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

• URL: http://arxiv.org/abs/2404.04878v1
• Date: Sun, 7 Apr 2024 08:48:01 GMT
• Title: CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data
• Authors: 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,
• Abstract summary: CycleINR is a novel enhanced Implicit Neural Representation model for 3D medical data super-resolution. We introduce a new metric, Slice-wise Noise Level Inconsistency (SNLI), to quantitatively assess inter-slice noise level inconsistency.
• Score: 19.085329423308938
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Improving 3D Medical Image Segmentation at Boundary Regions using Local Self-attention and Global Volume Mixing [14.0825980706386]
  Volumetric medical image segmentation is a fundamental problem in medical image analysis where the objective is to accurately classify a given 3D volumetric medical image with voxel-level precision. In this work, we propose a novel hierarchical encoder-decoder-based framework that strives to explicitly capture the local and global dependencies for 3D medical image segmentation. The proposed framework exploits local volume-based self-attention to encode the local dependencies at high resolution and introduces a novel volumetric-mixer to capture the global dependencies at low-resolution feature representations.
  arXiv  Detail & Related papers  (2024-10-20T11:08:38Z)
• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  Deep neural networks have shown great potential for reconstructing high-fidelity images from undersampled measurements. Our model is based on neural operators, a discretization-agnostic architecture. Our inference speed is also 1,400x faster than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• SIMPLE: Simultaneous Multi-Plane Self-Supervised Learning for Isotropic MRI Restoration from Anisotropic Data [1.980639720136382]
  Traditional MRI scans often yield anisotropic data due to technical constraints. Super-resolution techniques aim to address these limitations by reconstructing isotropic high-resolution images from anisotropic data. We introduce SIMPLE, a Simultaneous Multi-Plane Self-Supervised Learning approach for isotropic MRI restoration from anisotropic data.
  arXiv  Detail & Related papers  (2024-08-23T13:48:11Z)
• Super-resolution of biomedical volumes with 2D supervision [84.5255884646906]
  Masked slice diffusion for super-resolution exploits the inherent equivalence in the data-generating distribution across all spatial dimensions of biological specimens. We focus on the application of SliceR to stimulated histology (SRH), characterized by its rapid acquisition of high-resolution 2D images but slow and costly optical z-sectioning.
  arXiv  Detail & Related papers  (2024-04-15T02:41:55Z)
• SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging [59.78761085714715]
  This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework for liver lesion classification. The proposed framework has been validated through comprehensive experiments on two clinical datasets. To support the scientific community, we are releasing our extensive multi-phase MR dataset for liver lesion analysis to the public.
  arXiv  Detail & Related papers  (2024-02-27T06:32:56Z)
• Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations [9.683341998041634]
  Implicit Neural Representations (INR) proposed to learn two different contrasts of complementary views in a continuous spatial function. Our model provides realistic super-resolution across different pairs of contrasts in our experiments with three datasets.
  arXiv  Detail & Related papers  (2023-03-27T10:18:42Z)
• CLADE: Cycle Loss Augmented Degradation Enhancement for Unpaired Super-Resolution of Anisotropic Medical Images [0.06597195879147556]
  Three-dimensional (3D) imaging is popular in medical applications, however, anisotropic 3D volumes with thick, low-spatial-resolution slices are often acquired to reduce scan times. Deep learning (DL) offers a solution to recover high-resolution features through super-resolution reconstruction (SRR) We show the feasibility of CLADE in abdominal MRI and abdominal CT and demonstrate significant improvements in CLADE image quality over low-resolution volumes.
  arXiv  Detail & Related papers  (2023-03-21T13:19:51Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• Joint Semi-supervised 3D Super-Resolution and Segmentation with Mixed Adversarial Gaussian Domain Adaptation [13.477290490742224]
  Super-resolution in medical imaging aims to increase the resolution of images but is conventionally trained on features from low resolution datasets. Here we propose a semi-supervised multi-task generative adversarial network (Gemini-GAN) that performs joint super-resolution of the images and their labels. Our proposed approach is extensively evaluated on two transnational multi-ethnic populations of 1,331 and 205 adults respectively.
  arXiv  Detail & Related papers  (2021-07-16T15:42:39Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.