Energy-resolved Photon-counting Detector Research Articles

A blurring correction method suitable to analyze quantitative x-ray images derived from energy-resolving photon counting detector

Objective. The purpose of this study is to propose a novel blurring correction method that enables accurate quantitative analysis of the object edge when using energy-resolving photon counting detectors (ERPCDs). Although the ERPCDs have the ability to generate various quantitative analysis techniques, such as the derivations of effective atomic number (Z eff) and bone mineral density values, at the object edge in these quantitative images, accurate quantitative information cannot be obtained. This is because image blurring prevents the gathering of accurate primary x-ray attenuation information. Approach. We developed the following procedure for blurring correction. A 5 × 5 pixels masking region was set as the processing area, and the pixels affected by blurring were extracted from the analysis of pixel value distribution. The blurred pixel values were then corrected to the proper values estimated by analyzing minimum and/or maximum values in the set mask area. The suitability of our correction method was verified by a simulation study and an experiment using a prototype ERPCD. Main results. When Z eff image of aluminum objects (Z eff = 13) were analyzed without applying our correction method, regardless of raw data or correction data applying a conventional edge enhancement method, the proper Z eff values could not be derived for the object edge. In contrast, when applying our correction method, 82% of pixels affected by blurring were corrected and the proper Z eff values were calculated for those pixels. As a result of investigating the applicability limits of our method through simulation, it was proven that it works effectively for objects with 4 × 4 pixels or more. Significance. Our method is effective in correcting image blurring when the quantitative image is calculated based on multiple images. It will become an in-demand technology for putting a quantitative diagnosis into actual medical examinations.

Window-Based Energy Selecting X-ray Imaging and Charge Sharing in Cadmium Zinc Telluride Linear Array Detectors for Contaminant Detection

The spectroscopic and imaging performance of energy-resolved photon counting detectors, based on new sub-millimetre boron oxide encapsulated vertical Bridgman cadmium zinc telluride linear arrays, are presented in this work. The activities are in the framework of the AVATAR X project, planning the development of X-ray scanners for contaminant detection in food industry. The detectors, characterized by high spatial (250 µm) and energy (<3 keV) resolution, allow spectral X-ray imaging with interesting image quality improvements. The effects of charge sharing and energy-resolved techniques on contrast-to-noise ratio (CNR) enhancements are investigated. The benefits of a new energy-resolved X-ray imaging approach, termed window-based energy selecting, in the detection of low- and high-density contaminants are also shown.

Enhanced laboratory x-ray particle tracking velocimetry with newly developed tungsten-coated O(50 μm) tracers

Tracer particles designed specifically for X-ray particle tracking and imaging velocimetry (XPTV and XPIV) are necessary to widen the range of flows that can be studied with these techniques. In this study, we demonstrate in-lab XPTV using new, custom-designed O(50 μm) diameter tungsten-coated hollow carbon spheres and a single energy threshold photon counting detector. To the best knowledge of the authors, these are the first O(50 μm) tracer particles to be developed specifically for X-ray particle velocimetry. To explore the measurement quality enhancement enabled by the new tracer particles and photon counting detector, a well understood Poiseuille pipe flow is measured. The data show agreement with the analytical solution for the depth-averaged velocity profile. The experiment also shows that the tungsten-coated particles achieve higher contrast and are better localized than previously available silver-coated particles, making faster and more precise measurements attainable. The particles are manufactured with a readily scalable chemical vapor deposition process. We further show that laboratory XPTV is practical with currently available energy-resolving photon counting detectors (PCDs), despite their presently lower spatiotemporal resolution compared to scintillating detectors. This finding suggests that energy-thresholding identification of different classes of tracers is feasible, further motivating the exploration of the X-ray tracer particle design space. The latest generation of PCDs is incorporating multiple energy thresholds, and has higher count rate limits. In the near future one could potentially expand on the work presented and track multiple tracer species and scalar fields simultaneously.

