Measurements of charge sharing in small pixel CdTe detectors

Veale, Matthew C.; Bell, Steven; Duarte, Diana D.; Schneider, Andreas; Seller, P.; Wilson, Matthew D.; Iniewski, Krzysztof

doi:10.1016/j.nima.2014.08.036

Cited by 97 publications

(66 citation statements)

References 6 publications

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“…This is implemented by inspecting indi-vidual frames of data: if, in the same frame of data, neighbouring pixels simultaneously register an X-ray event, then they are removed from the processed data. For more detailed information on the implementation of charge sharing algorithms see Veale et al ( 2014). The Bremsstrahlung radiation from the source had a useful energy range approximately between 20 and 100 keV with an estimated flux of 4 Â 10 6 photons s À1 mm À2 keV À1 at 40 keV at a distance of 1 m from the source.…”

Section: Methodsmentioning

confidence: 99%

Full-field energy-dispersive powder diffraction imaging using laboratory X-rays

Egan

Jacques²,

Wilson

et al. 2015

J Appl Cryst

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A laboratory instrument with the ability to spatially resolve energy-dispersed X-ray powder diffraction patterns taken in a single snapshot has been developed. The experimental arrangement is based on a pinhole camera coupled with a pixelated spectral X-ray detector. Collimation of the diffracted beam is defined by the area of the footprint of a detector pixel and the diameter of the pinhole aperture. Each pixel in the image, therefore, contains an energydispersed powder diffraction pattern. This new X-ray imaging technique enables spatial mapping of crystallinity, crystalline texture or crystalline phases from within a sample. Validation of the method has been carried out with a back-toback comparison with crystalline texture mapping local to a friction stir weld in an aluminium alloy taken using synchrotron radiation.

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Section: Methodsmentioning

confidence: 99%

Full-field energy-dispersive powder diffraction imaging using laboratory X-rays

Egan

Jacques²,

Wilson

et al. 2015

J Appl Cryst

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“…The measured spectrum for a given detector element is a result of the spectra incident on that detector element and its neighbors. 31 However, the neural network developed in this study estimates the basis material components for each ray independently. Furthermore, the neural network was trained from step wedge data where all detector elements imaged the same object simultaneously resulting in a similar number of counts detected in all pixels.…”

Section: M2mentioning

confidence: 99%

Experimental investigation of neural network estimator and transfer learning techniques for K‐edge spectral CT imaging

et al. 2020

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Purpose Spectral computed tomography (CT) material decomposition algorithms require accurate physics‐based models or empirically derived models. This study investigates a machine learning algorithm and transfer learning techniques for Spectral CT imaging of K‐edge contrast agents using simulated and experimental measurements. Methods A feed forward multilayer perceptron was implemented and trained on data acquired using a step wedge phantom containing acrylic, aluminum, and gadolinium materials. The neural network estimator was evaluated by scanning a rod phantom with varying dilutions of gadolinium oxide nanoparticles and by scanning a rat leg specimen with injected nanoparticles on a bench‐top photon‐counting computed tomography system. The algorithm decomposed each spectral projection measurement into path lengths of acrylic and aluminum and mass lengths of gadolinium. Each basis material sinogram was reconstructed into basis material images using filtered backprojection. Machine learning techniques of data standardization, transfer learning from aggregated pixel data, and transfer learning from simulations were investigated to improve image quality. The algorithm was compared to a previously published empirical method based on a linear approximation and calibration error look‐up tables. Results The combined transfer learning techniques did not improve quantification in the rod phantom and provided only a small qualitative improvement in ring artifacts. Transfer learning from aggregated pixel data and from simulations improved the qualitative image quality of the rat specimen, for which the calibration data were limited. Transfer learning from aggregated pixel data and simulations estimated 3.26, 6.26, and 12.45 mg/mL Gd concentrations compared to true 2.72, 5.44, and 10.88 mg/mL concentrations in the rod phantom. Additionally, the neural networks were able to separate the soft tissue, bone, and gadolinium nanoparticles of the ex vivo rat leg specimen into the different basis images. Conclusions The results demonstrate that empirical K‐edge imaging from calibration measurements using machine learning and transfer learning is possible without explicit models of material attenuations, incident spectra, or the detector response.

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“…The X-ray events are known to generate charge clouds that are less than 200µm in size [14]. Software was written to remove the events that spanned more than one pixel,…”

Section: Data Processingmentioning

confidence: 99%

Element Specific Imaging Using Muonic X-rays

Hillier

Ishida

Seller

et al. 2018

Proceedings of the 14th International Conference on Muon Spin Rotation, Relaxation and Resonance (ΜSR2017)

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The RIKEN-RAL facility provides a source of negative muons that can be used to non-destructively determine the elemental composition of bulk samples. A negative muon can replace an electron in an atom and subsequently transition to lower orbital positions. As with conventional X-ray fluorescence, an X-ray photon is emitted with a characteristic energy to enable the transition between orbitals of an atom. As the mass of a negative muon is much greater than that of an electron, a higher energy X-ray photon is emitted when the negative muon transitions between orbitals compared to conventional X-ray fluorescence. The higher energy muonic X-rays are able to escape large samples even when they are emitted from lower Z atoms, making muonic X-rays fluorescence a unique method to characterize the elemental content of a sample. In a typical experiment a section of a sample will be probed with negative muons with the muon momentum tuned to interact at a desired depth in the sample. A small number of single element high purity Ge detectors are positioned to capture up to one photon each from each of the forty muon pulses per second at the RIKEN-RAL facility. This can provide a high resolution and high dynamic range X-ray energy spectrum when collected for several hours but can only provide a spatial average or single point elemental distribution per collection. Here, an STFC developed CdTe detector with 80 x 80 energy resolving channels has been used to demonstrate the ability to image the elemental distribution of a test sample. A test sample of C, Al and Fe 2 O 3 was positioned close to the detector surface and each of the 250 µm pitch pixels recorded a muonic X-ray energy spectrum. Results are presented to show the principal of this new technique and potential improvements to provide higher resolution and larger area elemental imaging using muonic X-rays are discussed.

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Measurements of charge sharing in small pixel CdTe detectors

Cited by 97 publications

References 6 publications

Full-field energy-dispersive powder diffraction imaging using laboratory X-rays

Full-field energy-dispersive powder diffraction imaging using laboratory X-rays

Experimental investigation of neural network estimator and transfer learning techniques for K‐edge spectral CT imaging

Element Specific Imaging Using Muonic X-rays

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