Rapid simulation of X-ray transmission imaging for baggage inspection via GPU-based ray-tracing

Gong, Qian; Stoian, Razvan-Ionut; Coccarelli, David; Greenberg, Joel A.; Vera, Esteban; Gehm, Michael E.

doi:10.1016/j.nimb.2017.09.035

Cited by 16 publications

(10 citation statements)

References 24 publications

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“…A second phantom is a multi‐material resolution phantom design inspired by Gong et al 33 . The length of the phantom is 20 cm with pixel pitch around 0.1 cm.…”

Section: Estimation Algorithm and Illustrative Resultsmentioning

confidence: 99%

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Invertibility of multi‐energy X‐ray transform

2021

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The goal is to provide a sufficient condition for the invertibility of a multi-energy (ME) X-ray transform. The energy-dependent X-ray attenuation profiles can be represented by a set of coefficients using the Alvarez-Macovski (AM) method. An ME X-ray transform is a mapping from N AM coefficients to N noise-free energy-weighted measurements, where N ≥ 2. Methods: We apply a general invertibility theorem to prove the equivalence of global and local invertibility for an ME X-ray transform. We explore the global invertibility through testing whether the Jacobian of the mapping J(A) has zero values over the support of the mapping. The Jacobian of an arbitrary ME X-ray transform is an integration over all spectral measurements. A sufficient condition for J(A) ≠ 0 for all A is that the integrand of J(A) is ≥ 0 (or ≤ 0) everywhere. Note that the trivial case of the integrand equals 0 everywhere is ignored. Using symmetry, we simplified the integrand of the Jacobian to three factors that are determined by the total attenuation, the basis functions, and the energy-weighting functions, respectively. The factor related to the total attenuation is always positive; hence, the invertibility of the X-ray transform can be determined by testing the signs of the other two factors. Furthermore, we use the Cramér-Rao lower bound (CRLB) to characterize the noise-induced estimation uncertainty and provide a maximum-likelihood (ML) estimator. Results:The factor related to the basis functions is always negative when the photoelectric/Compton/Rayleigh basis functions are used and K-edge materials are not considered. The sign of the energy-weighting factor depends on the system source spectra and the detector response functions. For four special types of X-ray detectors, the sign of this factor stays the same over the integration range. Therefore, when these four types of detectors are used for imaging non-K-edge materials, the ME X-ray transform is globally invertible. The same framework can be used to study an arbitrary ME X-ray imaging system, for example, when K-edge materials are present. Furthermore, the ML estimator we presented is an unbiased, efficient estimator and can be used for a wide range of scenes. Conclusions: We have provided a framework to study the invertibility of an arbitrary ME X-ray transform and proved the global invertibility for four types of systems. K E Y W O R D Sinvertibility, multi-energy X-ray imaging, spectral X-ray imaging, X-rayThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…A second phantom is a multi‐material resolution phantom design inspired by Gong et al 33 . The length of the phantom is 20 cm with pixel pitch around 0.1 cm.…”

Section: Estimation Algorithm and Illustrative Resultsmentioning

confidence: 99%

“…A second phantom is a multi-material resolution phantom design inspired by Gong et al 33 The length of the phantom is 20 cm with pixel pitch around 0.1 cm. The phantom, as shown in Figure 8 (left), is a Delrin block The reconstruction can be further sped up using a graphic processing unit (GPU).…”

Section: Phantom Reconstructionmentioning

confidence: 99%

Invertibility of multi‐energy X‐ray transform

2021

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“…Figure 4: Four-view, dual-energy images of a soda can generated via a) a Smiths 6040 aTiX scanner, b) our homebuilt testbed scanner, and c) our virtual model of the 6040 aTiX system generated via our physics simulation tool. 14,15 The true value in using the testbed and simulation tools, however, lies in their ability to explore the full breadth of possible transmission and scatter measurements. For example, we can use the testbed to measure the energy-dependent attenuation coefficient µ(E), rather than just a dual energy representation of the material.…”

Section: Resultsmentioning

confidence: 99%

“…Relatedly, ensembles of target objects with sufficient material and shape diversity and realism are necessary but typically do not exist. We therefore use the physical simulation tool developed by Gong et al 14,15 to represent arbitrary transmission and scatter measurement systems and the stochastic bag generator (SBG) 16 to create representative, randomly-generated virtual bags. We use these tools together to create virtual representations of existing systems (e.g.…”

Section: Simulationmentioning

confidence: 99%

Motivations and methods for the analysis of multi-modality x-ray systems for explosives detection

Carpenter

Ding

Hurlock

et al. 2019

Anomaly Detection and Imaging With X-Rays (ADIX) IV

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Transmission-based imaging and X-ray diffraction-based material analysis have largely developed independently. However, for a variety of applications ranging from in-vivo soft tissue analysis to concealed explosives detection, it is necessary to realize high-fidelity, spatially-resolved material discrimination. We therefore seek to understand to what degree transmission and X-ray diffraction (XRD) complement one another and can be implemented practically, particularly in the case of explosives detection in aviation security. Using a combination of simulated and experimental data, we identify the relative value of the X-ray signatures available to transmission and XRD measurements, and explore how the measurement fidelity can impact these results.

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“…In many fields, the possibility to create artificial data is considered to overcome the challenge of acquiring large datasets. Monte-Carlo simulations using 3D models of objects of interest were proposed for a variety of manufacturing defect detection problems 4 , luggage inspection 18 , breast X-ray imaging 19 . While this approach could generate realistic data, it requires detailed knowledge of the X-ray imaging setup (source and detector properties) and objects that are supposed to be studied (3D model and material properties).…”

Section: Related Workmentioning

confidence: 99%

CT-based data generation for foreign object detection on a single X-ray projection

Andriiashen

Liere

Leeuwen

et al. 2023

Sci Rep

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Although X-ray imaging is used routinely in industry for high-throughput product quality control, its capability to detect internal defects has strong limitations. The main challenge stems from the superposition of multiple object features within a single X-ray view. Deep Convolutional neural networks can be trained by annotated datasets of X-ray images to detect foreign objects in real-time. However, this approach depends heavily on the availability of a large amount of data, strongly hampering the viability of industrial use with high variability between batches of products. We present a computationally efficient, CT-based approach for creating artificial single-view X-ray data based on just a few physically CT-scanned objects. By algorithmically modifying the CT-volume, a large variety of training examples is obtained. Our results show that applying the generative model to a single CT-scanned object results in image analysis accuracy that would otherwise be achieved with scans of tens of real-world samples. Our methodology leads to a strong reduction in training data needed, improved coverage of the combinations of base and foreign objects, and extensive generalizability to additional features. Once trained on just a single CT-scanned object, the resulting deep neural network can detect foreign objects in real-time with high accuracy.

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Rapid simulation of X-ray transmission imaging for baggage inspection via GPU-based ray-tracing

Cited by 16 publications

References 24 publications

Invertibility of multi‐energy X‐ray transform

Invertibility of multi‐energy X‐ray transform

Motivations and methods for the analysis of multi-modality x-ray systems for explosives detection

CT-based data generation for foreign object detection on a single X-ray projection

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