Energy Bins Research Articles

Machine learning models for binary molecular classification using VUV absorption spectra

Machine learning methods were combined with differential absorption spectroscopy measurements in the vacuum-ultraviolet region (5.167 – 9.920 eV) in order to develop predictive capabilities for inferring molecular structure from the spectra. Several types of species were analyzed and, for modeling purposes, were defined using a single classification: (1) alkane, (2) conjugation with oxygen (e.g. diacetyl, ethyl vinyl ether), (3) non-conjugated alkene (e.g. 1-butene, 1,4-cyclohexadiene), (4) oxygen-containing (e.g. 1-butanol, tetrahydrofuran), or (5) cyclic (e.g. cyclopentane, cyclohexanone). The latter molecular classification excluded cyclic ethers. Several modeling methods were employed in the analysis of 102 absorption spectra, 24 of which were measured for the first time.The primary objective was to identify suitable methods that enable accurate predictions of molecular structure classifications with minimized statistical uncertainties. Rather than identifying a single, unifying method to reliably predict molecular structure contributions to VUV absorption spectra, coordination is required among a particular method, the type of molecular structure detail (e.g. conjugation), and absorption region of interest. The latter is accomplished using a binning approach, wherein absorption regions of ∼0.5 eV were utilized rather than the entire ∼4.8 eV range. Photon energy binning enabled analysis of region-specific predictions of accuracy, precision, and recall. The outcome from the binning approach is that, rather than utilizing the entire spectrum, optimal determination of molecular structure using machine learning methods depends on the absorption region. The present work provides separate machine learning models for each molecular classification, which enables the identification of multi-functional species relevant to atmospheric chemistry and combustion chemistry, where isomer-resolved speciation is critical to understanding complex reaction networks.

Implementation of pencil beam redefinition algorithm (PBRA) for intraoperative electron radiation therapy (IOERT) treatment planning

PurposeSimilar to other radiation therapy techniques, intraoperative electron radiation therapy (IOERT) can benefit from an online treatment planning system (TPS). Among all the analytical electron dose calculation algorithms, pencil beam redefinition algorithm (PBRA) has shown an acceptable accuracy in inhomogeneities. The input dataset for PBRA includes electron planar fluence, mean direction and root mean square (RMS) spread about the mean direction which had been introduced based on the conventional linear accelerator geometry in former studies. Herein, three methods for implementing PBRA for IOERT system are presented. MethodsThe initialization parameters were identified using Monte Carlo (MC) simulation of a dedicated IOERT system equipped with a cylindrical 10 cm applicator, irradiating a water phantom. Phase space distribution of electrons was recorded on a plane below the applicator. The input dataset was extracted for 2 × 2 mm2 pixels and energy bin width of 1 MeV. ResultsPBRA was implemented with three initialization methods and compared to MC. The 3D gamma analysis of the algorithm with the Formula method, which was in best agreement with MC in a simple water phantom, showed passing rates of more than 99 % for all nominal energies and it was 97.1 % for 8 MeV in the presence of protecting disk and irregular surface. Implementing PBRA on CUDA C++ resulted in 5 s run time for 8 MeV nominal energy in a water phantom. ConclusionsThe agreement between PBRA dose calculation and MC is promising for the development of an intraoperative TPS for IOERT.

Triple-source saddle-curve cone-beam photon counting CT image reconstruction: A simulation study

