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：受限于成像系统，从部分测量数据重建磁共振成像（MRI）图像是医学影像研究的关键。得益于不同成像模态下多对比MR图像的多样性与互补信息，多对比超分辨率（SR）重建有望生成更高质量的SR图像。在医学场景中，为全面观察病灶，放射科医生习惯以任意尺度缩放MR图像，而非如多数MRI SR方法使用固定尺度参数。此外，现有多对比MRI SR方法通常要求参考图像具有固定分辨率，这既增加了参考图像获取难度，也限制了任意尺度SR任务的应用空间。针对上述问题，我们提出基于隐式神经表示的双任意多对比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，使网络能输入任意尺度的目标图像与参考图像。随后，隐式融合解码器融合多对比特征，并利用隐式解码函数（IDF）获取最终MRI SR结果。此外，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数据集上的广泛实验表明，本方法在不同尺度因子下均优于现有最优方法，并在临床实践中具有巨大潜力。