  In the realm of medical 3D data, such as CT and MRI images, prevalent
anisotropic resolution is characterized by high intra-slice but diminished
inter-slice resolution. The lowered resolution between adjacent slices poses
challenges, hindering optimal viewing experiences and impeding the development
of robust downstream analysis algorithms. Various volumetric super-resolution
algorithms aim to surmount these challenges, enhancing inter-slice resolution
and overall 3D medical imaging quality. However, existing approaches confront
inherent challenges: 1) often tailored to specific upsampling factors, lacking
flexibility for diverse clinical scenarios; 2) newly generated slices
frequently suffer from over-smoothing, degrading fine details, and leading to
inter-slice inconsistency. In response, this study presents CycleINR, a novel
enhanced Implicit Neural Representation model for 3D medical data volumetric
super-resolution. Leveraging the continuity of the learned implicit function,
the CycleINR model can achieve results with arbitrary up-sampling rates,
eliminating the need for separate training. Additionally, we enhance the grid
sampling in CycleINR with a local attention mechanism and mitigate
over-smoothing by integrating cycle-consistent loss. We introduce a new metric,
Slice-wise Noise Level Inconsistency (SNLI), to quantitatively assess
inter-slice noise level inconsistency. The effectiveness of our approach is
demonstrated through image quality evaluations on an in-house dataset and a
downstream task analysis on the Medical Segmentation Decathlon liver tumor
dataset.
