Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT

• URL: http://arxiv.org/abs/2310.06949v1
• Date: Tue, 10 Oct 2023 19:08:57 GMT
• Title: Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT
• Authors: Wenjun Xia and Yongyi Shi and Chuang Niu and Wenxiang Cong and Ge Wang
• Abstract summary: We introduce an iterative reconstruction algorithm regularized by a diffusion prior. We also incorporate the Nesterov momentum acceleration technique. Our method offers a potential pathway to high-definition CT image reconstruction with minimized radiation.
• Score: 9.866443235747287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computed tomography (CT) involves a patient's exposure to ionizing radiation. To reduce the radiation dose, we can either lower the X-ray photon count or down-sample projection views. However, either of the ways often compromises image quality. To address this challenge, here we introduce an iterative reconstruction algorithm regularized by a diffusion prior. Drawing on the exceptional imaging prowess of the denoising diffusion probabilistic model (DDPM), we merge it with a reconstruction procedure that prioritizes data fidelity. This fusion capitalizes on the merits of both techniques, delivering exceptional reconstruction results in an unsupervised framework. To further enhance the efficiency of the reconstruction process, we incorporate the Nesterov momentum acceleration technique. This enhancement facilitates superior diffusion sampling in fewer steps. As demonstrated in our experiments, our method offers a potential pathway to high-definition CT image reconstruction with minimized radiation.

Related papers

• Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Models [1.4019041243188557]
  We propose a novel computed tomography (CT) reconstruction method by combining the denoising diffusion probabilistic model with iterative CT reconstruction. We demonstrated the effectiveness of the proposed method through the sparse-projection CT reconstruction of 1/10 projection data.
  arXiv  Detail & Related papers  (2024-08-06T12:55:17Z)
• 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)
• Score-Based Diffusion Models for Photoacoustic Tomography Image Reconstruction [6.497421531514728]
  Photoacoustic tomography (PAT) is a rapidly-evolving medical imaging modality that combines optical absorption contrast with ultrasound imaging depth. One challenge in PAT is image reconstruction with inadequate acoustic signals due to limited sensor coverage or due to the density of the transducer array. We use score-based diffusion models to solve the inverse problem of reconstructing an image from limited PAT measurements.
  arXiv  Detail & Related papers  (2024-03-30T20:34:49Z)
• Efficient Diffusion Model for Image Restoration by Residual Shifting [63.02725947015132]
  This study proposes a novel and efficient diffusion model for image restoration. Our method avoids the need for post-acceleration during inference, thereby avoiding the associated performance deterioration. Our method achieves superior or comparable performance to current state-of-the-art methods on three classical IR tasks.
  arXiv  Detail & Related papers  (2024-03-12T05:06:07Z)
• 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)
• Parallel Diffusion Model-based Sparse-view Cone-beam Breast CT [7.712142153700843]
  We transform the cutting-edge Denoising Diffusion Probabilistic Model (DDPM) into a parallel framework for sub-volume-based sparse-view breast CT image reconstruction. Our experimental findings reveal that this method delivers competitive reconstruction performance at half to one-third of the standard radiation doses.
  arXiv  Detail & Related papers  (2023-03-22T18:55:43Z)
• One Sample Diffusion Model in Projection Domain for Low-Dose CT Imaging [10.797632196651731]
  Low-dose computed tomography (CT) plays a significant role in reducing the radiation risk in clinical applications. With the rapid development and wide application of deep learning, it has brought new directions for the development of low-dose CT imaging algorithms. We propose a fully unsupervised one sample diffusion model (OSDM)in projection domain for low-dose CT reconstruction. The results prove that OSDM is practical and effective model for reducing the artifacts and preserving the image quality.
  arXiv  Detail & Related papers  (2022-12-07T13:39:23Z)
• REGAS: REspiratory-GAted Synthesis of Views for Multi-Phase CBCT Reconstruction from a single 3D CBCT Acquisition [75.64791080418162]
  REGAS proposes a self-supervised method to synthesize the undersampled tomographic views and mitigate aliasing artifacts in reconstructed images. To address the large memory cost of deep neural networks on high resolution 4D data, REGAS introduces a novel Ray Path Transformation (RPT) that allows for distributed, differentiable forward projections.
  arXiv  Detail & Related papers  (2022-08-17T03:42:19Z)
• Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction [108.06731611196291]
  We develop a multi-channel convolutional analysis operator learning (MCAOL) method to exploit common spatial features within attenuation images at different energies. We propose an optimization method which jointly reconstructs the attenuation images at low and high energies with a mixed norm regularization on the sparse features.
  arXiv  Detail & Related papers  (2022-03-10T14:22:54Z)
• Towards performant and reliable undersampled MR reconstruction via diffusion model sampling [67.73698021297022]
  DiffuseRecon is a novel diffusion model-based MR reconstruction method. It guides the generation process based on the observed signals. It does not require additional training on specific acceleration factors.
  arXiv  Detail & Related papers  (2022-03-08T02:25:38Z)
• Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model [24.024765099719886]
  Iterative reconstruction is one of the most promising ways to compensate for the increased noise due to reduction of photon flux. In this work we integrate the data-consistency as a conditional term into the iterative generative model for low-dose CT. The distance between the reconstructed image and the manifold is minimized along with data fidelity during reconstruction.
  arXiv  Detail & Related papers  (2020-09-27T06:36:39Z)

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.