Limited by imaging systems, the reconstruction of Magnetic Resonance Imaging
(MRI) images from partial measurement is essential to medical imaging research.
Benefiting from the diverse and complementary information of multi-contrast MR
images in different imaging modalities, multi-contrast Super-Resolution (SR)
reconstruction is promising to yield SR images with higher quality. In the
medical scenario, to fully visualize the lesion, radiologists are accustomed to
zooming the MR images at arbitrary scales rather than using a fixed scale, as
used by most MRI SR methods. In addition, existing multi-contrast MRI SR
methods often require a fixed resolution for the reference image, which makes
acquiring reference images difficult and imposes limitations on arbitrary scale
SR tasks. To address these issues, we proposed an implicit neural
representations based dual-arbitrary multi-contrast MRI super-resolution
method, called Dual-ArbNet. First, we decouple the resolution of the target and
reference images by a feature encoder, enabling the network to input target and
reference images at arbitrary scales. Then, an implicit fusion decoder fuses
the multi-contrast features and uses an Implicit Decoding Function~(IDF) to
obtain the final MRI SR results. Furthermore, we introduce a curriculum
learning strategy to train our network, which improves the generalization and
performance of our Dual-ArbNet. Extensive experiments in two public MRI
datasets demonstrate that our method outperforms state-of-the-art approaches
under different scale factors and has great potential in clinical practice.

本文提出了一种基于隐式神经表示的双任意多对比度 MRI 超分辨率方法 (Dual-ArbNet)，该方法可以无限制地使用任意缩放的目标和参考图像进行图像重建，并通过课程学习策略提高了该方法在两个 MRI 数据集上的性能。