DYRECT Computed Tomography: DYnamic Reconstruction of Events on a Continuous Timescale

• URL: http://arxiv.org/abs/2412.00065v1
• Date: Fri, 15 Nov 2024 14:21:46 GMT
• Title: DYRECT Computed Tomography: DYnamic Reconstruction of Events on a Continuous Timescale
• Authors: Wannes Goethals, Tom Bultreys, Steffen Berg, Matthieu N. Boone, Jan Aelterman,
• Abstract summary: Time-resolved high-resolution X-ray Computed Tomography (4D $mu$CT) is an imaging technique that offers insight into the evolution of dynamic processes inside materials that are opaque to visible light. Conventional tomographic reconstruction techniques are based on recording a sequence of 3D images that represent the sample state at different moments in time. This frame-based approach limits the temporal resolution compared to dynamic radiography experiments due to the time needed to make CT scans. Our proposed 4D $mu$CT reconstruction technique, named DYRECT, estimates individual attenuation evolution profiles for each position in the sample.
• Score: 0.19249183980865092
• License:
• Abstract: Time-resolved high-resolution X-ray Computed Tomography (4D $\mu$CT) is an imaging technique that offers insight into the evolution of dynamic processes inside materials that are opaque to visible light. Conventional tomographic reconstruction techniques are based on recording a sequence of 3D images that represent the sample state at different moments in time. This frame-based approach limits the temporal resolution compared to dynamic radiography experiments due to the time needed to make CT scans. Moreover, it leads to an inflation of the amount of data and thus to costly post-processing computations to quantify the dynamic behaviour from the sequence of time frames, hereby often ignoring the temporal correlations of the sample structure. Our proposed 4D $\mu$CT reconstruction technique, named DYRECT, estimates individual attenuation evolution profiles for each position in the sample. This leads to a novel memory-efficient event-based representation of the sample, using as little as three image volumes: its initial attenuation, its final attenuation and the transition times. This third volume represents local events on a continuous timescale instead of the discrete global time frames. We propose a method to iteratively reconstruct the transition times and the attenuation volumes. The dynamic reconstruction technique was validated on synthetic ground truth data and experimental data, and was found to effectively pinpoint the transition times in the synthetic dataset with a time resolution corresponding to less than a tenth of the amount of projections required to reconstruct traditional $\mu$CT time frames.

Related papers

• 4DRGS: 4D Radiative Gaussian Splatting for Efficient 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images [49.170407434313475]
  Existing methods often produce suboptimal results or require excessive computation time. We propose 4D radiative Gaussian splatting (4DRGS) to achieve high-quality reconstruction efficiently. 4DRGS achieves impressive results in 5 minutes training, which is 32x faster than the state-of-the-art method.
  arXiv  Detail & Related papers  (2024-12-17T13:51:56Z)
• TEAM PILOT -- Learned Feasible Extendable Set of Dynamic MRI Acquisition Trajectories [2.7719338074999547]
  We introduce a novel deep-compressed sensing approach that uses 3D window attention and flexible, temporally extendable acquisition trajectories. Our method significantly reduces both training and inference times compared to existing approaches. Tests with real data show that our approach outperforms current state-of-theart techniques.
  arXiv  Detail & Related papers  (2024-09-19T13:45:13Z)
• 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)
• 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images via Vessel Probability Guided Attenuation Learning [79.60829508459753]
  Current commercial Digital Subtraction Angiography (DSA) systems typically demand hundreds of scanning views to perform reconstruction. The dynamic blood flow and insufficient input of sparse-view DSA images present significant challenges to the 3D vessel reconstruction task. We propose to use a time-agnostic vessel probability field to solve this problem effectively.
  arXiv  Detail & Related papers  (2024-05-17T11:23:33Z)
• Distributed Stochastic Optimization of a Neural Representation Network for Time-Space Tomography Reconstruction [4.689071714940848]
  4D time-space reconstruction of dynamic events or deforming objects using Xray computed tomography (CT) is an extremely ill-posed inverse problem. Existing approaches assume that the object remains static for the duration of several tens or hundreds of X-ray projection measurement images. We propose to perform a 4D time-space reconstruction using a distributed implicit neural representation network that is trained using a novel distributed training algorithm.
  arXiv  Detail & Related papers  (2024-04-29T19:41:51Z)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• 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)
• Neural Computed Tomography [1.7188280334580197]
  Motion during acquisition of a set of projections can lead to significant motion artifacts in computed tomography reconstructions. We propose a novel reconstruction framework, NeuralCT, to generate time-resolved images free from motion artifacts.
  arXiv  Detail & Related papers  (2022-01-17T18:50:58Z)
• Multi-Temporal Convolutions for Human Action Recognition in Videos [83.43682368129072]
  We present a novel temporal-temporal convolution block that is capable of extracting at multiple resolutions. The proposed blocks are lightweight and can be integrated into any 3D-CNN architecture.
  arXiv  Detail & Related papers  (2020-11-08T10:40:26Z)
• 4D Spatio-Temporal Convolutional Networks for Object Position Estimation in OCT Volumes [69.62333053044712]
  3D convolutional neural networks (CNNs) have shown promising performance for pose estimation of a marker object using single OCT images. We extend 3D CNNs to 4D-temporal CNNs to evaluate the impact of additional temporal information for marker object tracking.
  arXiv  Detail & Related papers  (2020-07-02T12:02:20Z)
• Real-time sparse-sampled Ptychographic imaging through deep neural networks [3.3351024234383946]
  A ptychography reconstruction is achieved by means of solving a complex inverse problem that imposes constraints both on the acquisition and on the analysis of the data. We propose PtychoNN, a novel approach to solve the ptychography reconstruction problem based on deep convolutional neural networks.
  arXiv  Detail & Related papers  (2020-04-15T23:43:17Z)

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.