaging results, which impedes both qualitative and quantitative
medical analysis. Recently, several unsupervised deep learning-based models
have been proposed for MR image improvement. However, these models merely
concentrate on global appearance learning, and neglect constraints from image
structures and smoothness of bias field, leading to distorted corrected
results. In this paper, novel structure and smoothness constrained dual
networks, named S2DNets, are proposed aiming to self-supervised bias field
correction. S2DNets introduce piece-wise structural constraints and smoothness
of bias field for network training to effectively remove non-uniform intensity
and retain much more structural details. Extensive experiments executed on both
clinical and simulated MR datasets show that the proposed model outperforms
other conventional and deep learning-based models. In addition to comparison on
visual metrics, downstream MR image segmentation tasks are also used to
evaluate the impact of the proposed model. The source code is available at:
https://github.com/LeongDong/S2DNets}{https://github.com/LeongDong/S2DNets.
