CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale
  Volumetric Super-Resolution of Medical Data
Wei Fang Yuxing Tang Heng Guo Mingze Yuan Tony C. W. Mok Ke Yan Jiawen Yao Xin Chen Zaiyi Liu Le Lu Ling Zhang Minfeng Xu
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.