Benchmarking learned algorithms for computed tomography image reconstruction tasks

• URL: http://arxiv.org/abs/2412.08350v1
• Date: Wed, 11 Dec 2024 12:45:17 GMT
• Title: Benchmarking learned algorithms for computed tomography image reconstruction tasks
• Authors: Maximilian B. Kiss, Ander Biguri, Zakhar Shumaylov, Ferdia Sherry, K. Joost Batenburg, Carola-Bibiane Schönlieb, Felix Lucka,
• Abstract summary: We use the 2DeteCT dataset, a real-world experimental computed tomography dataset, for benchmarking machine learning based CT image reconstruction algorithms. We categorize these methods into post-processing networks, learned/unrolled iterative methods, learned regularizer methods, and plug-and-play methods. Our benchmarking results showcase the effectiveness of various algorithms on tasks such as full data reconstruction, limited-angle reconstruction, sparse-angle reconstruction, low-dose reconstruction, and beam-hardening corrected reconstruction.
• Score: 8.850508171390194
• License:
• Abstract: Computed tomography (CT) is a widely used non-invasive diagnostic method in various fields, and recent advances in deep learning have led to significant progress in CT image reconstruction. However, the lack of large-scale, open-access datasets has hindered the comparison of different types of learned methods. To address this gap, we use the 2DeteCT dataset, a real-world experimental computed tomography dataset, for benchmarking machine learning based CT image reconstruction algorithms. We categorize these methods into post-processing networks, learned/unrolled iterative methods, learned regularizer methods, and plug-and-play methods, and provide a pipeline for easy implementation and evaluation. Using key performance metrics, including SSIM and PSNR, our benchmarking results showcase the effectiveness of various algorithms on tasks such as full data reconstruction, limited-angle reconstruction, sparse-angle reconstruction, low-dose reconstruction, and beam-hardening corrected reconstruction. With this benchmarking study, we provide an evaluation of a range of algorithms representative for different categories of learned reconstruction methods on a recently published dataset of real-world experimental CT measurements. The reproducible setup of methods and CT image reconstruction tasks in an open-source toolbox enables straightforward addition and comparison of new methods later on. The toolbox also provides the option to load the 2DeteCT dataset differently for extensions to other problems and different CT reconstruction tasks.

Related papers

• 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)
• Enhancing Low-dose CT Image Reconstruction by Integrating Supervised and Unsupervised Learning [13.17680480211064]
  We propose a hybrid supervised-unsupervised learning framework for X-ray computed tomography (CT) image reconstruction. Each proposed trained block consists of a deterministic MBIR solver and a neural network. We demonstrate the efficacy of this learned hybrid model for low-dose CT image reconstruction with limited training data.
  arXiv  Detail & Related papers  (2023-11-19T20:23:59Z)
• Deep Unfolding of the DBFB Algorithm with Application to ROI CT Imaging with Limited Angular Density [15.143939192429018]
  This paper presents a new method for reconstructing regions of interest (ROI) from a limited number of computed (CT) measurements. Deep methods are fast, and they can reach high reconstruction quality by leveraging information from datasets. We introduce an unfolding neural network called UDBFB designed for ROI reconstruction from limited data.
  arXiv  Detail & Related papers  (2022-09-27T09:10:57Z)
• Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction [33.193423488300255]
  This paper proposes a unsupervised deep learning method for LDCT image reconstruction. The proposed method is built on a re-parametrization technique for Bayesian inference via deep network with random weights, combined with additional total variational (TV) regularization. Experiments show that the proposed method noticeably outperforms existing dataset-free image reconstruction methods on the test data.
  arXiv  Detail & Related papers  (2022-05-01T13:05:04Z)
• A comparison of different atmospheric turbulence simulation methods for image restoration [64.24948495708337]
  Atmospheric turbulence deteriorates the quality of images captured by long-range imaging systems. Various deep learning-based atmospheric turbulence mitigation methods have been proposed in the literature. We systematically evaluate the effectiveness of various turbulence simulation methods on image restoration.
  arXiv  Detail & Related papers  (2022-04-19T16:21:36Z)
• 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)
• Reference-based Magnetic Resonance Image Reconstruction Using Texture Transforme [86.6394254676369]
  We propose a novel Texture Transformer Module (TTM) for accelerated MRI reconstruction. We formulate the under-sampled data and reference data as queries and keys in a transformer. The proposed TTM can be stacked on prior MRI reconstruction approaches to further improve their performance.
  arXiv  Detail & Related papers  (2021-11-18T03:06:25Z)
• Shared Prior Learning of Energy-Based Models for Image Reconstruction [69.72364451042922]
  We propose a novel learning-based framework for image reconstruction particularly designed for training without ground truth data. In the absence of ground truth data, we change the loss functional to a patch-based Wasserstein functional. In shared prior learning, both aforementioned optimal control problems are optimized simultaneously with shared learned parameters of the regularizer.
  arXiv  Detail & Related papers  (2020-11-12T17:56:05Z)
• Self-Supervised Training For Low Dose CT Reconstruction [0.0]
  This study defines a training scheme to use low-dose sinograms as their own training targets. We apply the self-supervision principle in the projection domain where the noise is element-wise independent. We demonstrate that our method outperforms both conventional and compressed sensing based iterative reconstruction methods.
  arXiv  Detail & Related papers  (2020-10-25T22:02:14Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Data Consistent CT Reconstruction from Insufficient Data with Learned Prior Images [70.13735569016752]
  We investigate the robustness of deep learning in CT image reconstruction by showing false negative and false positive lesion cases. We propose a data consistent reconstruction (DCR) method to improve their image quality, which combines the advantages of compressed sensing and deep learning. The efficacy of the proposed method is demonstrated in cone-beam CT with truncated data, limited-angle data and sparse-view data, respectively.
  arXiv  Detail & Related papers  (2020-05-20T13:30:49Z)

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.