Enhancing Dynamic CT Image Reconstruction with Neural Fields Through Explicit Motion Regularizers

• URL: http://arxiv.org/abs/2406.01299v1
• Date: Mon, 3 Jun 2024 13:07:29 GMT
• Title: Enhancing Dynamic CT Image Reconstruction with Neural Fields Through Explicit Motion Regularizers
• Authors: Pablo Arratia, Matthias Ehrhardt, Lisa Kreusser,
• Abstract summary: We show the benefits of introducing explicit PDE-based motion regularizers in 2D+time computed tomography for the optimization of neural fields. We also compare neural fields against a grid-based solver and show that the former outperforms the latter.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image reconstruction for dynamic inverse problems with highly undersampled data poses a major challenge: not accounting for the dynamics of the process leads to a non-realistic motion with no time regularity. Variational approaches that penalize time derivatives or introduce motion model regularizers have been proposed to relate subsequent frames and improve image quality using grid-based discretization. Neural fields offer an alternative parametrization of the desired spatiotemporal quantity with a deep neural network, a lightweight, continuous, and biased towards smoothness representation. The inductive bias has been exploited to enforce time regularity for dynamic inverse problems resulting in neural fields optimized by minimizing a data-fidelity term only. In this paper we investigate and show the benefits of introducing explicit PDE-based motion regularizers, namely, the optical flow equation, in 2D+time computed tomography for the optimization of neural fields. We also compare neural fields against a grid-based solver and show that the former outperforms the latter.

