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.

翻译：受成像系统限制，从部分测量数据中重建磁共振图像是医学影像研究的核心。得益于多对比度MR图像在不同成像模态中的多样性与互补信息，多对比度超分辨率重建有望生成更高质量的SR图像。在医学场景中，为充分观察病灶，放射科医生习惯对MR图像进行任意尺度缩放，而非使用大多数MRI SR方法所采用的固定尺度。此外，现有基于多对比度的MRI SR方法通常要求参考图像具有固定分辨率，这使得参考图像获取困难，并对任意尺度SR任务施加限制。为解决上述问题，我们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。首先，通过特征编码器解耦目标图像与参考图像的分辨率，使网络能够输入任意尺度的目标与参考图像。随后，隐式融合解码器融合多对比度特征，并利用隐式解码函数获取最终MRI SR结果。此外，我们引入课程学习策略训练网络，提升了Dual-ArbNet的泛化能力与性能。在两个公开MRI数据集上的大量实验表明，本方法在不同尺度因子下均优于现有最优方法，在临床实践中具有巨大潜力。