mlsanityData to 7 Oct 2026

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

arXiv 2404.04878 ↗First posted 7 Apr 2024eess.IV cs.CVcited by 15

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Abstract (arXiv)

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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 39 of its 56 references that are in the map. First posted on arXiv 7 Apr 2024.

Builds on

39 of 56 references.

References · 39

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27 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 7; 2026: 7.

15 papers.

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  1. 2025MRI Super-Resolution with Deep Learning: A Comprehensive Surveycited by 7
  2. 2024SIMPLE: Simultaneous Multi-Plane Self-Supervised Learning for Isotropic MRI Restoration from Anisotropic Datacited by 5
  3. 2025Continuous Representation Methods, Theories, and Applications: An Overview and Perspectivescited by 4
  4. 2025An Anisotropic Cross-View Texture Transfer with Multi-Reference Non-Local Attention for CT Slice Interpolationcited by 2
  5. 2025From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstructioncited by 1
  6. 2025Zero-shot CT Super-Resolution using Diffusion-based 2D Projection Priors and Signed 3D Gaussianscited by 1
  7. 2025DCDB: Dynamic Conditional Dual Diffusion Bridge for Ill-posed Multi-Taskscited by 1
  8. 2025Nexus-INR: Diverse Knowledge-guided Arbitrary-Scale Multimodal Medical Image Super-Resolutioncited by 0
  9. 2026Don't Mind the Gaps: Implicit Neural Representations for Resolution-Agnostic Retinal OCT Analysiscited by 0
  10. 2026Dual-Prior Guided Null-Space Learning with Mixture-of-Splines for Arbitrary Medical Slice Super-Resolutioncited by 0
  11. 2026Towards Voxel Spacing Consistency for Medical Image Segmentationcited by 0
  12. 2026PhyMRI-SR: Toward Physics-Aware MRI Image Super-Resolutioncited by 0
3 more
  1. 2026CUST: Clustered Unit-level Similarity Transformer for Lightweight Image Super-Resolutioncited by 0
  2. 2026Coarse-to-fine Framework for Generative MEF via Implicit Neural Representationcited by 0
  3. 2026MedGSSR: Generalizable Medical Image Super-Resolution 3D Reconstruction via Hierarchical Feed-forward Gaussian Splattingcited by 0