Enhanced Low-Dose CT Image Reconstruction by Domain and Task Shifting Gaussian Denoisers

• URL: http://arxiv.org/abs/2403.03551v4
• Date: Fri, 14 Mar 2025 12:30:28 GMT
• Title: Enhanced Low-Dose CT Image Reconstruction by Domain and Task Shifting Gaussian Denoisers
• Authors: Tim Selig, Thomas März, Martin Storath, Andreas Weinmann,
• Abstract summary: Computed tomography from a low radiation dose (LDCT) is challenging due to high noise in the projection data.<n>We propose a method combining the simplicity and efficiency of two-stage methods with state-of-the-art reconstruction quality.
• Score: 3.4748713192043876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computed tomography from a low radiation dose (LDCT) is challenging due to high noise in the projection data. Popular approaches for LDCT image reconstruction are two-stage methods, typically consisting of the filtered backprojection (FBP) algorithm followed by a neural network for LDCT image enhancement. Two-stage methods are attractive for their simplicity and potential for computational efficiency, typically requiring only a single FBP and a neural network forward pass for inference. However, the best reconstruction quality is currently achieved by unrolled iterative methods (Learned Primal-Dual and ItNet), which are more complex and thus have a higher computational cost for training and inference. We propose a method combining the simplicity and efficiency of two-stage methods with state-of-the-art reconstruction quality. Our strategy utilizes a neural network pretrained for Gaussian noise removal from natural grayscale images, fine-tuned for LDCT image enhancement. We call this method FBP-DTSGD (Domain and Task Shifted Gaussian Denoisers) as the fine-tuning is a task shift from Gaussian denoising to enhancing LDCT images and a domain shift from natural grayscale to LDCT images. An ablation study with three different pretrained Gaussian denoisers indicates that the performance of FBP-DTSGD does not depend on a specific denoising architecture, suggesting future advancements in Gaussian denoising could benefit the method. The study also shows that pretraining on natural images enhances LDCT reconstruction quality, especially with limited training data. Notably, pretraining involves no additional cost, as existing pretrained models are used. The proposed method currently holds the top mean position in the LoDoPaB-CT challenge.