PurposeThe most common detector material in the PC CT system, cannot achieve the best performance at a relatively higher photon flux rate. In the reconstruction view, the most commonly used filtered back projection, is not able to provide sufficient reconstructed image quality in spectral computed tomography (CT). Developing a triple-source saddle-curve cone-beam photon counting CT image reconstruction method can improve the temporal resolution. MethodsTriple-source saddle-curve cone-beam trajectory was rearranged into four trajectory sets for simulation and reconstruction. Projection images in different energy bins were simulated by forward projection and photon counting CT respond model simulation. After simulation, the object was reconstructed using Katsevich’s theory after photon counts correction using the pseudo inverse of photon counting CT response matrix. The material decomposition can be performed based on images in different energy bins. ResultsRoot mean square error (RMSE) and structural similarity index (SSIM) are calculated to quantify the image quality of reconstruction images. Compared with FDK images, the RMSE for the triple-source image was improved by 27%, 21%, 14%, 8%, and 6% for the reconstrued image of 20–33, 33–47, 47–58, 58–69, 69–80 keV energy bin. The SSIM was improved by 1.031%, 0.665%, 0.396%, 0.235%, 0.174% for corresponding energy bin. The decomposition image based on corrected images shows improved RMSE and SSIM, each by 33.861% and 0.345%. SSIM of corrected decomposition image of iodine reaches 99.415% of the original image. ConclusionsA new Triple-source saddle-curve cone-beam PC CT image reconstruction method was developed in this work. The exact reconstruction of the triple-source saddle-curve improved both the image quality and temporal resolution.

Dual energy-based quantification method for determination of breast microcalcification types

The purpose of this study is to distinguish and characterize breast microcalcification types (benign and malignant) non-invasively using photon-counting spectral mammography. In this study, a photon-counting spectral mammography system was simulated using Geant4 application for tomographic emission (GATE) tool. Based on the photon-counting detector, dual-energy images were obtained using two energy bins. Calcium oxalate and calcium hydroxyapatite were used for benign and malignant microcalcifications, respectively. As a quantitative characteristic evaluation for two types of microcalcification related to etiology, an analytical model was implemented for the determination of the calcium/phosphorus mass ratio (mCa/mP) based on the dual-energy method. In the chemical composition of each microcalcification, the calcium hydroxyapatite has a phosphate substance, and calcium oxalate has an oxalate substance. The thicknesses of the calcium oxalate corresponding to calcium hydroxyapatite were calculated to preserve equal photon beam attenuation. The results indicate a higher mass ratio value in the case of malignant microcalcifications when compared with that in the case of benign ones. Even when the breast thickness increased up to 5 cm, the difference in the mass ratio exhibited a constant tendency. By excluding the effect on x-ray attenuation properties, we confirmed that malignant and benign microcalcifications were quantitatively distinguished based only on the mineral properties of the chemical composition. In conclusion, this study provides quantitative and objective indicators, a non-invasive discrimination method of two types of microcalcifications associated with pathology. We also expect to reduce the frequency of biopsies by improving the accuracy of early diagnosis in mammography.

Investigation of deflection angle for muon energy classification in muon scattering tomography via GEANT4 simulations

In muon scattering tomography, the investigated materials are discriminated according to the scattering angle that mainly depends on the atomic number, the density, and the thickness of the medium at a given energy value. The scattering angles at different initial energies also provide the opportunity to classify the incoming muons into a number of energy groups. In this study, by employing the GEANT4 code, we show that the deflection angle exponentially decays as a function of energy, and the numerical values for the current configuration are below the detector accuracy except the initial energy bins owing to the low-Z, low density, and low thickness of the current plastic scintillators. This implies the necessity of additional components that provoke the muon scattering. Therefore, we introduce stainless steel surfaces into the top and bottom sections in order to amplify the deflection angle as well as to reduce the uncertainty, thereby improving the detector performance.

Metal artifact correction in photon-counting detector computed tomography: metal trace replacement using high-energy data.