Material classification from sparse spectral X-ray CT using vectorial total variation based on L infinity norm

The development of energy resolving photon counting detectors (PCD) has paved the way to spectral X-ray Computed Tomography (CT), with which one can simultaneously extract the energy dependence of a material's linear attenuation coefficient (LAC). Spectral CT has proved to be an advanced technique to classify materials based on their physical properties such as electron density ( ρ e ) and effective atomic number ( Z eff ). However, the application of spectral CT may be hindered by the poor image reconstruction quality when used for material identification in security screening where rapid scanning is required. Image reconstruction from few projections or low radiation exposure time would enable rapid scanning. The reconstruction quality for each energy bin may also be degraded since the division of photon counts into multiple energy bins naturally leads to higher noise levels. In this work, we explore how to perform accurate spectral CT reconstructions from such data, and propose L ∞ norm-based vectorial total variation (L ∞ -VTV) regularization that uses correlations between multiple energy bins. Using experimental data acquired with a custom laboratory instrument for spectral CT, the L ∞ -VTV is tested on “real life” phantoms consisting of materials in the range of 6 ≤ Z eff ≤ 15. For material classification from only 7 projections, the L ∞ -VTV gives the relative deviations of 3.5% for ρ e and 2.4% for Z eff , whereas the total nuclear variation (TNV) of another state-of-the-art joint reconstruction and the total variation (TV) yield those of 3.4% and 3.1%, and 3.8% and 4.2%, respectively. The L ∞ -VTV is now ready for use in security screening. • A joint reconstruction algorithm with L infinity norm improves image reconstruction. • The algorithm is robust to few projections and/or low photon counts. • Using correlations between multiple energy bins enhances material identification. • L infinity norm in the algorithm leads to suppression of outliers in gradient magnitudes. • High noise levels in specific energy bins can be alleviated using unaffected bins.

A novel algorithm for extracting soft-tissue and bone images measured using a photon-counting type X-ray imaging detector with the help of effective atomic number analysis

Most of the objects targeted for X-ray examination are composed of soft-tissue and bone. We aimed to develop an algorithm for generating X-ray images which can give quantitative information of soft-tissue and bone using an energy-resolving photon-counting type imaging detector. We used polychromatic X-rays for analysis in which both the beam hardening effect and detector response were properly corrected and then succeeded in virtually treating the amount of measured X-ray attenuation as if it were measured using monochromatic X-rays.

Abstract No. 454 Photon counting C-arm computed tomography for thoracoabdominal interventional imaging: an in vivo porcine feasibility study

Prior studies on conventional CT demonstrate improved contrast resolution and radiation dose efficiency using direct conversion and energy-resolving photon counting detectors (PCD). The objective of this study was to determine the feasibility of using a prototype C-arm based PCD-CT system for thoracoabdominal interventional imaging and assess the potential benefits in comparison to flat panel detector-based cone-beam CT (FPD-CBCT). A 51 cm × 0.6 cm CdTe-based PCD strip with 100 μm pixel size was mounted to the front of the FPD of a Siemens Artis Zee interventional system. The PCD has a max frame rate of 150 fps and two adjustable energy thresholds for high-speed dual energy imaging. Eight-second rotational FPD-CBCT and PCD-CT acquisitions of the upper abdomen and lower thorax were performed in three female domestic swine (49.3 ± 2.5 kg). Acquisitions in one of the animals were performed after a stent graft was placed during a TIPS procedure. To assess radiation dose reduction potential, PCD-CT scans were performed at 25% (7.5 mGy) of the standard CTDIvol level (30 mGy) used by the conventional FPD-CBCT protocol. Images were assessed for artifacts, noise, contrast and spatial resolution. PCD-CT images had lower noise due to rejection of electronic noise by the PCD. This resulted in higher spatial and contrast resolution for PCD-CT, even at a lower radiation dose (25% of the FPD-CBCT). Blooming and streak artifacts from iodine accumulated in the kidney and bone were lower on the PCD images. Increased soft tissue contrast on the PCD images improved visualization of visceral detail, especially of bowel. Blooming and shading artifacts near the TIPS stent graft were suppressed in PCD-CT images. In this preliminary study, a prototype C-arm based PCD-CT system demonstrated potential benefits in image quality and radiation dose efficiency when compared to FDP-CBCT. Further investigation of C-arm PCD-CT is warranted in order to assess the potential for applying this technology in improving thoracoabdominal interventions.

