A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography

• URL: http://arxiv.org/abs/2501.13961v1
• Date: Tue, 21 Jan 2025 19:34:01 GMT
• Title: A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography
• Abstract summary: Cone-beam X-ray Computed Tomography (XCT) with large detectors and corresponding large-scale 3D reconstruction plays a pivotal role in micron-scale characterization of materials and parts across various industries.<n>We present a novel deep neural network-based iterative algorithm that integrates an artifact reduction-trained CNN as a prior model with automated regularization parameter selection.
• Score: 5.104810959579395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cone-beam X-ray Computed Tomography (XCT) with large detectors and corresponding large-scale 3D reconstruction plays a pivotal role in micron-scale characterization of materials and parts across various industries. In this work, we present a novel deep neural network-based iterative algorithm that integrates an artifact reduction-trained CNN as a prior model with automated regularization parameter selection, tailored for large-scale industrial cone-beam XCT data. Our method achieves high-quality 3D reconstructions even for extremely dense thick metal parts - which traditionally pose challenges to industrial CT images - in just a few iterations. Furthermore, we show the generalizability of our approach to out-of-distribution scans obtained under diverse scanning conditions. Our method effectively handles significant noise and streak artifacts, surpassing state-of-the-art supervised learning methods trained on the same data.

Related papers

• Three-dimensional visualization of X-ray micro-CT with large-scale datasets: Efficiency and accuracy for real-time interaction [10.568087673951531]
  This article provides a unique perspective on recent advances in accurate and efficient 3D visualization using Micro-CT.<n>By comparing the principles of computed tomography with advancements in microstructural technology, this article examines the evolution of CT reconstruction algorithms.<n>It explores advanced lighting models for high-accuracy, photorealistic, and efficient volume rendering.
  arXiv  Detail & Related papers  (2026-01-21T15:37:38Z)
• Resolution-Independent Neural Operators for Multi-Rate Sparse-View CT [67.14700058302016]
  Deep learning methods achieve high-fidelity reconstructions but often overfit to a fixed acquisition setup.<n>We propose Computed Tomography neural Operator (CTO), a unified CT reconstruction framework that extends to continuous function space.<n>CTO enables consistent multi-sampling-rate and cross-resolution performance, with on average >4dB PSNR gain over CNNs.
  arXiv  Detail & Related papers  (2025-12-13T08:31:46Z)
• Accelerating 3D Photoacoustic Computed Tomography with End-to-End Physics-Aware Neural Operators [74.65171736966131]
  Photoacoustic computed tomography (PACT) combines optical contrast with ultrasonic resolution, achieving deep-tissue imaging beyond the optical diffusion limit.<n>Current implementations require dense transducer arrays and prolonged acquisition times, limiting clinical translation.<n>We introduce Pano, an end-to-end physics-aware model that directly learns the inverse acoustic mapping from sensor measurements to volumetric reconstructions.
  arXiv  Detail & Related papers  (2025-09-11T23:12:55Z)
• Plug-and-Play with 2.5D Artifact Reduction Prior for Fast and Accurate Industrial Computed Tomography Reconstruction [4.024850952459758]
  Cone-beam X-ray computed tomography (XCT) is an essential imaging technique for generating 3D reconstructions of internal structures.<n>Recent work incorporating artifact reduction priors within a plug-and-play tomography reconstruction framework has shown promising results.<n>We show that this 2.5D prior not only improves the quality of reconstructions but also enables the model to directly suppress commonly occurring XCT artifacts.
  arXiv  Detail & Related papers  (2025-06-17T16:52:57Z)
• X-GRM: Large Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomography [89.84588038174721]
  Computed Tomography serves as an indispensable tool in clinical, providing non-invasive visualization of internal anatomical structures.<n>Existing CT reconstruction works are limited to small-capacity model architecture and inflexible volume representation.<n>We present X-GRM, a large feedforward model for reconstructing 3D CT volumes from sparse-view 2D X-ray projections.
  arXiv  Detail & Related papers  (2025-05-21T08:14:10Z)
• A Learnt Half-Quadratic Splitting-Based Algorithm for Fast and High-Quality Industrial Cone-beam CT Reconstruction [5.104810959579396]
  Industrial X-ray cone-beam CT (XCT) scanners are widely used for scientific imaging and non-destructive characterization.<n>In order to obtain high-quality reconstruction when using typical analytic algorithms, the scan involves collecting a large number of projections/views.<n>We propose a half-quadratic splitting-based algorithm that uses convolutional neural networks (CNN) in order to obtain high-quality reconstructions from large sparse-view cone-beam CT (CBCT) measurements.