Metal artifacts have been an outstanding issue in computed tomography (CT) since its first uses in the clinic and continue to interfere. Metal artifact reduction (MAR) methods continue to be proposed and photon-counting detectors (PCDs) have recently been the subject of research toward this purpose. PCDs offer the ability to distinguish the energy of incident x-rays and sort them in a set number of energy bins. High-energy data captured using PCDs have been shown to reduce metal artifacts in reconstructions due to reduced beam hardening. High-energy reconstructions using PCD-CT have their drawbacks, such as reduced image contrast and increased noise. Here, we demonstrate a MAR algorithm, trace replacement MAR (TRMAR), in which the data corrupted by metal artifacts in full energy spectrum projections are corrected using the high-energy data captured during the same scan. The resulting reconstructions offer similar MAR to that seen in high-energy reconstructions, but with improved image quality. Experimental data were collected using a bench-top PCD-CT system with a cadmium zinc telluride PCD. Simulations were performed to determine the optimal high-energy threshold and to test TRMAR in simulations using the XCAT phantom and a biological sample. For experiments a 100-mm diameter cylindrical phantom containing vials of water, two screws, various densities of Ca(ClO4 )2 , and a spatial resolution phantom was imaged with and without the screws. The screws were segmented in the initial reconstruction and forward projected to identify them in the sinogram space in order to perform TRMAR. The resulting reconstructions were compared to the control and to reconstructions corrected using normalized metal artifact reduction (NMAR). Additionally, a beef short rib was imaged with and without metal to provide a more realistic phantom. XCAT simulations showed a reduction in the streak artifact from -978HU in uncorrected images to -10HU with TRMAR. The magnitude of the metal artifact in uncorrected images of the 100-mm phantom was -442 HU, compared to the desired -81HU with no metal. TRMAR reduced the magnitude of the artifact to -142 HU, with NMAR reducing the magnitude to -96 HU. Relative image noise was reduced from 176% in the high-energy image to 56% using TRMAR. Density quantification was better with NMAR, with the Ca(ClO4 )2 vial affected most by metal artifacts showing 0.8% error compared to 2.1% with TRMAR. Small features were preserved to a greater extent with TRMAR, with the limiting spatial frequency at 20% of the MTF fully maintained at 1.31 lp/mm, while with NMAR it was reduced to 1.22 lp/mm. Images of the beef short rib showed better delineation of the shape of the metal using TRMAR. NMAR offers slightly better performance compared to TRMAR in streak reduction and image quality metrics. However, TRMAR is less susceptible to metal segmentation errors and can closely approximate the reduction in the streak metal artifact seen in NMAR at 1/3 the computation time. With the recent introduction of PCD-CT into the clinic, TRMAR offers notable potential for fast, effective MAR.

New physics in b → sμμ: FCC-hh or a muon collider?

Rare flavour-changing neutral-current transitions b → sμ+μ− probe higher energy scales than what is directly accessible at the LHC. Therefore, the presence of new physics in such transitions, as suggested by the present-day LHCb anomalies, would have a major impact on the motivation and planning of future high-energy colliders. The two most prominent options currently debated are a proton-proton collider at 100 TeV (FCC-hh) and a multi-TeV muon collider (MuC). In this work, we compare the discovery prospects at these colliders on benchmark new physics models indirectly detectable in b → sμ+μ− decays but beyond the reach of the high-pT searches at the HL-LHC. We consider a comprehensive set of scenarios: semileptonic contact interactions, Z′ from a gauged textrm{U}{(1)}_{B_3-{L}_{mu }} and textrm{U}{(1)}_{L_{mu }-{L}_{tau }} , the scalar leptoquark S3, and the vector leptoquark U1. We find that a 3 TeV MuC has a sensitivity reach comparable to the one of the FCC-hh. However, for a heavy enough mediator, the new physics effects at a 3 TeV MuC are only observed indirectly via deviations in the highest energy bin, while the FCC-hh has a greater potential for the discovery of a resonance. Finally, to completely cover the parameter space suggested by the bsμμ anomalies, among the proposed future colliders, only a MuC of 10 TeV (or higher) can meet the challenge.

Sub-pixel energy-weighting techniques for metallic contaminant highlighting in a pharmaceutical hard capsule using a Timepix3 CdZnTe hybrid pixel detector

The presented work describes the capabilities of a state-of-the-art spectroscopic photon-counting hybrid pixel detector for use in non-destructive testing, as a contaminant detector with potential uses in the food and pharmaceutical industries. A pharmaceutical hard capsule containing vitamin powder, contaminated with Steel (ST) and Tungsten (W) was prepared under controlled conditions. Both contaminants are distinguished from the powder by combining sub-pixel imaging with spectroscopic imaging analysis, made possible by the use of a 2 mm CdZnTe sensor coupled to a Timepix3 ASIC, operated in Time-over-Threshold and Time-of-Arrival mode. The sub-pixelization technique achieves a spatial resolution of 18.3 μm, with a maximum photon flux of 2.5× 106 photons/s· cm2. For the spectral analysis technique implemented, each energy bin image is energy-weighted, with weights optimally defined by considering the bin contrast and noise behavior, and combined to form an integrated-spectrum enhanced image with increased contaminant visibility. Contrast-to-Noise Ratio (CNR) for each contaminant type was used for performance assessment of the approach. A CNR increase of 106% above the reference unweighted-spectrum image was achieved for the ST contaminant, while the denser W contaminant showed a CNR increase of 80%. This means that the presented spectroscopic imaging technique is feasible for implementation in material visibility enhancement.