Related papers

• Accelerated Optimization of Implicit Neural Representations for CT Reconstruction [0.3222802562733786]
  implicit neural representations (INRs) have been recently proposed for reconstruction in low-dose/sparse-view X-ray computed tomography (CT) An INR represents a CT image as a small-scale neural network that takes spatial coordinates as inputs and outputs attenuation values. This paper investigates strategies to accelerate the optimization of INRs for CT reconstruction.
  arXiv  Detail & Related papers  (2025-04-18T00:52:56Z)
• Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques [7.694256285730863]
  In this paper, we introduce the beta noise distribution, which provides flexibility in adjusting noise levels. High Noise Improved Consistency Training (HN-iCT) is trained in a supervised fashion. Our results indicate that unconditional image generation using HN-iCT significantly outperforms basic CT and iCT training techniques with NFE=1.
  arXiv  Detail & Related papers  (2024-11-19T02:48:36Z)
• CoCPF: Coordinate-based Continuous Projection Field for Ill-Posed Inverse Problem in Imaging [78.734927709231]
  Sparse-view computed tomography (SVCT) reconstruction aims to acquire CT images based on sparsely-sampled measurements. Due to ill-posedness, implicit neural representation (INR) techniques may leave considerable holes'' (i.e., unmodeled spaces) in their fields, leading to sub-optimal results. We propose the Coordinate-based Continuous Projection Field (CoCPF), which aims to build hole-free representation fields for SVCT reconstruction.
  arXiv  Detail & Related papers  (2024-06-21T08:38:30Z)
• 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)
• Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation [4.43162303545687]
  Deep learning-based Generative Models have the potential to convert low-resolution CT images into high-resolution counterparts without long acquisition times and increased radiation exposure in thin-slice CT imaging. procuring appropriate training data for these Super-Resolution (SR) models is challenging. Previous SR research has simulated thick-slice CT images from thin-slice CT images to create training pairs. We introduce a simple yet realistic method to generate thick CT images from thin-slice CT images, facilitating the creation of training pairs for SR algorithms.
  arXiv  Detail & Related papers  (2023-07-02T11:09:08Z)
• 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)
• Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising [10.854795474105366]
  Denoising low-dose computed tomography (CT) images is a critical task in medical image computing. Existing unsupervised deep learning-based methods often require training with a large number of low-dose CT images. We propose a novel unsupervised method that only utilizes normal-dose CT images during training, enabling zero-shot denoising of low-dose CT images.
  arXiv  Detail & Related papers  (2023-05-25T09:38:52Z)
• Zero-shot Blind Image Denoising via Implicit Neural Representations [77.79032012459243]
  We propose an alternative denoising strategy that leverages the architectural inductive bias of implicit neural representations (INRs) We show that our method outperforms existing zero-shot denoising methods under an extensive set of low-noise or real-noise scenarios.
  arXiv  Detail & Related papers  (2022-04-05T12:46:36Z)
• Convolutional Neural Network to Restore Low-Dose Digital Breast Tomosynthesis Projections in a Variance Stabilization Domain [15.149874383250236]
  convolution neural network (CNN) proposed to restore low-dose (LD) projections to image quality equivalent to a standard full-dose (FD) acquisition. Network achieved superior results in terms of the mean squared error (MNSE), normalized training time and noise spatial correlation compared with networks trained with traditional data-driven methods.
  arXiv  Detail & Related papers  (2022-03-22T13:31:47Z)
• Image reconstruction algorithms in radio interferometry: from handcrafted to learned denoisers [7.1439425093981574]
  We introduce a new class of iterative image reconstruction algorithms for radio interferometry, inspired by plug-and-play methods. The approach consists in learning a prior image model by training a deep neural network (DNN) as a denoiser. We plug the learned denoiser into the forward-backward optimization algorithm, resulting in a simple iterative structure alternating a denoising step with a gradient-descent data-fidelity step.
  arXiv  Detail & Related papers  (2022-02-25T20:26:33Z)
• 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)
• 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)
• Self-Supervised Training For Low Dose CT Reconstruction [0.0]
  This study defines a training scheme to use low-dose sinograms as their own training targets. We apply the self-supervision principle in the projection domain where the noise is element-wise independent. We demonstrate that our method outperforms both conventional and compressed sensing based iterative reconstruction methods.
  arXiv  Detail & Related papers  (2020-10-25T22:02:14Z)
• Progressive Training of Multi-level Wavelet Residual Networks for Image Denoising [80.10533234415237]
  This paper presents a multi-level wavelet residual network (MWRN) architecture as well as a progressive training scheme to improve image denoising performance. Experiments on both synthetic and real-world noisy images show that our PT-MWRN performs favorably against the state-of-the-art denoising methods.
  arXiv  Detail & Related papers  (2020-10-23T14:14:00Z)
• Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
  Blind image restoration is a common yet challenging problem in computer vision. We propose a novel blind image restoration method, aiming to integrate both the advantages of them. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
  arXiv  Detail & Related papers  (2020-08-25T03:30:53Z)
• Cascaded Convolutional Neural Networks with Perceptual Loss for Low Dose CT Denoising [0.0]
  Low Dose CT Denoising research aims to reduce the risks of radiation exposure to patients. Recent approaches that use mean-squared-error (MSE) tend to over smooth the image resulting in loss of fine structural details in low contrast regions of the image. We show that our method outperforms related works and more effectively reconstructs fine structural details in low contrast regions of the image.
  arXiv  Detail & Related papers  (2020-06-26T00:35:26Z)
• The Power of Triply Complementary Priors for Image Compressive Sensing [89.14144796591685]
  We propose a joint low-rank deep (LRD) image model, which contains a pair of complementaryly trip priors. We then propose a novel hybrid plug-and-play framework based on the LRD model for image CS. To make the optimization tractable, a simple yet effective algorithm is proposed to solve the proposed H-based image CS problem.
  arXiv  Detail & Related papers  (2020-05-16T08:17:44Z)
• Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline [100.5353614588565]
  We propose to incorporate the domain knowledge of the LDR image formation pipeline into our model. We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization. We demonstrate that the proposed method performs favorably against state-of-the-art single-image HDR reconstruction algorithms.
  arXiv  Detail & Related papers  (2020-04-02T17:59:04Z)

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.