Related papers

• Exploring Kernel Transformations for Implicit Neural Representations [57.2225355625268]
  Implicit neural representations (INRs) leverage neural networks to represent signals by mapping coordinates to their corresponding attributes. This work pioneers the exploration of the effect of kernel transformation of input/output while keeping the model itself unchanged. A byproduct of our findings is a simple yet effective method that combines scale and shift to significantly boost INR with negligible overhead.
  arXiv  Detail & Related papers  (2025-04-07T04:43:50Z)
• RSR-NF: Neural Field Regularization by Static Restoration Priors for Dynamic Imaging [5.569092860148177]
  In inverse problem of dynamic computed tomography (dCT), only a single projection at one view angle is available at a time. Ground-truth dynamic data is usually either unavailable or too scarce to be used for supervised learning techniques. We use an ADMM-based algorithm with variable splitting to efficiently optimize the variational objective.
  arXiv  Detail & Related papers  (2025-03-13T03:50:47Z)
• Dynamic-Aware Spatio-temporal Representation Learning for Dynamic MRI Reconstruction [7.704793488616996]
  We propose Dynamic-Aware INR (DA-INR), an INR-based model for dynamic MRI reconstruction. It captures the spatial and temporal continuity of dynamic MRI data in the image domain and explicitly incorporates the temporal redundancy of the data into the model structure. As a result, DA-INR outperforms other models in reconstruction quality even at extreme undersampling ratios.
  arXiv  Detail & Related papers  (2025-01-15T12:11:33Z)
• Deep-Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware [78.17783007774295]
  This paper explores the potential of conversion-based neuromorphic algorithms for highly accurate and energy-efficient single-snapshot multidimensional harmonic retrieval. A novel method for converting the complex-valued convolutional layers and activations into spiking neural networks (SNNs) is developed. The converted SNNs achieve almost five-fold power efficiency at moderate performance loss compared to the original CNNs.
  arXiv  Detail & Related papers  (2024-12-05T09:41:33Z)
• Trajectory Flow Matching with Applications to Clinical Time Series Modeling [77.58277281319253]
  Trajectory Flow Matching (TFM) trains a Neural SDE in a simulation-free manner, bypassing backpropagation through the dynamics. We demonstrate improved performance on three clinical time series datasets in terms of absolute performance and uncertainty prediction.
  arXiv  Detail & Related papers  (2024-10-28T15:54:50Z)
• NODER: Image Sequence Regression Based on Neural Ordinary Differential Equations [2.711538918087856]
  We propose an optimization-based new framework called NODER, which leverages neural ordinary differential equations to capture complex underlying dynamics. Our model needs only a couple of images in a sequence for prediction, which is practical, especially for clinical situations.
  arXiv  Detail & Related papers  (2024-07-18T07:50:46Z)
• Space-Variant Total Variation boosted by learning techniques in few-view tomographic imaging [0.0]
  This paper focuses on the development of a space-variant regularization model for solving an under-determined linear inverse problem. The primary objective of the proposed model is to achieve a good balance between denoising and the preservation of fine details and edges. A convolutional neural network is designed, to approximate both the ground truth image and its gradient using an elastic loss function in its training.
  arXiv  Detail & Related papers  (2024-04-25T08:58:41Z)
• EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision [85.17951804790515]
  EmerNeRF is a simple yet powerful approach for learning spatial-temporal representations of dynamic driving scenes. It simultaneously captures scene geometry, appearance, motion, and semantics via self-bootstrapping. Our method achieves state-of-the-art performance in sensor simulation.
  arXiv  Detail & Related papers  (2023-11-03T17:59:55Z)
• Neural Delay Differential Equations: System Reconstruction and Image Classification [14.59919398960571]
  We propose a new class of continuous-depth neural networks with delay, named Neural Delay Differential Equations (NDDEs) Compared to NODEs, NDDEs have a stronger capacity of nonlinear representations. We achieve lower loss and higher accuracy not only for the data produced synthetically but also for the CIFAR10, a well-known image dataset.
  arXiv  Detail & Related papers  (2023-04-11T16:09:28Z)
• Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks [25.051918587650636]
  Multitemporal hyperspectral unmixing (MTHU) is a fundamental tool in the analysis of hyperspectral image sequences. We propose an unsupervised MTHU algorithm based on variational recurrent neural networks.
  arXiv  Detail & Related papers  (2023-03-19T04:51:34Z)
• Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions [65.84090965167535]
  We present Neural Motion Fields, a novel object representation which encodes both object point clouds and the relative task trajectories as an implicit value function parameterized by a neural network. This object-centric representation models a continuous distribution over the SE(3) space and allows us to perform grasping reactively by leveraging sampling-based MPC to optimize this value function.
  arXiv  Detail & Related papers  (2022-06-29T18:47:05Z)
• Revisit Geophysical Imaging in A New View of Physics-informed Generative Adversarial Learning [2.12121796606941]
  Full waveform inversion produces high-resolution subsurface models. FWI with least-squares function suffers from many drawbacks such as the local-minima problem. Recent works relying on partial differential equations and neural networks show promising performance for two-dimensional FWI. We propose an unsupervised learning paradigm that integrates wave equation with a discriminate network to accurately estimate the physically consistent models.
  arXiv  Detail & Related papers  (2021-09-23T15:54:40Z)
• NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo [97.07453889070574]
  We present a new multi-view depth estimation method that utilizes both conventional SfM reconstruction and learning-based priors. We show that our proposed framework significantly outperforms state-of-the-art methods on indoor scenes.
  arXiv  Detail & Related papers  (2021-09-02T17:54:31Z)
• Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix [8.774604259603302]
  We learn a probabilistic motion model from simulating temporal-temporal registration in a sequence of images. We show improved registration accuracy-temporally smoother consistencys compared to three state-of-the-art registration algorithms. We also demonstrate the model's applicability for motion analysis, simulation and super-resolution by an improved motion reconstruction from sequences with missing frames.
  arXiv  Detail & Related papers  (2020-11-03T14:44:09Z)
• Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
  Blind image restoration is a common yet challenging problem in computer vision. We propose a novel blind image restoration method, aiming to integrate both the advantages of them. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
  arXiv  Detail & Related papers  (2020-08-25T03:30:53Z)
• Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning [68.9515120904028]
  Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem. Regularizing priors are necessary to reduce artifacts by improving the condition of such problems. We devised a recurrent neural network (RNN) architecture with a novel split-convolutional gated recurrent unit (SC-GRU) as the building block.
  arXiv  Detail & Related papers  (2020-07-21T11:48:22Z)
• Liquid Time-constant Networks [117.57116214802504]
  We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations.
  arXiv  Detail & Related papers  (2020-06-08T09:53:35Z)
• Time Dependence in Non-Autonomous Neural ODEs [74.78386661760662]
  We propose a novel family of Neural ODEs with time-varying weights. We outperform previous Neural ODE variants in both speed and representational capacity.
  arXiv  Detail & Related papers  (2020-05-05T01:41:46Z)

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.