Spectral CT reconstruction via Spectral-Image Tensor and Bidirectional Image-gradient minimization

It is challenging to obtain good image quality in spectral computed tomography (CT) as the photon-number for the photon-counting detectors is limited for each narrow energy bin. This results in a lower signal to noise ratio (SNR) for the projections. To handle this issue, we first formulate the weight bidirectional image gradient with L0-norm constraint of spectral CT image. Then, as a new regularizer, bidirectional image gradient with L0-norm constraint is introduced into the tensor decomposition model, generating the Spectral-Image Tensor and Bidirectional Image-gradient Minimization (SITBIM) algorithm. Finally, the split-Bregman method is employed to optimize the proposed SITBIM mathematical model. The experiments on the numerical mouse phantom and real mouse experiments are designed to validate and evaluate the SITBIM method. The results demonstrate that the SITBIM can outperform other state-of-the-art methods (including TVM, TV + LR, SSCMF and NLCTF). Index terms—spectral CT, image reconstruction, tensor decomposition, unidirectional image gradient, image similarity.

Approximate Bayesian Computation applied to the Diffuse Gamma-Ray Sky

ABSTRACT Many sources contribute to the diffuse gamma-ray background (DGRB), including star forming galaxies, active galactic nuclei, and cosmic ray interactions in the Milky Way. Exotic sources, such as dark matter annihilation, may also make some contribution. The photon counts-in-pixels distribution is a powerful tool for analysing the DGRB and determining the relative contributions of different sources. However, including photon energy information in a likelihood analysis of the counts-in-pixels distribution quickly becomes computationally intractable as the number of source types and energy bins increase. Here, we apply the likelihood-free method of approximate Bayesian computation (ABC) to the problem. We consider a mock analysis that includes contributions from dark matter annihilation in Galactic subhaloes as well as astrophysical backgrounds. We show that our results using ABC are consistent with the exact likelihood when energy information is discarded, and that significantly tighter parameter constraints can be obtained with ABC when energy information is included. ABC presents a powerful tool for analysing the DGRB and understanding its varied origins.

Mesoscopic energy ranking constraints in the IllustrisTNG simulations

ABSTRACT We revisited the problem of mixing in a gravitational N-body system from the point of view of the ordering of coarse-grained cells in the one-particle energy space, here denoted energy ranking preservation (ERP). This effect has been noted for some time in simulations, although individual particle energies and their phase-space variables mix considerably. The present investigation aimed to map ERP in terms of parameters involving the collective range in which it is effective, as well as in terms of global and historical characterizations of gravitational systems evolving towards equilibrium. We examined a subset of the IllustrisTNG cosmological magnetohydrodynamical simulations (TNG50-4 and TNG100-3), considering both their full and dark-only versions. For each simulation, we selected the 20 most massive haloes at redshift z = 0, tracing their ERP fractions back at selected redshift markers (z = {1.0, 5.0, 10 0}), and for a coarse-graining set ranging from 5 to 30 energy bins. At the redshift marker z = 1, we found high ERP fractions (above ${\sim}80\,\hbox{per cent}$) in both simulations, regardless of the coarse-graining level. The decline in ERP fractions with redshift was roughly a function of mass and fractional mass increase in the analysed TNG50-4 haloes, but not in the TNG100-3 ones, indicating a possible relative susceptibility of the ERP effect to mass accretion for haloes less massive than ${\sim}10^{14} \, {\rm M}_{\odot }$. We confirmed earlier indications in the literature concerning a possible ‘mesoscopic’ constraint operative in a time span of at least several Gyr.

