Medical image arbitrary-scale super-resolution (MIASSR) has recently gained widespread attention, aiming to super sample medical volumes at arbitrary scales via a single model. However, existing MIASSR methods face two major limitations: (i) reliance on high-resolution (HR) volumes and (ii) limited generalization ability, which restricts their application in various scenarios. To overcome these limitations, we propose Cube-based Neural Radiance Field (CuNeRF), a zero-shot MIASSR framework that can yield medical images at arbitrary scales and viewpoints in a continuous domain. Unlike existing MIASSR methods that fit the mapping between low-resolution (LR) and HR volumes, CuNeRF focuses on building a coordinate-intensity continuous representation from LR volumes without the need for HR references. This is achieved by the proposed differentiable modules: including cube-based sampling, isotropic volume rendering, and cube-based hierarchical rendering. Through extensive experiments on magnetic resource imaging (MRI) and computed tomography (CT) modalities, we demonstrate that CuNeRF outperforms state-of-the-art MIASSR methods. CuNeRF yields better visual verisimilitude and reduces aliasing artifacts at various upsampling factors. Moreover, our CuNeRF does not need any LR-HR training pairs, which is more flexible and easier to be used than others. Our code will be publicly available soon.

翻译：医学图像任意尺度超分辨率（MIASSR）近期受到广泛关注，旨在通过单一模型对医学体数据实现任意尺度的超采样。然而，现有MIASSR方法面临两大局限：（i）依赖高分辨率（HR）体数据；（ii）泛化能力有限，这限制了其在多种场景中的应用。为克服这些局限，我们提出基于立方体的神经辐射场（CuNeRF），一种零样本MIASSR框架，可在连续域内生成任意尺度和视角的医学图像。与现有通过拟合低分辨率（LR）与HR体数据映射关系的MIASSR方法不同，CuNeRF无需HR参考即可从LR体数据构建坐标-强度连续表示。该目标通过所提出的可微模块实现：包括基于立方体的采样、各向同性体渲染以及基于立方体的分层渲染。通过在磁共振成像（MRI）和计算机断层扫描（CT）模态上的大量实验，我们证明CuNeRF优于最先进的MIASSR方法。CuNeRF在不同上采样倍数下均能生成更逼真的视觉效果并减少混叠伪影。此外，我们的CuNeRF无需任何LR-HR训练对，相比其他方法更加灵活且易于使用。我们的代码将很快公开。