DeVAR: Low-Dose CT Denoising via Visual Autoregressive Modeling

• URL: http://arxiv.org/abs/2606.28453v1
• Date: Fri, 26 Jun 2026 09:55:37 GMT
• Title: DeVAR: Low-Dose CT Denoising via Visual Autoregressive Modeling
• Abstract summary: 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.
• Score: 14.938419722549575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computed tomography (CT) plays a crucial role in medical diagnosis, but minimizing radiation exposure while maintaining image quality remains a critical challenge. Low-dose CT (LDCT) protocols reduce radiation risks but inevitably suffer from severe noise and artifacts that compromise diagnostic accuracy. While existing deep learning methods have achieved promising results, there remains a continuous quest for generative paradigms that intrinsically capture global-to-local structural dependencies to better preserve fine anatomical details. To this end, we propose DeVAR, a novel generative framework that applies visual autoregressive modeling (VAR) to LDCT denoising for the first time. Conditioned on global context provided by LDCT prefix tokens, DeVAR progressively generates discrete token maps of the target normal-dose CT (NDCT) via next-scale prediction. Because quantization inherently discards high-frequency information, we introduce a residual refiner to capture subtle anatomical structures beyond the capacity of a discrete codebook. Finally, empowered by a dual-representation hybrid training strategy, our hybrid NDCT decoder seamlessly integrates continuous and discrete latents to reconstruct high-fidelity, detail-preserved images. Extensive experiments on two public datasets demonstrate that DeVAR consistently achieves superior qualitative and quantitative performance compared to state-of-the-art LDCT denoising methods.

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)
• Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling [51.330452704464925]
  We train a latent diffusion model on 1,046 PCCTs and 405,379 EICTs from 145 hospitals.<n>We construct a large-scale dataset of over 17,316 publicly available EICTs enhanced to PCCT-like quality.<n>Our results suggest emerging imaging advances can be systematically distilled into routine EICT using limited high-quality scans as reference.
  arXiv  Detail & Related papers  (2026-04-08T17:47:35Z)
• 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)
• Tunable-Generalization Diffusion Powered by Self-Supervised Contextual Sub-Data for Low-Dose CT Reconstruction [5.107409624991683]
  TUnable-geneRalizatioN Diffusion (TurnDiff) is powered by self-supervised contextual sub-data for low-dose CT reconstruction.<n>TurnDiff consistently outperforms state-of-the-art methods in both reconstruction and generalization.
  arXiv  Detail & Related papers  (2025-09-28T13:50:29Z)
• DenoMamba: A fused state-space model for low-dose CT denoising [6.468495781611433]
  Low-dose computed tomography (LDCT) lower potential risks linked to radiation exposure.<n>LDCT denoising is based on neural network models that learn data-driven image priors to separate noise evoked by dose reduction from underlying tissue signals.<n>DenoMamba is a novel denoising method based on state-space modeling (SSM) that efficiently captures short- and long-range context in medical images.
  arXiv  Detail & Related papers  (2024-09-19T21:32:07Z)
• 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)
• Cross-domain Denoising for Low-dose Multi-frame Spiral Computed Tomography [20.463308418655526]
  X-ray exposure raises concerns about potential health risks such as cancer. The desire for lower radiation doses has driven researchers to improve reconstruction quality. This paper proposes a two-stage method for the commercially available multi-slice spiral CT scanners.
  arXiv  Detail & Related papers  (2023-04-21T09:30:22Z)
• Self-supervised Physics-based Denoising for Computed Tomography [2.2758845733923687]
  Computed Tomography (CT) imposes risk on the patients due to its inherent X-ray radiation. Lowering the radiation dose reduces the health risks but leads to noisier measurements, which decreases the tissue contrast and causes artifacts in CT images. Modern deep learning noise suppression methods alleviate the challenge but require low-noise-high-noise CT image pairs for training. We introduce a new self-supervised approach for CT denoising Noise2NoiseTD-ANM that can be trained without the high-dose CT projection ground truth images.
  arXiv  Detail & Related papers  (2022-11-01T20:58:50Z)
• 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)
• No-reference denoising of low-dose CT projections [2.7716102039510564]
  Low-dose computed tomography (LDCT) became a clear trend in radiology with an aspiration to refrain from delivering excessive X-ray radiation to the patients. The reduction of the radiation dose decreases the risks to the patients but raises the noise level, affecting the quality of the images and their ultimate diagnostic value. One mitigation option is to consider pairs of low-dose and high-dose CT projections to train a denoising model using deep learning algorithms; however, such pairs are rarely available in practice. In this paper, we present a new self-supervised method for CT denoising. Unlike existing self-supervised
  arXiv  Detail & Related papers  (2021-02-03T13:51:33Z)
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images [152.34988415258988]
  Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. To address these challenges, a novel COVID-19 Deep Lung Infection Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices.
  arXiv  Detail & Related papers  (2020-04-22T07:30:56Z)

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.