What can be learnt from UHECR anisotropies observations

Context.In recent years, evidence for an anisotropic distribution of ultra-high-energy cosmic rays (UHECRs) has been claimed, notably a dipole modulation in right ascension has been reported by the Auger collaboration above the 5σsignificance threshold.Aims.We investigate the implications of the current data regarding large-scale anisotropies, including higher order multipoles, and we examine to what extent they can be used to shed some light on the origin of UHECRs and constrain the astrophysical and/or physical parameters of the source scenarios. We investigate the possibility of observing an associated anisotropy of the UHECR composition and discuss the potential benefit of a good determination of the composition and of the separation of the different nuclear components. We also discuss the interest and relevance of observing the UHECR sky with larger exposure future observatories.Methods.We simulated realistic UHECR sky maps for a wide range of astrophysical scenarios satisfying the current observational constraints, taking into account the energy losses and the photo-dissociation of the UHE protons and nuclei, as well as their deflexions by intervening magnetic fields. We investigated scenarios in which the UHECR source distribution follows that of the galaxies in the Universe (with possible biases), varying the UHECR source composition and spectrum, as well as the source density and the magnetic field models. For each of them, we simulated 300 realizations of independent datasets corresponding to various assumptions for the statistics and sky coverage, and we applied similar analyses as those used by the Auger collaboration for the search of large-scale anisotropies.Results.We find the following. First, reproducing the amplitude of the first-order (dipole) anisotropy observed in the Auger data, as well as its evolution as a function of energy, is relatively easy within our general assumptions. Second, this general agreement can be obtained with different sets of assumptions on the astrophysical and physical parameters, and thus it cannot be used, at the present stage, to derive strong constraints on the UHECR source scenarios or draw model-independent constraints on the various parameters individually. Third, the actual direction of the dipole modulation reconstructed from the Auger data, in the energy bin where the signal is most significant, appears highly unnatural in essentially all scenarios investigated, and this calls for their main assumptions to be reconsidered, either regarding the source distribution itself or the assumed magnetic field configuration, especially in the Galaxy. Fourth, the energy evolution of the reconstructed dipole direction contains potentially important information, which may become constraining for specific source models when larger statistics is collected. Fifth, for such high-statistics datasets, most of our investigated scenarios predict a significant quadrupolar modulation, especially if the light component of UHECRs can be extracted from the all-particle dataset. Sixth, except for protons, the energy range in which the GZK horizon strongly reduces is a key target for anisotropy searches for each given nuclear species. Seventh, although a difference in the average composition of the UHECRs in regions having a different count rate is naturally expected in our models, it is unlikely that the composition anisotropy recently reported by Auger can be explained by this effect, unless the reported amplitude is a strong positive statistical fluctuation of an intrinsically weaker signal.

Ultrahigh resolution whole body photon counting computed tomography as a novel versatile tool for translational research from mouse to man

X-ray computed tomography (CT) is a cardinal tool in clinical practice. It provides cross-sectional images within seconds. The recent introduction of clinical photon-counting CT allowed for an increase in spatial resolution by more than a factor of two resulting in a pixel size in the center of rotation of about 150 µm. This level of spatial resolution is in the order of dedicated preclinical micro-CT systems. However so far, the need for different dedicated clinical and preclinical systems often hinders the rapid translation of early research results to applications in men. This drawback might be overcome by ultra-high resolution (UHR) clinical photon-counting CT unifying preclinical and clinical research capabilities in a single machine. Herein, the prototype of a clinical UHR PCD CT (SOMATOM CounT, Siemens Healthineers, Forchheim, Germany) was used. The system comprises a conventional energy-integrating detector (EID) and a novel photon-counting detector (PCD). While the EID provides a pixel size of 0.6 mm in the centre of rotation, the PCD provides a pixel size of 0.25 mm. Additionally, it provides a quantification of photon energies by sorting them into up to four distinct energy bins. This acquisition of multi-energy data allows for a multitude of applications, e.g. pseudo-monochromatic imaging. In particular, we examine the relation between spatial resolution, image noise and administered radiation dose for a multitude of use-cases. These cases include ultra-high resolution and multi-energy acquisitions of mice administered with a prototype bismuth-based contrast agent (nanoPET Pharma, Berlin, Germany) as well as larger animals and actual patients. The clinical EID provides a spatial resolution of about 9 lp/cm (modulation transfer function at 10%, MTF10%) while UHR allows for the acquisition of images with up to 16 lp/cm allowing for the visualization of all relevant anatomical structures in preclinical and clinical specimen. The spectral capabilities of the system enable a variety of applications previously not available in preclinical research such as pseudo-monochromatic images. Clinical ultra-high resolution photon-counting CT has the potential to unify preclinical and clinical research on a single system enabling versatile imaging of specimens and individuals ranging from mice to man.