Effective atomic number image determination with an energy-resolving photon-counting detector using polychromatic X-ray attenuation by correcting for the beam hardening effect and detector response

In this study, we propose an effective atomic number (Zeff) determination method based on a photon-counting technique. The proposed method can correct for the beam hardening effect and detector response based on polychromatic X-rays to allow high accuracy material identification. To demonstrate the effectiveness of our method, the procedure was applied to X-ray images acquired by a prototype energy-resolving photon-counting detector and we obtained an Zeff image with accuracy of Zeff ± 0.5 regardless of the mass thickness.

An Efficient One-Step Method for Spectral CT Based on an Approximate Linear Model

Spectral computed tomography (spectral CT) is a medical and biomedical imaging technique which uses the spectral information of the attenuated X-ray beam. Energy-resolved photon-counting detector is a promising technique for improved spectral CT imaging and allows to obtain material selective images. Two different kind of approaches can be used to solve the problem of spectral reconstruction consisting of material decomposition and tomographic reconstruction: the “two steps” methods which are most often projection-based methods, and the “one step” methods. While the projection-based methods are interesting for the fast computational time, the one-step methods link directly the material image domain and the multienergy sinograms domain, which is an advantage to introduce spatial prior information in the material image domain. In this work we propose a proximal operator associated to a data fidelity term (based on an empirical linear forward model) for multimaterial decomposition. This proximal operator, which has a closed-form solution, makes it possible to propose fast regularized reconstruction methods for spectral CT, in particular for data of photon-counting detectors with many energy bins. We propose a one-step method based on the alternating direction method of multipliers (ADMM) and this proximal operator. We present promising results on dual material image reconstructions obtained from simulated and experimental data. We show qualitative and quantitative comparisons with two previously published multimaterial decomposition methods.

Removing Ring Artefacts for Photon-Counting Detectors Using Neural Networks in Different Domains

The development of energy-resolving photon-counting detectors provides a new approach for obtaining spectral information in computed tomography. However, the responses of different photon counting detector pixels can be inconsistent, which will always cause stripe artefacts in projection domain and concentric ring artefacts in image domain. Traditional ring artifacts processing methods are mostly based on averaging and filtering. In this paper, we propose to use deep learning methods for ring artifacts removal respectively in image domain, projection domain and the polar coordinate system. Besides, by incorporating reconstruction process into neural networks, we unite the information from image domain and projection domain for ring artifacts removal under the framework of deep learning for the first time. A traditional ring artifacts removal method, which is based on wavelet and Fourier transform, is implemented for comparison. Quantitative analysis is performed on simulation and experimental results and it shows that deep learning based methods are promising in solving the problem of non-uniformity correction for photon-counting detectors.

Spectral Signatures of X-ray Scatter Using Energy-Resolving Photon-Counting Detectors.

Energy-resolving photon-counting detectors (PCDs) separate photons from a polychromatic X-ray source into a number of separate energy bins. This spectral information from PCDs would allow advancements in X-ray imaging, such as improving image contrast, quantitative imaging, and material identification and characterization. However, aspects like detector spectral distortions and scattered photons from the object can impede these advantages if left unaccounted for. Scattered X-ray photons act as noise in an image and reduce image contrast, thereby significantly hindering PCD utility. In this paper, we explore and outline several important characteristics of spectral X-ray scatter with examples of soft-material imaging (such as cancer imaging in mammography or explosives detection in airport security). Our results showed critical spectral signatures of scattered photons that depend on a few adjustable experimental factors. Additionally, energy bins over a large portion of the spectrum exhibit lower scatter-to-primary ratio in comparison to what would be expected when using a conventional energy-integrating detector. These important findings allow flexible choice of scatter-correction methods and energy-bin utilization when using PCDs. Our findings also propel the development of efficient spectral X-ray scatter correction methods for a wide range of PCD-based applications.

A framework for performance characterization of energy-resolving photon-counting detectors.

