CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Rates

• URL: http://arxiv.org/abs/2409.01571v1
• Date: Tue, 3 Sep 2024 03:06:15 GMT
• Title: CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Rates
• Authors: Liutao Yang, Jiahao Huang, Guang Yang, Daoqiang Zhang,
• Abstract summary: Sparse views X-ray computed tomography has emerged as a contemporary technique to mitigate radiation dose degradation. Recent studies utilizing deep learning methods has made promising progress in removing artifacts for Sparse-View Computed Tomography (SVCT) Our study proposes a adaptive reconstruction method to achieve high-performance SVCT reconstruction at any sampling rate.
• Score: 16.985836345715963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sparse views X-ray computed tomography has emerged as a contemporary technique to mitigate radiation dose. Because of the reduced number of projection views, traditional reconstruction methods can lead to severe artifacts. Recently, research studies utilizing deep learning methods has made promising progress in removing artifacts for Sparse-View Computed Tomography (SVCT). However, given the limitations on the generalization capability of deep learning models, current methods usually train models on fixed sampling rates, affecting the usability and flexibility of model deployment in real clinical settings. To address this issue, our study proposes a adaptive reconstruction method to achieve high-performance SVCT reconstruction at any sampling rate. Specifically, we design a novel imaging degradation operator in the proposed sampling diffusion model for SVCT (CT-SDM) to simulate the projection process in the sinogram domain. Thus, the CT-SDM can gradually add projection views to highly undersampled measurements to generalize the full-view sinograms. By choosing an appropriate starting point in diffusion inference, the proposed model can recover the full-view sinograms from any sampling rate with only one trained model. Experiments on several datasets have verified the effectiveness and robustness of our approach, demonstrating its superiority in reconstructing high-quality images from sparse-view CT scans across various sampling rates.

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)
• MSDiff: Multi-Scale Diffusion Model for Ultra-Sparse View CT Reconstruction [5.5805994093893885]
  We propose an ultra-sparse view CT reconstruction method utilizing multi-scale dif-fusion models (MSDiff) The proposed model ingeniously integrates information from both comprehensive sampling and selectively sparse sampling tech-niques. By leveraging the inherent correlations within the projec-tion data, we have designed an equidistant mask, enabling the model to focus its attention more effectively.
  arXiv  Detail & Related papers  (2024-05-09T14:52:32Z)
• CT Reconstruction using Diffusion Posterior Sampling conditioned on a Nonlinear Measurement Model [17.26789842018146]
  Diffusion posterior sampling has been used to produce high quality CT images given low-quality measurements. Current methods rely on a linear model of x-ray CT physics to reconstruct or restore images. We propose a new method that solves the inverse problem of nonlinear CT image reconstruction via diffusion posterior sampling.
  arXiv  Detail & Related papers  (2023-12-03T17:20:11Z)
• Enhancing Low-dose CT Image Reconstruction by Integrating Supervised and Unsupervised Learning [13.17680480211064]
  We propose a hybrid supervised-unsupervised learning framework for X-ray computed tomography (CT) image reconstruction. Each proposed trained block consists of a deterministic MBIR solver and a neural network. We demonstrate the efficacy of this learned hybrid model for low-dose CT image reconstruction with limited training data.
  arXiv  Detail & Related papers  (2023-11-19T20:23:59Z)
• APRF: Anti-Aliasing Projection Representation Field for Inverse Problem in Imaging [74.9262846410559]
  Sparse-view Computed Tomography (SVCT) reconstruction is an ill-posed inverse problem in imaging. Recent works use Implicit Neural Representations (INRs) to build the coordinate-based mapping between sinograms and CT images. We propose a self-supervised SVCT reconstruction method -- Anti-Aliasing Projection Representation Field (APRF) APRF can build the continuous representation between adjacent projection views via the spatial constraints.
  arXiv  Detail & Related papers  (2023-07-11T14:04:12Z)
• Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models [75.52575380824051]
  We present a learning method to optimize sub-sampling patterns for compressed sensing multi-coil MRI. We use a single-step reconstruction based on the posterior mean estimate given by the diffusion model and the MRI measurement process. Our method requires as few as five training images to learn effective sampling patterns.
  arXiv  Detail & Related papers  (2023-06-05T22:09:06Z)
• Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction [12.932897771104825]
  radiation dose in computed tomography (CT) examinations can be significantly reduced by intuitively decreasing the number of projection views. Previous deep learning techniques with sparse-view data require sparse-view/full-view CT image pairs to train the network with supervised manners. We present a fully unsupervised score-based generative model in sinogram domain for sparse-view CT reconstruction.
  arXiv  Detail & Related papers  (2022-11-25T06:49:18Z)
• 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)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• Reference-based Magnetic Resonance Image Reconstruction Using Texture Transforme [86.6394254676369]
  We propose a novel Texture Transformer Module (TTM) for accelerated MRI reconstruction. We formulate the under-sampled data and reference data as queries and keys in a transformer. The proposed TTM can be stacked on prior MRI reconstruction approaches to further improve their performance.
  arXiv  Detail & Related papers  (2021-11-18T03:06:25Z)

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.