Significance of the spectral correction of photon counting detector response in material classification from spectral x-ray CT.

Purpose: Photon counting imaging detectors (PCD) has paved the way for spectral x-ray computed tomography (spectral CT), which simultaneously measures a sample's linear attenuation coefficient (LAC) at multiple energies. However, cadmium telluride (CdTe)-based PCDs working under high flux suffer from detector effects, such as charge sharing and photon pileup. These effects result in the severe spectral distortions of the measured spectra and significant deviation of the extracted LACs from the reference attenuation curve. We analyze the influence of the spectral distortion correction on material classification performance. Approach: We employ a spectral correction algorithm to reduce the primary spectral distortions. We use a method for material classification that measures system-independent material properties, such as electron density, , and effective atomic number, . These parameters are extracted from the LACs using attenuation decomposition and are independent of the scanner specification. The classification performance with the raw and corrected data is tested on different numbers of energy bins and projections and different radiation dose levels. We use experimental data with a broad range of materials in the range of , acquired with a custom laboratory instrument for spectral CT. Results: We show that using the spectral correction leads to an accuracy increase of 1.6 and 3.8 times in estimating and , respectively, when the image reconstruction is performed from only 12 projections and the 15 energy bins approach is used. Conclusions: The correction algorithm accurately reconstructs the measured attenuation curve and thus gives better classification performance.

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.

Contaminant detection using a CZT photon counting detector with TDI image reconstruction

Food x-ray inspection systems are designed to detect unwanted physical contaminants in packaged food to maintain a high level of food safety for consumers. Modern day x-ray inspection systems often utilize line scan sensors to detect these physical contaminants but are limited to single or dual energies. However, by using a photon counting detector (PCD), a new generation of food inspection systems capable of acquiring images at more than two energy bins could improve discrimination between low density contaminants. In this work, five type of contaminants were embedded in an acrylic phantom and imaged using a cadmium zinc telluride (CZT) PCD with a pixel pitch of 330 μm. A set of images were acquired while the phantom was stationary, and another set of images were acquired while the phantom was moving to mimic the movement of a conveyor belt. Image quality was assessed by evaluating the contrast-to-noise ratio (CNR) for each set of images. For imaging times larger than 25 ms, the results showed that the moving phantom data set yielded larger CNR values compared to a stationary phantom. While conventional x-ray inspections often utilize line scan sensors, we report that physical contaminant detection is possible with a CZT PCD x-ray imaging system.

Framelet tensor sparsity with block matching for spectral CT reconstruction.

Spectral computed tomography (CT) based on the photon-counting detection system has the capability to produce energy-discriminative attenuation maps of objects with a single scan. However, the insufficiency of photons collected into the narrow energy bins results in high quantum noise levels causing low image quality. This work aims to improve spectral CT image quality by developing a novel regularization based on framelet tensor prior. First, similar patches are extracted from highly correlated interchannel images in spectral and spatial domains, and stacked to form a third-order tensor after vectorization along the energy channels. Second, the framelet tensor nuclear norm (FTNN) is introduced and applied to construct the regularization to exploit the sparsity embedded in nonlocal similarity of spectral images, and thus the reconstruction problem is modeled as a constrained optimization. Third, an iterative algorithm is proposed by utilizing the alternating direction method of multipliers framework in which efficient solvers are developed for each subproblem. Both numerical simulations and real data verifications were performed to evaluate and validate the proposed FTNN-based method. Compared to the analytic, TV-based, and the state-of-the-art tensor-based methods, the proposed method achieves higher numerical accuracy on both reconstructed CT images and decomposed material maps in the mouse data indicating the capability in noise suppression and detail preservation of the proposed method. A framelet tensor sparsity-based iterative algorithm is proposed for spectral reconstruction. The qualitative and quantitative comparisons show a promising improvement of image quality, indicating its promising potential in spectral CT imaging.

