Dynamic stereoscopic X-ray imaging

Evans, Paul; Hon, Hock Woon

doi:10.1016/s0963-8695(01)00061-5

Cited by 12 publications

(9 citation statements)

References 7 publications

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“… 26 In general, K A values fall close to experiment, with experimental values falling within the standard deviation of DMPC, DPPC, DOPC and POPC simulation results; however the POPE value comes out high and there is a large standard deviation in all values. Although the DOPC value is above the published experimental value of 300 mN m –1 , 63 a personal communication with E. Evans revealed that this K A value has recently been revised upward to 318 mN m –1 , 64 closer to the Lipid14 simulation result. This was not known prior to the lipid simulations.…”

Section: Validationmentioning

confidence: 59%

Lipid14: The Amber Lipid Force Field

Dickson

Madej

Skjevik

et al. 2014

J. Chem. Theory Comput.

1,109

1,033

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The AMBER lipid force field has been updated to create Lipid14, allowing tensionless simulation of a number of lipid types with the AMBER MD package. The modular nature of this force field allows numerous combinations of head and tail groups to create different lipid types, enabling the easy insertion of new lipid species. The Lennard-Jones and torsion parameters of both the head and tail groups have been revised and updated partial charges calculated. The force field has been validated by simulating bilayers of six different lipid types for a total of 0.5 μs each without applying a surface tension; with favorable comparison to experiment for properties such as area per lipid, volume per lipid, bilayer thickness, NMR order parameters, scattering data, and lipid lateral diffusion. As the derivation of this force field is consistent with the AMBER development philosophy, Lipid14 is compatible with the AMBER protein, nucleic acid, carbohydrate, and small molecule force fields.

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Section: Validationmentioning

confidence: 59%

Lipid14: The Amber Lipid Force Field

Dickson

Madej

Skjevik

et al. 2014

J. Chem. Theory Comput.

1,109

1,033

View full text Add to dashboard Cite

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“…These state-of-the-art 3D X-ray imaging techniques access volumetric information by scanning the sample over different exposures, which is a time-consuming process. Thus, the scanning process may hamper the applicability of these 3D imaging approaches to i) 3D temporally-resolved studies [9,10], where the scanning process takes longer than the studied dynamics or the scanning may induce forces that alter the studied dynamics, ii) experiments where the setup or configuration makes the scanning process difficult or impossible [11,12], e.g., in medical surgeries and guidance approaches, and iii) dose-limited experiments [13,14], for example medical applications or radiosensitive samples.…”

Section: Introductionmentioning

confidence: 99%

ONIX: an X-ray deep-learning tool for 3D reconstructions from sparse views

Zhang¹,

Yao²,

Ritschel³

et al. 2022

Preprint

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Three-dimensional (3D) X-ray imaging techniques like tomography and confocal microscopy are crucial for academic and industrial applications. These approaches access 3D information by scanning the sample with respect to the X-ray source. However, the scanning process limits the temporal resolution when studying dynamics and is not feasible for some applications, such as surgical guidance in medical applications. Alternatives to obtaining 3D information when scanning is not possible are X-ray stereoscopy and multi-projection imaging. However, these approaches suffer from limited volumetric information as they only acquire a small number of views or projections compared to traditional 3D scanning techniques. Here, we present ONIX (Optimized Neural Implicit X-ray imaging), a deep-learning algorithm capable of retrieving 3D objects with arbitrary large resolution from only a set of sparse projections. ONIX, although it does not have access to any volumetric information, outperforms current 3D reconstruction approaches because it includes the physics of image formation with X-rays, and it generalizes across different experiments over similar samples to overcome the limited volumetric information provided by sparse views. We demonstrate the capabilities of ONIX compared to state-of-the-art tomographic reconstruction algorithms by applying it to simulated and experimental datasets, where a maximum of eight projections are acquired. We anticipate that ONIX will become a crucial tool for the X-ray community by i) enabling the study of fast dynamics not possible today when implemented together with X-ray multi-projection imaging, and ii) enhancing the volumetric information and capabilities of X-ray stereoscopic imaging in medical applications.

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“…Rapid non-destructive characterization of extended (mm sized) objects is an essential requirement for material science [1][2][3], security screening [4][5][6][7][8] and medicine [9]. At the mm length scale non-destructive structural probes are limited mostly to highly penetrating radiation, such as hard X-ray.…”

Section: Introductionmentioning

confidence: 99%

“…At the mm length scale non-destructive structural probes are limited mostly to highly penetrating radiation, such as hard X-ray. Volumetric X-ray imaging techniques such as CT (computerized tomography), X-ray tomosynthesis [7], laminography [10] and phase contrast imaging [3] have developed into rapid screening tools where mass attenuation coefficients and refractive indices provide the contrast mechanisms, respectively. In addition recent advances in switched X-ray sources [11] demonstrate the potential to replace mechanical scanning.…”

Section: Introductionmentioning

confidence: 99%

Sporadic absorption tomography using a conical shell X-ray beam

Elarnaut¹,

Evans²,

Downes³

et al. 2017

Opt. Express

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Abstract:We demonstrate tomography by measuring a sporadic sequence of ring shaped projections collected during a translational scan. We show that projections using 10% sampling may be used to construct optical sections with peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) of the order of 40 dB and 0.9, respectively. This relatively small degradation in image fidelity was achieved for a 90% potential reduction in X-ray dose coupled with a reduction in scan time. Our approach is scalable in both X-ray energy and inspection volume. A driver for our method is to complement previously reported conical shell beam techniques concerning the measurement of diffracted flux for structural analysis. This work is of great relevance to time critical analytical scanning applications in security screening, process control and diagnostic imaging. Withers, and R. J. Cernik, "3D chemical imaging in the laboratory by hyperspectral X-ray computed tomography," Sci. Rep. 5(1), 15979 (2015). 14. S. Singh and M. Singh, "Explosives detection systems (EDS) for aviation security," Signal Proc. 83(1), 31-55 (2003). 15. K. Wells and D. A. Bradley, "A review of X-ray explosives detection techniques for checked baggage," Appl.

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Dynamic stereoscopic X-ray imaging

Cited by 12 publications

References 7 publications

Lipid14: The Amber Lipid Force Field

Lipid14: The Amber Lipid Force Field

ONIX: an X-ray deep-learning tool for 3D reconstructions from sparse views

Sporadic absorption tomography using a conical shell X-ray beam

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