05 Fakultät Informatik, Elektrotechnik und Informationstechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6

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Author: search.filters.author.Alsaffar, AmmarAuthor: search.filters.author.Simon, SvenSubject: search.filters.subject.004

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    Compensating streak artifacts in sparse-view inline industrial CT for accurate metrology using self-supervised optimization of implicit neural volume representations
    (2025) Ahmad, Faizan; Yang, Guangpu; Buchfink, Manuel; Alsaffar, Ammar; Baraka, Ahmed; Liu, Xingyu; Simon, Sven
    Sparse-view computed tomography (CT) can reduce acquisition times, supporting inline industrial inspection in suitable settings. In practice, scan time may also be shortened by lowering exposure per view, using faster detectors or motion systems, or leveraging partial/parallel acquisition; here we focus on reducing the number of projections. Fewer projections, however, can introduce streak artifacts that cause measurement deviations during metrological evaluations. This paper presents two self-supervised deep learning approaches using implicit neural representations (INR) to mitigate sparse-view artifacts and enhance measurement accuracy. Both methods represent the 3D object volume using a multi-layer perceptron (MLP) optimized individually for each scan through an incremental forward-backward strategy. The first approach, Neural Representation with Sparse-View Volume-based Loss (NR-SVOL), employs volume-domain training using an initial filtered back-projection (FBP) volume, enabling rapid artifact reduction with limited computational overhead. The second, Neural Representation with Sparse-View Projection-based Loss (NR-SPRO), directly optimizes the INR to match measured sparse projections, analogous to Neural Radiance Fields (NeRF), yielding superior artifact compensation at the expense of increased computation. Comprehensive evaluations were conducted on three industrial objects, a gear, a cylinder head, and a connector, at varying sparse-view configurations (32-256 projections). Both NR-SVOL and NR-SPRO demonstrated substantial artifact reduction, decreasing surface deviations by up to an order of magnitude in standard deviation. NR-SVOL achieved results within approximately five minutes, suggesting compatibility with some inline cycle times for our tested parts, while NR-SPRO delivered even higher accuracy when allowed more computation. This study highlights a practical trade-off between speed and precision, showcasing the potential of these methods for sparse-view inline industrial CT for improved metrological quality.
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    Multi-material blind beam hardening correction in near real-time based on non-linearity adjustment of projections
    (2023) Alsaffar, Ammar; Sun, Kaicong; Simon, Sven
    Beam hardening (BH) is one of the major artifacts that severely reduces the quality of computed tomography (CT) imaging. This BH artifact arises due to the polychromatic nature of the X-ray source and causes cupping and streak artifacts. This work aims to propose a fast and accurate BH correction method that requires no prior knowledge of the materials and corrects first and higher-order BH artifacts. This is achieved by performing a wide sweep of the material based on an experimentally measured look-up table to obtain the closest estimate of the material. Then, the non-linearity effect of the BH is corrected by adding the difference between the estimated monochromatic and the polychromatic simulated projections of the segmented image. The estimated polychromatic projection is accurately derived using the least square estimation (LSE) method by minimizing the difference between the experimental projection and the linear combination of simulated polychromatic projections. As a result, an accurate non-linearity correction term is derived that leads to an accurate BH correction result. The simulated projections in this work are performed using a multi-GPU-accelerated forward projection model which ensures a fast BH correction in near real-time. To evaluate the proposed BH correction method, we have conducted extensive experiments on real-world CT data. It is shown that the proposed method results in images with improved contrast-to-noise ratio (CNR) in comparison to the images corrected from only the scatter artifacts and the BH-corrected images using the state-of-the-art empirical BH correction method.
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    Computational scatter correction in near real-time with a fast Monte Carlo photon transport model for high-resolution flat-panel CT
    (2022) Alsaffar, Ammar; Kieß, Steffen; Sun, Kaicong; Simon, Sven
    In computed tomography (CT), scattering causes server quality degradation of the reconstructed CT images by introducing streaks and cupping artifacts which reduce the detectability of low contrast objects. Monte Carlo (MC) simulation is considered the most accurate approach for scatter estimation. However, the existing MC estimators are computationally expensive, especially for high-resolution flat-panel CT. In this paper, we propose a fast and accurate MC photon transport model which describes the physics within the 1 keV to 1 MeV range using multiple controllable key parameters. Based on this model, scatter computation for a single projection can be completed within a range of a few seconds under well-defined model parameters. Smoothing and interpolation are performed on the estimated scatter to accelerate the scatter calculation without compromising accuracy too much compared to measured near scatter-free projection images. Combining the fast scatter estimation with the filtered backprojection (FBP), scatter correction is performed effectively in an iterative manner. To evaluate the proposed MC model, we have conducted extensive experiments on the simulated data and real-world high-resolution flat-panel CT. Compared to the state-of-the-art MC simulators, the proposed MC model achieved a 15 ×acceleration on a single-GPU compared to the GPU implementation of the Penelope simulator (MCGPU) utilizing several acceleration techniques, and a 202 ×speed-up on a multi-GPU system compared to the multi-threaded state-of-the-art EGSnrc MC simulator. Furthermore, it is shown that for high-resolution images, scatter correction with sufficient accuracy is accomplished within one to three iterations using a FBP and the proposed fast MC photon transport model.