In this paper, we propose an efficient self-supervised arbitrary-scale super-resolution (SR) framework to reconstruct isotropic magnetic resonance (MR) images from anisotropic MRI inputs without involving external training data. The proposed framework builds a training dataset using in-the-wild anisotropic MR volumes with arbitrary image resolution. We then formulate the 3D volume SR task as a SR problem for 2D image slices. The anisotropic volume's high-resolution (HR) plane is used to build the HR-LR image pairs for model training. We further adapt the implicit neural representation (INR) network to implement the 2D arbitrary-scale image SR model. Finally, we leverage the well-trained proposed model to up-sample the 2D LR plane extracted from the anisotropic MR volumes to their HR views. The isotropic MR volumes thus can be reconstructed by stacking and averaging the generated HR slices. Our proposed framework has two major advantages: (1) It only involves the arbitrary-resolution anisotropic MR volumes, which greatly improves the model practicality in real MR imaging scenarios (e.g., clinical brain image acquisition); (2) The INR-based SR model enables arbitrary-scale image SR from the arbitrary-resolution input image, which significantly improves model training efficiency. We perform experiments on a simulated public adult brain dataset and a real collected 7T brain dataset. The results indicate that our current framework greatly outperforms two well-known self-supervised models for anisotropic MR image SR tasks.

翻译：本文提出一种高效的自监督任意尺度超分辨率框架，用于从各向异性MRI输入重建各向同性磁共振图像，且无需依赖外部训练数据。该框架利用任意图像分辨率的自然场景各向异性MR体数据构建训练数据集，并将三维体超分辨率任务转化为二维图像切片超分辨率问题。通过各向异性体数据的高分辨率平面构建高-低分辨率图像对用于模型训练，进一步采用隐式神经表征网络实现二维任意尺度图像超分辨率模型。最后利用训练完备的模型对各向异性MR体数据中的二维低分辨率平面进行上采样，生成其高分辨率视图。通过堆叠并平均生成的高分辨率切片，即可重建各向同性MR体数据。本框架具备两大优势：（1）仅需任意分辨率各向异性MR体数据，显著提升模型在真实MR成像场景（如临床脑部图像采集）中的实用性；（2）基于隐式神经表征的超分辨率模型可对任意分辨率输入图像实现任意尺度超分辨率，大幅提高模型训练效率。我们在模拟公开成人脑部数据集与真实采集的7T脑部数据集上进行实验，结果表明当前框架在各向异性MR图像超分辨率任务中显著优于两种经典自监督模型。