Photon-counting, energy-resolving detectors are subject to intense research interest, and there is a need for a general framework for performance assessment of these detectors. The commonly used linear-systems theory framework, which measures detector performance in terms of noise-equivalent quanta (NEQ) and detective quantum efficiency (DQE) is widely used for characterizing conventional x-ray detectors but does not take energy-resolving capabilities into account. The purpose of this work is to extend this framework to encompass energy-resolving photon-counting detectors and elucidate how the imperfect energy response and other imperfections in real-world detectors affect imaging performance, both for feature detection and for material quantification tasks. We generalize NEQ and DQE to matrix-valued quantities as functions of spatial frequency, and show how these matrices can be calculated from simple Monte Carlo simulations. To demonstrate how the new metrics can be interpreted, we compute them for simplified models of fluorescence and Compton scatter in a photon-counting detector and for a Monte Carlo model of a CdTe detector with pixels. Our results show that the ideal-linear-observer performance for any detection or material quantification task can be calculated from the proposed generalized NEQ and DQE metrics. We also demonstrate that the proposed NEQ metric is closely related to a generalized version of the Cramér-Rao lower bound commonly used for assessing material quantification performance. Off-diagonal elements in the NEQ and DQE matrices are shown to be related to loss of energy information due to imperfect energy resolution. The Monte Carlo model of the CdTe detector predicts a zero-frequency dose efficiency relative to an ideal detector of 0.86 and 0.65 for detecting water and bone, respectively. When the task instead is to quantify these materials, the corresponding values are 0.34 for water and 0.26 for bone. We have developed a framework for assessing the performance of photon-counting energy-resolving detectors and shown that the matrix-valued NEQ and DQE metrics contain sufficient information for calculating the dose efficiency for both detection and quantification tasks, the task having any spatial and energy dependence. This framework will be beneficial for the development and optimization of photon-counting x-ray detectors.

Spectral correction algorithm for multispectral CdTe x-ray detectors

Compared to the dual-energy scintillator detectors widely used today, energy-resolved photon-counting x-ray detectors show the potential to improve material identification in various radiography and tomography applications used for industrial and security purposes. However, detector effects, such as charge sharing and photon pileup, distort the measured spectra in pixelated, photon-counting detectors operating under high flux. These effects result in a significant performance degradation of the detectors when used for material identification where accurate spectral measurements are required. We have developed a semianalytical, postdata acquisition, computational algorithm that corrects the measured attenuation curve for severe spectral distortions caused by the detector. The calibration of the algorithm is based on simple attenuation measurements of commercially available materials using standard laboratory sources, enabling the algorithm to be used in any x-ray setup. The algorithm is developed for correcting spectral data acquired with the MultiX ME100 CdTe x-ray detector but could be adapted with small adjustments to other photon-counting, energy-resolved detectors with CdTe sensors. The validation of the algorithm has been done using experimental data acquired with both a standard laboratory source and synchrotron radiation. The experiments show that the algorithm is fast, reliable at x-ray flux up to 5 Mph / s / mm2 and greatly improves the accuracy of the measured spectrally resolved linear attenuation, making the algorithm useful for both security and industrial applications where photon-counting detectors are used.

Investigation of contrast-enhanced subtracted breast CT images with MAP-EM based on projection-based weighting imaging.

Contrast-enhanced subtracted breast computer tomography (CESBCT) images acquired using energy-resolved photon counting detector can be helpful to enhance the visibility of breast tumors. In such technology, one challenge is the limited number of photons in each energy bin, thereby possibly leading to high noise in separate images from each energy bin, the projection-based weighted image, and the subtracted image. In conventional low-dose CT imaging, iterative image reconstruction provides a superior signal-to-noise compared with the filtered back projection (FBP) algorithm. In this paper, maximum a posteriori expectation maximization (MAP-EM) based on projection-based weighting imaging for reconstruction of CESBCT images acquired using an energy-resolving photon counting detector is proposed, and its performance was investigated in terms of contrast-to-noise ratio (CNR). The simulation study shows that MAP-EM based on projection-based weighting imaging can improve the CNR in CESBCT images by 117.7%-121.2% compared with FBP based on projection-based weighting imaging method. When compared with the energy-integrating imaging that uses the MAP-EM algorithm, projection-based weighting imaging that uses the MAP-EM algorithm can improve the CNR of CESBCT images by 10.5%-13.3%. In conclusion, MAP-EM based on projection-based weighting imaging shows significant improvement the CNR of the CESBCT image compared with FBP based on projection-based weighting imaging, and MAP-EM based on projection-based weighting imaging outperforms MAP-EM based on energy-integrating imaging for CESBCT imaging.