  arXiv  Detail & Related papers  (2025-01-21T19:15:34Z)
• Unsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts in 3D X-Ray CBCT [51.95884144860506]
  Ring artifacts are prevalent in 3D cone-beam computed tomography (CBCT)<n>Existing state-of-the-art (SOTA) ring artifact reduction (RAR) methods rely on supervised learning with large-scale paired CT datasets.<n>In this work, we propose Riner, a new unsupervised RAR method.
  arXiv  Detail & Related papers  (2024-12-08T08:22:58Z)
• Fan-Beam CT Reconstruction for Unaligned Sparse-View X-ray Baggage Dataset [0.0]
  We present a calibration and reconstruction method using an unaligned sparse multi-view X-ray baggage dataset.<n>Our approach integrates multi-spectral neural attenuation field reconstruction with Linear pushbroom (LPB) camera model pose optimization.
  arXiv  Detail & Related papers  (2024-12-04T05:16:54Z)
• TomoGRAF: A Robust and Generalizable Reconstruction Network for Single-View Computed Tomography [3.1209855614927275]
  Traditional analytical/iterative CT reconstruction algorithms require hundreds of angular data samplings. We develop a novel TomoGRAF framework incorporating the unique X-ray transportation physics to reconstruct high-quality 3D volumes.
  arXiv  Detail & Related papers  (2024-11-12T20:07:59Z)
• 3D-CT-GPT: Generating 3D Radiology Reports through Integration of Large Vision-Language Models [51.855377054763345]
  This paper introduces 3D-CT-GPT, a Visual Question Answering (VQA)-based medical visual language model for generating radiology reports from 3D CT scans. Experiments on both public and private datasets demonstrate that 3D-CT-GPT significantly outperforms existing methods in terms of report accuracy and quality.
  arXiv  Detail & Related papers  (2024-09-28T12:31:07Z)
• R$^2$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction [53.19869886963333]
  3D Gaussian splatting (3DGS) has shown promising results in rendering image and surface reconstruction. This paper introduces R2$-Gaussian, the first 3DGS-based framework for sparse-view tomographic reconstruction.
  arXiv  Detail & Related papers  (2024-05-31T08:39:02Z)
• Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training [51.16994853817024]
  This work focuses on designing an effective pre-training framework for 3D radiology images. We introduce Disruptive Autoencoders, a pre-training framework that attempts to reconstruct the original image from disruptions created by a combination of local masking and low-level perturbations. The proposed pre-training framework is tested across multiple downstream tasks and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-07-31T17:59:42Z)
• 2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning [1.0266286487433585]
  We provide a versatile, open 2D fan-beam CT dataset suitable for developing machine learning techniques. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline.
  arXiv  Detail & Related papers  (2023-06-09T14:02:53Z)
• Orientation-Shared Convolution Representation for CT Metal Artifact Learning [63.67718355820655]
  During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts. Existing deep-learning-based methods have gained promising reconstruction performance. We propose an orientation-shared convolution representation strategy to adapt the physical prior structures of artifacts.
  arXiv  Detail & Related papers  (2022-12-26T13:56:12Z)
• 3D helical CT Reconstruction with a Memory Efficient Learned Primal-Dual Architecture [1.3518297878940662]
  This paper modifies a domain adapted neural network architecture, the Learned Primal-Dual (LPD), so it can be trained and applied to reconstruction in this setting. It is the first to apply an unrolled deep learning architecture for reconstruction on full-sized clinical data.
  arXiv  Detail & Related papers  (2022-05-24T10:32:32Z)
• Ultrasound Signal Processing: From Models to Deep Learning [64.56774869055826]
  Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Deep learning based methods, which are optimized in a data-driven fashion, have gained popularity. A relatively new paradigm combines the power of the two: leveraging data-driven deep learning, as well as exploiting domain knowledge.
  arXiv  Detail & Related papers  (2022-04-09T13:04:36Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)
• Fed-Sim: Federated Simulation for Medical Imaging [131.56325440976207]
  We introduce a physics-driven generative approach that consists of two learnable neural modules. We show that our data synthesis framework improves the downstream segmentation performance on several datasets.
  arXiv  Detail & Related papers  (2020-09-01T19:17:46Z)

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.