Accelerated Optimization of Implicit Neural Representations for CT Reconstruction

• URL: http://arxiv.org/abs/2504.13390v1
• Date: Fri, 18 Apr 2025 00:52:56 GMT
• Title: Accelerated Optimization of Implicit Neural Representations for CT Reconstruction
• Authors: Mahrokh Najaf, Gregory Ongie,
• Abstract summary: implicit neural representations (INRs) have been recently proposed for reconstruction in low-dose/sparse-view X-ray computed tomography (CT)<n>An INR represents a CT image as a small-scale neural network that takes spatial coordinates as inputs and outputs attenuation values.<n>This paper investigates strategies to accelerate the optimization of INRs for CT reconstruction.
• Score: 0.3222802562733786
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inspired by their success in solving challenging inverse problems in computer vision, implicit neural representations (INRs) have been recently proposed for reconstruction in low-dose/sparse-view X-ray computed tomography (CT). An INR represents a CT image as a small-scale neural network that takes spatial coordinates as inputs and outputs attenuation values. Fitting an INR to sinogram data is similar to classical model-based iterative reconstruction methods. However, training INRs with losses and gradient-based algorithms can be prohibitively slow, taking many thousands of iterations to converge. This paper investigates strategies to accelerate the optimization of INRs for CT reconstruction. In particular, we propose two approaches: (1) using a modified loss function with improved conditioning, and (2) an algorithm based on the alternating direction method of multipliers. We illustrate that both of these approaches significantly accelerate INR-based reconstruction of a synthetic breast CT phantom in a sparse-view setting.

Related papers

• 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)
• Deep Guess acceleration for explainable image reconstruction in sparse-view CT [0.0]
  Sparse-view Computed (CT) is an emerging protocol designed to reduce X-ray dose radiation in medical imaging.<n>Traditional Filtered Back Projection reconstructions suffer from severe artifacts due to sparse data.<n>In contrast, Model-Based Iterative Reconstruction (MBIR) are too computationally costly for clinical use.
  arXiv  Detail & Related papers  (2024-12-02T16:49:42Z)
• AC-IND: Sparse CT reconstruction based on attenuation coefficient estimation and implicit neural distribution [12.503822675024054]
  Computed tomography (CT) reconstruction plays a crucial role in industrial nondestructive testing and medical diagnosis. Sparse view CT reconstruction aims to reconstruct high-quality CT images while only using a small number of projections. We introduce AC-IND, a self-supervised method based on Attenuation Coefficient Estimation and Implicit Neural Distribution.
  arXiv  Detail & Related papers  (2024-09-11T10:34:41Z)
• A Low-dose CT Reconstruction Network Based on TV-regularized OSEM Algorithm [10.204918070701211]
  Low-dose computed tomography (LDCT) offers significant advantages in reducing the potential harm to human bodies. By utilizing the expectation (EM) algorithm, statistical priors could be combined with artificial priors to improve LDCT reconstruction quality. In this paper, we propose to integrate TV regularization into the M''-step of the EM algorithm, thus achieving effective and efficient regularization.
  arXiv  Detail & Related papers  (2024-08-25T13:31:53Z)
• Enhanced Low-Dose CT Image Reconstruction by Domain and Task Shifting Gaussian Denoisers [3.4748713192043876]
  Computed tomography from a low radiation dose (LDCT) is challenging due to high noise in the projection data. We propose a method combining the simplicity and efficiency of two-stage methods with state-of-the-art reconstruction quality.
  arXiv  Detail & Related papers  (2024-03-06T08:51:09Z)
• 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)
• Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomography [31.774432128324385]
  We propose a Self-supervised COordinate Projection nEtwork (SCOPE) to reconstruct the artifacts-free CT image from a single SV sinogram. Compared with recent related works that solve similar problems using implicit neural representation network (INR), our essential contribution is an effective and simple re-projection strategy.
  arXiv  Detail & Related papers  (2022-09-12T06:14:04Z)
• Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction [108.06731611196291]
  We develop a multi-channel convolutional analysis operator learning (MCAOL) method to exploit common spatial features within attenuation images at different energies. We propose an optimization method which jointly reconstructs the attenuation images at low and high energies with a mixed norm regularization on the sparse features.
  arXiv  Detail & Related papers  (2022-03-10T14:22:54Z)
• Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover [87.28082715343896]
  We consider deep neural networks for solving inverse problems that are robust to forward model mis-specifications. We design a new robust deep neural network architecture by applying algorithm unfolding techniques to a robust version of the underlying recovery problem. The proposed REST network is shown to outperform state-of-the-art model-based and data-driven algorithms in both compressive sensing and radar imaging problems.
  arXiv  Detail & Related papers  (2021-10-20T06:15:45Z)
• Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation [60.663499381212425]
  We enhance the image quality by using a Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.
  arXiv  Detail & Related papers  (2021-04-05T13:05:22Z)
• 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)

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.