Magnetic resonance imaging (MRI) with high resolution (HR) provides more
detailed information for accurate diagnosis and quantitative image analysis.
Despite the significant advances, most existing super-resolution (SR)
reconstruction network for medical images has two flaws: 1) All of them are
designed in a black-box principle, thus lacking sufficient interpretability and
further limiting their practical applications. Interpretable neural network
models are of significant interest since they enhance the trustworthiness
required in clinical practice when dealing with medical images. 2) most
existing SR reconstruction approaches only use a single contrast or use a
simple multi-contrast fusion mechanism, neglecting the complex relationships
between different contrasts that are critical for SR improvement. To deal with
these issues, in this paper, a novel Model-Guided interpretable Deep Unfolding
Network (MGDUN) for medical image SR reconstruction is proposed. The
Model-Guided image SR reconstruction approach solves manually designed
objective functions to reconstruct HR MRI. We show how to unfold an iterative
MGDUN algorithm into a novel model-guided deep unfolding network by taking the
MRI observation matrix and explicit multi-contrast relationship matrix into
account during the end-to-end optimization. Extensive experiments on the
multi-contrast IXI dataset and BraTs 2019 dataset demonstrate the superiority
of our proposed model.

本文提出了一种新的模型引导的可解释深层展开网络 (MGDUN) 用于医学图像超分辨重建，该方法解决了现有超分辨率重建网络的黑匣子缺陷和单一对比度或简单多对比度融合机制的问题通过，通过手动设计的目标函数重建高分辨率 MRI，实验证明该方法在 IXI 数据集和 BraTs 2019 数据集上具有优越性。