Comparison Study of Regularizations in Spectral Computed Tomography Reconstruction.

The energy-resolving photon-counting detectors in spectral computed tomography (CT) can acquire projections of an object in different energy channels. In other words, they are able to reliably distinguish the received photon energies. These detectors lead to the emerging spectral CT, which is also called multi-energy CT, energy-selective CT, color CT, etc. Spectral CT can provide additional information in comparison with the conventional CT in which energy integrating detectors are used to acquire polychromatic projections of an object being investigated. The measurements obtained by X-ray CT detectors are noisy in reality, especially in spectral CT where the photon number is low in each energy channel. Therefore, some regularization should be applied to obtain a better image quality for this ill-posed problem in spectral CT image reconstruction. Quadratic-based regularizations are not often satisfactory as they blur the edges in the reconstructed images. As a result, different edge-preserving regularization methods have been adopted for reconstructing high quality images in the last decade. In this work, we numerically evaluate the performance of different regularizers in spectral CT, including total variation, non-local means and anisotropic diffusion. The goal is to provide some practical guidance to accurately reconstruct the attenuation distribution in each energy channel of the spectral CT data.

K-edge energy-based calibration method for photon counting detectors

In recent years, potential applications of energy-resolved photon counting detectors (PCDs) in the x-ray medical imaging field have been actively investigated. Unlike conventional x-ray energy integration detectors, PCDs count the number of incident x-ray photons within certain energy windows. For PCDs, the interactions between x-ray photons and photoconductor generate electronic voltage pulse signals. The pulse height of each signal is proportional to the energy of the incident photons. By comparing the pulse height with the preset energy threshold values, x-ray photons with specific energies are recorded and sorted into different energy bins. To quantitatively understand the meaning of the energy threshold values, and thus to assign an absolute energy value to each energy bin, energy calibration is needed to establish the quantitative relationship between the threshold values and the corresponding effective photon energies. In practice, the energy calibration is not always easy, due to the lack of well-calibrated energy references for the working energy range of the PCDs. In this paper, a new method was developed to use the precise knowledge of the characteristic K-edge energy of materials to perform energy calibration. The proposed method was demonstrated using experimental data acquired from three K-edge materials (viz., iodine, gadolinium, and gold) on two different PCDs (Hydra and Flite, XCounter, Sweden). Finally, the proposed energy calibration method was further validated using a radioactive isotope (Am-241) with a known decay energy spectrum.

Semi-analytic off-axis X-ray source model

Spectral computed tomography (CT) systems are employed with energy-resolving photon counting detectors. Incorporation of a spectrally accurate x-ray beam model in image reconstruction helps to improve material identification and quantification by these systems. Using an inaccurate x-ray model in spectral reconstruction can lead to severe image artifacts, one of the extreme cases of this is the well-known beam-hardening artifacts. An often overlooked spectral feature of x-ray beams in spectral reconstruction models is the angular dependence of the spectrum with reference to the central beam axis. To address these factors, we have developed a parameterized semi-analytical x-ray source model in the diagnostic imaging range (30-120 kVp) by applying regression techniques to data obtained from Monte Carlo simulations (EGSnrc). This x-ray beam model is generalized to describe the off-axis spectral information within ±17o along θ (vertical direction), ±5o along ϕ (horizontal direction) of the central axis, and can be parameterized for specific x-ray tube models. Comparisons of our model with those generated by SpekCalc, TOPAS, and IPEM78 at central axis show good agreement (within 2 %). We have evaluated the model with experimental data collected with a small animal spectral scanner.

Material Decomposition through Weighted Image Subtraction in Dual-energy Spectral Mammography with an Energy-resolved Photon-counting Detector using Monte Carlo Simulation

Material Decomposition through Weighted Image Subtraction in Dual-energy Spectral Mammography with an Energy-resolved Photon-counting Detector using Monte Carlo Simulation

ChromAIX2: A large area, high count-rate energy-resolving photon counting ASIC for a Spectral CT Prototype

