Neighbor2Inverse: Self-Supervised Denoising for Low-Dose Region-of-Interest Phase Contrast CT

• URL: http://arxiv.org/abs/2605.01075v1
• Date: Fri, 01 May 2026 20:17:33 GMT
• Title: Neighbor2Inverse: Self-Supervised Denoising for Low-Dose Region-of-Interest Phase Contrast CT
• Abstract summary: Supervised convolutional-neural-network-based denoising can restore image quality but depends on paired low- and high-dose datasets.<n>We introduce Neighbor2Inverse, a self-supervised denoising framework designed for low-dose PBI-CT that generalizes to clinical CT.
• Score: 3.292132675169194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Propagation-based X-ray phase-contrast imaging (PBI) enables high-contrast visualization of lung structures and holds strong medical potential. However, safe translation to the clinic will require a substantial radiation dose reduction, which inevitably increases image noise. Supervised convolutional-neural-network-based denoising can restore image quality but depends on paired low- and high-dose datasets, which are rarely available in practice. Self-supervised methods avoid this limitation, yet most are not well adapted to the inverse problem of PBI computed tomography (CT). We introduce Neighbor2Inverse, a self-supervised denoising framework designed for low-dose PBI-CT that generalizes to clinical CT. Building on the Neighbor2Neighbor principle, each noisy projection is subsampled into two variants that preserve structural information but contain independent noise realizations. These are reconstructed separately, and the resulting pairs are used to train a denoising network directly in the image domain. We benchmark the proposed method against established analytical and self-supervised denoising approaches. In region-of-interest PBI CT experiments, Neighbor2Inverse achieves superior noise suppression while preserving fine structural details, as demonstrated by improved contrast-to-noise ratio, spatial resolution, and composite image quality metrics. Competitive performance is also observed on clinical CT data under simulated low-dose conditions. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Code, data, and interactive figures are available at https://github.com/J-3TO/Neighbor2Inverse.

Related papers

• A deep dictionary network-based foundation model for ultra-low-dose CT denoising [49.70293041079032]
  Ultra-low-dose computed tomography (ULDCT) reduces radiation exposure but suffers from severe noise that degrades diagnostic image quality.<n>Existing deep learning-based denoising methods are typically trained in an organ-specific fashion, resulting in limited generalization across heterogeneous multi?organ imaging scenarios.<n>We propose an architecture?able foundation model based on the deep dictionary network (DDN) for unified multi-organ ULDCT denoising.
  arXiv  Detail & Related papers  (2026-09-11T05:14:08Z)
• DeVAR: Low-Dose CT Denoising via Visual Autoregressive Modeling [14.938419722549575]
  Low-dose CT (LDCT) protocols reduce radiation risks but inevitably suffer from severe noise and artifacts that compromise diagnostic accuracy.<n>We propose a novel generative framework that applies visual autore modeling to LDCT denoising for the first time.<n>Our hybrid NDCT decoder seamlessly integrates continuous and discrete latents to reconstruct high-fidelity, detail-preserved images.
  arXiv  Detail & Related papers  (2026-06-26T09:55:37Z)
• IPv2: An Improved Image Purification Strategy for Real-World Ultra-Low-Dose Lung CT Denoising [1.6574413179773761]
  The image purification strategy constructs an intermediate distribution with aligned anatomical structures.<n>The strategy suppresses noise only in the chest wall and bone regions while leaving the image background untreated.<n>This strategy lacks a dedicated mechanism for denoising the lung parenchyma.
  arXiv  Detail & Related papers  (2026-02-22T19:28:31Z)
• SDPA++: A General Framework for Self-Supervised Denoising with Patch Aggregation [1.1123754733827187]
  We propose SDPA++: A General Framework for Self-Supervised Denoising with Patch Aggregation.<n>Our novel approach leverages only noisy OCT images by first generating pseudo-ground-truth images through self-fusion and self-supervised denoising.<n>Performance improvements are validated via metrics such as Contrast-to-Noise Ratio (CNR), Mean Square Ratio (MSR), Texture Preservation (TP), and Edge Preservation (EP) on the real-world dataset from the IEEE Video and Image Processing Cup.
  arXiv  Detail & Related papers  (2025-10-19T04:05:34Z)
• A Denoising Framework for Real-World Ultra-Low Dose Lung CT Images Based on an Image Purification Strategy [1.6574413179773761]
  Ultra-low dose CT (uLDCT) significantly reduces radiation exposure but introduces severe noise and artifacts.<n>It also leads to substantial spatial misalignment between uLDCT and normal dose CT (NDCT) image pairs.<n>This paper proposes an innovative denoising framework based on an Image Purification (IP) strategy.
  arXiv  Detail & Related papers  (2025-10-08T19:40:38Z)
• CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
  We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism. We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
  arXiv  Detail & Related papers  (2024-03-21T15:13:36Z)
• WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising [74.14134385961775]
  We introduce a novel self-supervised CT image denoising method called WIA-LD2ND, only using NDCT data. WIA-LD2ND comprises two modules: Wavelet-based Image Alignment (WIA) and Frequency-Aware Multi-scale Loss (FAM)
  arXiv  Detail & Related papers  (2024-03-18T11:20:11Z)
• Rotational Augmented Noise2Inverse for Low-dose Computed Tomography Reconstruction [83.73429628413773]
  Supervised deep learning methods have shown the ability to remove noise in images but require accurate ground truth. We propose a novel self-supervised framework for LDCT, in which ground truth is not required for training the convolutional neural network (CNN) Numerical and experimental results show that the reconstruction accuracy of N2I with sparse views is degrading while the proposed rotational augmented Noise2Inverse (RAN2I) method keeps better image quality over a different range of sampling angles.
  arXiv  Detail & Related papers  (2023-12-19T22:40:51Z)
• InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images [53.4351366246531]
  We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
  arXiv  Detail & Related papers  (2021-12-23T15:52:37Z)
• Low-Dose CT Denoising Using a Structure-Preserving Kernel Prediction Network [10.09577595969254]
  CNN-based approaches treat all regions of the CT image equally and can be inefficient when fine-grained structures coexist with non-uniformly distributed noises. We propose a Structure-preserving Kernel Prediction Network (StructKPN) that combines the kernel prediction network with a structure-aware loss function. Our approach achieved superior performance on both synthetic and non-synthetic datasets, and better preserves structures that are highly desired in clinical screening and low-dose protocol optimization.
  arXiv  Detail & Related papers  (2021-05-31T07:42:21Z)
• Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images [98.82804259905478]
  We present Neighbor2Neighbor to train an effective image denoising model with only noisy images. In detail, input and target used to train a network are images sub-sampled from the same noisy image. A denoising network is trained on sub-sampled training pairs generated in the first stage, with a proposed regularizer as additional loss for better performance.
  arXiv  Detail & Related papers  (2021-01-08T02:03:25Z)
• Self-supervised Denoising via Diffeomorphic Template Estimation: Application to Optical Coherence Tomography [6.197149831796131]
  We propose a joint diffeomorphic template estimation and denoising framework which enables the use of self-supervised denoising for motion deformed repeat acquisitions. Strong qualitative and quantitative improvements are achieved in denoising OCT images, with generic utility in any imaging modality amenable to multiple exposures.
  arXiv  Detail & Related papers  (2020-08-18T16:52:10Z)

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.