Fan-Beam CT Reconstruction for Unaligned Sparse-View X-ray Baggage Dataset

• URL: http://arxiv.org/abs/2412.03036v1
• Date: Wed, 04 Dec 2024 05:16:54 GMT
• Title: Fan-Beam CT Reconstruction for Unaligned Sparse-View X-ray Baggage Dataset
• Authors: Shin Kim,
• Abstract summary: We present a calibration and reconstruction method using an unaligned sparse multi-view X-ray baggage dataset. Our approach integrates multi-spectral neural attenuation field reconstruction with Linear pushbroom (LPB) camera model pose optimization.
• Score: 0.0
• License:
• Abstract: Computed Tomography (CT) is a technology that reconstructs cross-sectional images using X-ray images taken from multiple directions. In CT, hundreds of X-ray images acquired as the X-ray source and detector rotate around a central axis, are used for precise reconstruction. In security baggage inspection, X-ray imaging is also widely used; however, unlike the rotating systems in medical CT, stationary X-ray systems are more common, and publicly available reconstructed data are limited. This makes it challenging to obtain large-scale 3D labeled data and voxel representations essential for training. To address these limitations, our study presents a calibration and reconstruction method using an unaligned sparse multi-view X-ray baggage dataset, which has extensive 2D labeling. Our approach integrates multi-spectral neural attenuation field reconstruction with Linear pushbroom (LPB) camera model pose optimization, enhancing rendering consistency for novel views through color coding network. Our method aims to improve generalization within the security baggage inspection domain, where generalization is particularly challenging.

Related papers

• DiffuX2CT: Diffusion Learning to Reconstruct CT Images from Biplanar X-Rays [41.393567374399524]
  We propose DiffuX2CT, which models CT reconstruction from ultra-sparse X-rays as a conditional diffusion process. By doing so, DiffuX2CT achieves structure-controllable reconstruction, which enables 3D structural information to be recovered from 2D X-rays. As an extra contribution, we collect a real-world lumbar CT dataset, called LumbarV, as a new benchmark to verify the clinical significance and performance of CT reconstruction from X-rays.
  arXiv  Detail & Related papers  (2024-07-18T14:20:04Z)
• Multi-view X-ray Image Synthesis with Multiple Domain Disentanglement from CT Scans [10.72672892416061]
  Over-dosed X-rays superimpose potential risks to human health to some extent. Data-driven algorithms from volume scans to X-ray images are restricted by the scarcity of paired X-ray and volume data. We propose CT2X-GAN to synthesize the X-ray images in an end-to-end manner using the content and style disentanglement from three different image domains.
  arXiv  Detail & Related papers  (2024-04-18T04:25:56Z)
• Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis [88.86777314004044]
  We propose a 3D Gaussian splatting-based framework, namely X-Gaussian, for X-ray novel view visualization. Experiments show that our X-Gaussian outperforms state-of-the-art methods by 6.5 dB while enjoying less than 15% training time and over 73x inference speed.
  arXiv  Detail & Related papers  (2024-03-07T00:12:08Z)
• XTransCT: Ultra-Fast Volumetric CT Reconstruction using Two Orthogonal X-Ray Projections for Image-guided Radiation Therapy via a Transformer Network [8.966238080182263]
  We introduce a novel Transformer architecture, termed XTransCT, to facilitate real-time reconstruction of CT images from two-dimensional X-ray images. Our findings indicate that our algorithm surpasses other methods in image quality, structural precision, and generalizability. In comparison to previous 3D convolution-based approaches, we note a substantial speed increase of approximately 300 %, achieving 44 ms per 3D image reconstruction.
  arXiv  Detail & Related papers  (2023-05-31T07:41:10Z)
• Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction [53.93674177236367]
  Cone Beam Computed Tomography (CBCT) plays a vital role in clinical imaging. Traditional methods typically require hundreds of 2D X-ray projections to reconstruct a high-quality 3D CBCT image. This has led to a growing interest in sparse-view CBCT reconstruction to reduce radiation doses. We introduce a novel geometry-aware encoder-decoder framework to solve this problem.
  arXiv  Detail & Related papers  (2023-03-26T14:38:42Z)
• Perspective Projection-Based 3D CT Reconstruction from Biplanar X-rays [32.98966469644061]
  We propose PerX2CT, a novel CT reconstruction framework from X-ray. Our proposed method provides a different combination of features for each coordinate which implicitly allows the model to obtain information about the 3D location.
  arXiv  Detail & Related papers  (2023-03-09T14:45:25Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• Cross-Modal Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop [63.81818077092879]
  We propose an end-to-end semi-supervised cross-modal contrastive learning framework for medical images. We first apply an image encoder to classify the chest X-rays and to generate the image features. The radiomic features are then passed through another dedicated encoder to act as the positive sample for the image features generated from the same chest X-ray.
  arXiv  Detail & Related papers  (2021-04-11T09:16:29Z)
• XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors [118.27130593216096]
  A radiograph visualizes the internal anatomy of a patient through the use of X-ray, which projects 3D information onto a 2D plane. To the best of our knowledge, this is the first work on radiograph view synthesis. We show that by gaining an understanding of radiography in 3D space, our method can be applied to radiograph bone extraction and suppression without groundtruth bone labels.
  arXiv  Detail & Related papers  (2020-12-04T05:08:53Z)
• Learning Invariant Feature Representation to Improve Generalization across Chest X-ray Datasets [55.06983249986729]
  We show that a deep learning model performing well when tested on the same dataset as training data starts to perform poorly when it is tested on a dataset from a different source. By employing an adversarial training strategy, we show that a network can be forced to learn a source-invariant representation.
  arXiv  Detail & Related papers  (2020-08-04T07:41:15Z)
• End-To-End Convolutional Neural Network for 3D Reconstruction of Knee Bones From Bi-Planar X-Ray Images [6.645111950779666]
  We present an end-to-end Convolutional Neural Network (CNN) approach for 3D reconstruction of knee bones directly from two bi-planar X-ray images.
  arXiv  Detail & Related papers  (2020-04-02T08:37:11Z)

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.