Patch Triplet Similarity Purification for Guided Real-World Low-Dose CT Image Denoising

• URL: http://arxiv.org/abs/2502.00253v1
• Date: Sat, 01 Feb 2025 01:24:41 GMT
• Title: Patch Triplet Similarity Purification for Guided Real-World Low-Dose CT Image Denoising
• Authors: Junhao Long, Fengwei Yang, Juncheng Yan, Baoping Zhang, Chao Jin, Jian Yang, Changliang Zou, Jun Xu,
• Abstract summary: Non-contrast CT (NCCT) images share content characteristics to corresponding NDCT images in a three-phase scan. We propose to incorporate clean NCCT images as useful guidance for the learning of real-world LDCT image denoising networks. We modify two image denoising transformers of SwinIR and HAT to accommodate the NCCT image guidance.
• Score: 13.095377482567194
• License:
• Abstract: Image denoising of low-dose computed tomography (LDCT) is an important problem for clinical diagnosis with reduced radiation exposure. Previous methods are mostly trained with pairs of synthetic or misaligned LDCT and normal-dose CT (NDCT) images. However, trained with synthetic noise or misaligned LDCT/NDCT image pairs, the denoising networks would suffer from blurry structure or motion artifacts. Since non-contrast CT (NCCT) images share the content characteristics to the corresponding NDCT images in a three-phase scan, they can potentially provide useful information for real-world LDCT image denoising. To exploit this aspect, in this paper, we propose to incorporate clean NCCT images as useful guidance for the learning of real-world LDCT image denoising networks. To alleviate the issue of spatial misalignment in training data, we design a new Patch Triplet Similarity Purification (PTSP) strategy to select highly similar patch (instead of image) triplets of LDCT, NDCT, and NCCT images for network training. Furthermore, we modify two image denoising transformers of SwinIR and HAT to accommodate the NCCT image guidance, by replacing vanilla self-attention with cross-attention. On our collected clinical dataset, the modified transformers trained with the data selected by our PTSP strategy show better performance than 15 comparison methods on real-world LDCT image denoising. Ablation studies validate the effectiveness of our NCCT image guidance and PTSP strategy. We will publicly release our data and code.

Related papers

• 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)
• Feature-oriented Deep Learning Framework for Pulmonary Cone-beam CT (CBCT) Enhancement with Multi-task Customized Perceptual Loss [9.59233136691378]
  Cone-beam computed tomography (CBCT) is routinely collected during image-guided radiation therapy. Recent deep learning-based CBCT enhancement methods have shown promising results in suppressing artifacts. We propose a novel feature-oriented deep learning framework that translates low-quality CBCT images into high-quality CT-like imaging.
  arXiv  Detail & Related papers  (2023-11-01T10:09:01Z)
• Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training [51.16994853817024]
  This work focuses on designing an effective pre-training framework for 3D radiology images. We introduce Disruptive Autoencoders, a pre-training framework that attempts to reconstruct the original image from disruptions created by a combination of local masking and low-level perturbations. The proposed pre-training framework is tested across multiple downstream tasks and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-07-31T17:59:42Z)
• Multi-View Vertebra Localization and Identification from CT Images [57.56509107412658]
  We propose a multi-view vertebra localization and identification from CT images. We convert the 3D problem into a 2D localization and identification task on different views. Our method can learn the multi-view global information naturally.
  arXiv  Detail & Related papers  (2023-07-24T14:43:07Z)
• Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging [65.47834983591957]
  We propose deep learning based neural networks to correct axial and coronal motion artifacts in OCT based on a single scan. The experimental result shows that the proposed method can effectively correct motion artifacts and achieve smaller error than other methods.
  arXiv  Detail & Related papers  (2023-05-27T03:55:19Z)
• QS-ADN: Quasi-Supervised Artifact Disentanglement Network for Low-Dose CT Image Denoising by Local Similarity Among Unpaired Data [10.745277107045949]
  This paper introduces a new learning mode, called quasi-supervised learning, to empower the ADN for LDCT image denoising. The proposed method is different from (but compatible with) supervised and semi-supervised learning modes and can be easily implemented by modifying existing networks. The experimental results show that the method is competitive with state-of-the-art methods in terms of noise suppression and contextual fidelity.
  arXiv  Detail & Related papers  (2023-02-08T07:19:13Z)
• Self Supervised Low Dose Computed Tomography Image Denoising Using Invertible Network Exploiting Inter Slice Congruence [20.965610734723636]
  This study proposes a novel method for self-supervised low-dose CT denoising to alleviate the requirement of paired LDCT and NDCT images. We have trained an invertible neural network to minimize the pixel-based mean square distance between a noisy slice and the average of its two immediate adjacent noisy slices.
  arXiv  Detail & Related papers  (2022-11-03T07:16:18Z)
• Patch-wise Deep Metric Learning for Unsupervised Low-Dose CT Denoising [33.706959549595496]
  We propose a novel unsupervised learning approach for lowdose CT reconstruction using patch-wise deep metric learning. The key idea is to learn embedding space by pulling the positive pairs of image patches which shares the same anatomical structure, and pushing the negative pairs which have same noise level each other. Experimental results confirm that our deep metric learning plays a critical role in producing high quality denoised images without CT number shift.
  arXiv  Detail & Related papers  (2022-07-06T00:58:11Z)
• Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism [4.998352078907441]
  Low radiation dose may result in increased noise and artifacts, which greatly affected the clinical diagnosis. To obtain high-quality Total-body Low-dose CT (LDCT) images, previous deep-learning-based research work has introduced various network architectures. In this paper, we propose a novel intra-task knowledge transfer method that leverages the distilled knowledge from NDCT images.
  arXiv  Detail & Related papers  (2021-12-01T06:46:38Z)
• CyTran: A Cycle-Consistent Transformer with Multi-Level Consistency for Non-Contrast to Contrast CT Translation [56.622832383316215]
  We propose a novel approach to translate unpaired contrast computed tomography (CT) scans to non-contrast CT scans. Our approach is based on cycle-consistent generative adversarial convolutional transformers, for short, CyTran. Our empirical results show that CyTran outperforms all competing methods.
  arXiv  Detail & Related papers  (2021-10-12T23:25:03Z)

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.