Joint Rigid Motion Correction and Sparse-View CT via Self-Calibrating Neural Field

• URL: http://arxiv.org/abs/2210.12731v1
• Date: Sun, 23 Oct 2022 13:55:07 GMT
• Title: Joint Rigid Motion Correction and Sparse-View CT via Self-Calibrating Neural Field
• Authors: Qing Wu, Xin Li, Hongjiang Wei, Jingyi Yu, Yuyao Zhang
• Abstract summary: NeRF has widely received attention in Sparse-View (SV) CT reconstruction problems as a self-supervised deep learning framework. Existing NeRF-based SVCT methods strictly suppose there is completely no relative motion during the CT acquisition. This work proposes a self-calibrating neural field that recovers the artifacts-free image from the rigid motion-corrupted SV measurement.
• Score: 37.86878619100209
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural Radiance Field (NeRF) has widely received attention in Sparse-View (SV) CT reconstruction problems as a self-supervised deep learning framework. NeRF-based SVCT methods model the desired CT image as a continuous function that maps coordinates to intensities and then train a Multi-Layer Perceptron (MLP) to learn the function by minimizing loss on the SV measurement. Thanks to the continuous representation provided by NeRF, the function can be approximated well and thus the high-quality CT image is reconstructed. However, existing NeRF-based SVCT methods strictly suppose there is completely no relative motion during the CT acquisition because they require accurate projection poses to simulate the X-rays that scan the SV sinogram. Therefore, these methods suffer from severe performance drops for real SVCT imaging with motion. To this end, this work proposes a self-calibrating neural field that recovers the artifacts-free image from the rigid motion-corrupted SV measurement without using any external data. Specifically, we parametrize the coarse projection poses caused by rigid motion as trainable variables and then jointly optimize these variables and the MLP. We perform numerical experiments on a public COVID-19 CT dataset. The results indicate that our model significantly outperforms two latest NeRF-based methods for SVCT reconstruction with four different levels of rigid motion.

Related papers

• CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Rates [16.985836345715963]
  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.
  arXiv  Detail & Related papers  (2024-09-03T03:06:15Z)
• FCDM: Sparse-view Sinogram Inpainting with Frequency Domain Convolution Enhanced Diffusion Models [14.043383277622874]
  We introduce a novel diffusion-based inpainting framework tailored for sinogram data. FCDM significantly outperforms existing methods, achieving SSIM over 0.95 and PSNR above 30 dB, with improvements of up to 33% in SSIM and 29% in PSNR compared to baselines.
  arXiv  Detail & Related papers  (2024-08-26T12:31:38Z)
• 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)
• 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)
• Deep Cardiac MRI Reconstruction with ADMM [7.694990352622926]
  We present a deep learning (DL)-based method for accelerated cine and multi-contrast reconstruction in the context of cardiac imaging. Our method optimize in both the image and k-space domains, allowing for high reconstruction fidelity.
  arXiv  Detail & Related papers  (2023-10-10T13:46:11Z)
• 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)
• 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)
• Geometric Constraints Enable Self-Supervised Sinogram Inpainting in Sparse-View Tomography [7.416898042520079]
  Sparse-angle tomographic scans reduce radiation and accelerate data acquisition, but suffer from image artifacts and noise. Existing image processing algorithms can restore CT reconstruction quality but often require large training data sets or can not be used for truncated objects. This work presents a self-supervised projection inpainting method that allows optimizing missing projective views via gradient-based optimization.
  arXiv  Detail & Related papers  (2023-02-13T15:15:18Z)
• DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction [13.358197688568463]
  iodine radiation in the imaging process induces irreversible injury. Iterative models are proposed to alleviate the appeared artifacts in sparse-view CT images, but the cost is too expensive. We propose textbfDual-textbfDomain textbfDuDoTrans to reconstruct CT image with both the enhanced and raw sinograms.
  arXiv  Detail & Related papers  (2021-11-21T10:41:07Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

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.