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，从部分测量数据重建磁共振成像（MRI）图像是医学影像研究的重要环节。得益于多对比度MR图像在不同成像模态中提供的多样性与互补信息，多对比度超分辨率（SR）重建有望生成质量更高的超分辨率图像。在医疗场景中，为充分观察病灶，放射科医生习惯任意缩放MR图像而非使用固定缩放倍数——这与大多数MRI超分辨率方法采用的固定尺度不同。此外，现有基于多对比度MRI的超分辨率方法通常要求参考图像具有固定分辨率，这不仅增加了参考图像获取难度，也限制了任意尺度超分辨率任务的应用。针对上述问题，我们提出了一种基于隐式神经表征的双任意多对比度MRI超分辨率方法——Dual-ArbNet。首先，通过特征编码器将目标图像与参考图像的分辨率解耦，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（IDF）生成最终的超分辨率MRI结果。进一步地，我们引入课程学习策略训练网络，有效提升了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，本方法在不同缩放因子下的表现均超越现有最优方法，具有显著的临床实践潜力。