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