CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data

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.