Spectral CT based on energy-resolving photon counting detectors is expected to deliver additional diagnostic value at a lower dose than current state-of-the-art CT [1]. The capability of simultaneously providing a number of spectrally distinct measurements not only allows distinguishing between photo-electric and Compton interactions but also discriminating contrast agents that exhibit a K-edge discontinuity in the absorption spectrum, referred to as K-edge Imaging [2]. Such detectors are based on direct converting sensors (e.g. CdTe or CdZnTe) and high-rate photon counting electronics.To support the development of Spectral CT and show the feasibility of obtaining rates exceeding 10Mcps/pixel (Poissonian observed count-rate), the ChromAIX ASIC has been previously reported showing 13.5Mcps/pixel (150Mcps/mm2 incident) [3]. The ChromAIX has been improved to offer the possibility of a large area coverage detector, and increased overall performance. The new ASIC is called ChromAIX2, and delivers count-rates exceeding 15Mcps/pixel with an rms-noise performance of approximately 260 e-. It has an isotropic pixel pitch of 500µm in an array of 22×32 pixels and is tile-able on three of its sides. The pixel topology consists of a two stage amplifier (CSA and Shaper) and a number of test features allowing to thoroughly characterize the ASIC without a sensor. A total of 5 independent thresholds are also available within each pixel, allowing to acquire 5 spectrally distinct measurements simultaneously. The ASIC also incorporates a baseline restorer to eliminate excess currents induced by the sensor (e.g. dark current and low frequency drifts) which would otherwise cause an energy estimation error.In this paper we report on the inherent electrical performance of the ChromAXI2 as well as measurements obtained with CZT (CdZnTe)/CdTe sensors and X-rays and radioactive sources.

Non-invasive classification of breast microcalcifications using x-ray coherent scatter computed tomography

We investigate the use of energy dispersive x-ray coherent scatter computed tomography (ED-CSCT) as a non-invasive diagnostic method to differentiate between type I and type II breast calcifications. This approach is sensitive to the differences of composition and internal crystal structure of different types of microcalcifications. The study is carried out by simulating a CSCT system with a scanning pencil beam, considering a polychromatic x-ray source and an energy-resolving photon counting detector. In a first step, the multidimensional angle and energy distributed CSCT data is reduced to the projection-space distributions of only a few components, corresponding to the expected target composition: adipose, glandular tissue, weddellite (calcium oxalate) for type I calcifications, and hydroxyapatite for type II calcifications. The maximum-likelihood estimation of scatter components algorithm used, operating in the projection space, takes into account the polychromatic source, the detector response function and the energy dependent attenuation. In the second step, component images are reconstructed from the corresponding estimated component projections using filtered backprojection. In a preliminary step the coherent scatter differential cross sections for hydroxyapatite and weddellite minerals were determined experimentally. The classification of type I or II calcifications is done using the relative contrasts of their components as the criterion. Simulation tests were carried out for different doses and energy resolutions for multiple realizations. The results were analyzed using relative/receiver operating characteristic methodology and show good discrimination ability at medium and higher doses. The noninvasive CSCT technique shows potential to further improve the breast diagnostic accuracy and reduce the number of breast biopsies.

Dose optimization for dual-energy contrast-enhanced digital mammography based on an energy-resolved photon-counting detector: A Monte Carlo simulation study

Dual-energy contrast-enhanced digital mammography (CEDM) has been used to decompose breast images and improve diagnostic accuracy for tumor detection. However, this technique causes an increase of radiation dose and an inaccuracy in material decomposition due to the limitations of conventional X-ray detectors. In this study, we simulated the dual-energy CEDM with an energy-resolved photon-counting detector (ERPCD) for reducing radiation dose and improving the quantitative accuracy of material decomposition images. The ERPCD-based dual-energy CEDM was compared to the conventional dual-energy CEDM in terms of radiation dose and quantitative accuracy. The correlation between radiation dose and image quality was also evaluated for optimizing the ERPCD-based dual-energy CEDM technique. The results showed that the material decomposition errors of the ERPCD-based dual-energy CEDM were 0.56–0.67 times lower than those of the conventional dual-energy CEDM. The imaging performance of the proposed technique was optimized at the radiation dose of 1.09mGy, which is a half of the MGD for a single view mammogram. It can be concluded that the ERPCD-based dual-energy CEDM with an optimal exposure level is able to improve the quality of material decomposition images as well as reduce radiation dose.