Distributed Stochastic Optimization of a Neural Representation Network for Time-Space Tomography Reconstruction

• URL: http://arxiv.org/abs/2404.19075v1
• Date: Mon, 29 Apr 2024 19:41:51 GMT
• Title: Distributed Stochastic Optimization of a Neural Representation Network for Time-Space Tomography Reconstruction
• Authors: K. Aditya Mohan, Massimiliano Ferrucci, Chuck Divin, Garrett A. Stevenson, Hyojin Kim,
• Abstract summary: 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.
• Score: 4.689071714940848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 4D time-space reconstruction of dynamic events or deforming objects using X-ray 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 (reconstruction of consecutive limited-angle CT scans). However, this is an unrealistic assumption for many in-situ experiments that causes spurious artifacts and inaccurate morphological reconstructions of the object. To solve this problem, we propose to perform a 4D time-space reconstruction using a distributed implicit neural representation (DINR) network that is trained using a novel distributed stochastic training algorithm. Our DINR network learns to reconstruct the object at its output by iterative optimization of its network parameters such that the measured projection images best match the output of the CT forward measurement model. We use a continuous time and space forward measurement model that is a function of the DINR outputs at a sparsely sampled set of continuous valued object coordinates. Unlike existing state-of-the-art neural representation architectures that forward and back propagate through dense voxel grids that sample the object's entire time-space coordinates, we only propagate through the DINR at a small subset of object coordinates in each iteration resulting in an order-of-magnitude reduction in memory and compute for training. DINR leverages distributed computation across several compute nodes and GPUs to produce high-fidelity 4D time-space reconstructions even for extremely large CT data sizes. We use both simulated parallel-beam and experimental cone-beam X-ray CT datasets to demonstrate the superior performance of our approach.

Related papers

• 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)
• Motion2VecSets: 4D Latent Vector Set Diffusion for Non-rigid Shape Reconstruction and Tracking [52.393359791978035]
  Motion2VecSets is a 4D diffusion model for dynamic surface reconstruction from point cloud sequences. We parameterize 4D dynamics with latent sets instead of using global latent codes. For more temporally-coherent object tracking, we synchronously denoise deformation latent sets and exchange information across multiple frames.
  arXiv  Detail & Related papers  (2024-01-12T15:05:08Z)
• IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI [11.159664312706704]
  IMJENSE is a scan-specific implicit neural representation-based method for improving parallel MRI reconstruction. Benefiting from the powerful continuous representation and joint estimation of the MRI image and coil sensitivities, IMJENSE outperforms conventional image or k-space domain reconstruction algorithms.
  arXiv  Detail & Related papers  (2023-11-21T07:24:11Z)
• 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)
• A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging [5.489791364472879]
  We propose a new model-data-driven network to achieve tomoSAR imaging based on multi-dimensional features. We add two 2D processing modules, both convolutional encoder-decoder structures, to enhance multi-dimensional features of the imaging scene effectively. Compared with the conventional CS-based FISTA method and DL-based gamma-Net method, the result of our proposed method has better performance on completeness while having decent imaging accuracy.
  arXiv  Detail & Related papers  (2022-11-28T02:01:43Z)
• 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)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)
• Category Level Object Pose Estimation via Neural Analysis-by-Synthesis [64.14028598360741]
  In this paper we combine a gradient-based fitting procedure with a parametric neural image synthesis module. The image synthesis network is designed to efficiently span the pose configuration space. We experimentally show that the method can recover orientation of objects with high accuracy from 2D images alone.
  arXiv  Detail & Related papers  (2020-08-18T20:30:47Z)
• Multi-Slice Fusion for Sparse-View and Limited-Angle 4D CT Reconstruction [3.045887205265198]
  We present multi-slice fusion, a novel algorithm for 4D reconstruction based on the fusion of multiple low-dimensional denoisers. We implement multi-slice fusion on distributed, heterogeneous clusters in order to reconstruct large 4D volumes in reasonable time.
  arXiv  Detail & Related papers  (2020-08-01T02:32:43Z)
• 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)

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.