Neutron capture cross section of 169Tm measured at the CSNS Back-n facility in the energy region from 30 to 300 keV * *Supported by the National Natural Science Foundation of China (11790321, 11805282) and the National Key Research and Development Program of China (2016YFA0401601)

The capture cross sections of the 169Tm reaction were measured at the back streaming white neutron beam line (Back-n) of the China Spallation Neutron Source (CSNS) using four C6D6 liquid scintillation detectors. The background subtraction, normalization, and correction were carefully considered in the data analysis to obtain accurate cross sections. For the resonance at 3.9 eV, the R-matrix code SAMMY was used to determine the resonance parameters with the internal normalization method. The average capture cross sections of 169Tm for energy between 30 and 300 keV were extracted relative to the 197Au reaction. The measured cross sections of the 169Tm reaction were reported in logarithmically equidistant energy bins with 20 bins per energy decade with a total uncertainty of 5.4% – 7.0% in this study and described in terms of average resonance parameters using a Hauser-Feshbach calculation with fluctuations. The point-wise cross sections and the average resonance parameters showed fair agreement with the evaluated values of the ENDF/B-VIII.0 library in the energy region studied.

Constraints on Cosmic Rays Population in the Radio Halo of the M87 Galaxy from Gamma-Ray Observations

We present results of our search for extended high-energy emission around the M87 galaxy, which is the central galaxy in the Virgo cluster. The M87 galaxy harbors extremely heavy supermassive black hole, {{M}_{{BH}}} > 4 times {{10}^{9}}{kern 1pt} {{M}_{ odot }} and for the last 100 Myr experiences a period of increased activity that resulted in formation of extended lobes, which are prominent radio- and X-ray sources. We perform a search in 13 yr of the Fermi-LAT data in three energy bins: 0.1–1, 1–10, and 10–100 GeV. No significant detection of extended emission was achieved in any bin, with weak indication of a signal ( sim {kern 1pt} 2.5sigma ) in the last bin (10–100 GeV). We used γ-ray observations to put limits on cosmic rays population in the lobes, constraining their fraction X in comparison to the thermal energy of the hot gas. Depending on the value of spectral index, for electrons {{X}_{e}} < (0.1{-} 0.2) and protons were constrained at {{X}_{p}} < (0.2{-} 0.5) level. We confirm that cosmic rays is a subdominant component of the lobes, which are dominated by thermal gas.

Virtual monoenergetic images from photon-counting spectral computed tomography to assess knee osteoarthritis

BackgroundDual-energy computed tomography has shown a great interest for musculoskeletal pathologies. Photon-counting spectral computed tomography (PCSCT) can acquire data in multiple energy bins with the potential to increase contrast, especially for soft tissues. Our objectives were to assess the value of PCSST to characterise cartilage and to extract quantitative measures of subchondral bone integrity.MethodsSeven excised human knees (3 males and 4 females; 4 normal and 3 with osteoarthritis; age 80.6 ± 14 years, mean ± standard deviation) were scanned using a clinical PCSCT prototype scanner. Tomographic image reconstruction was performed after Compton/photoelectric decomposition. Virtual monoenergetic images were generated from 40 keV to 110 keV every 10 keV (cubic voxel size 250 × 250 × 250 μm3). After selecting an optimal virtual monoenergetic image, we analysed the grey level histograms of different tissues and extracted quantitative measurements on bone cysts.ResultsThe optimal monoenergetic images were obtained for 60 keV and 70 keV. Visual inspection revealed that these images provide sufficient spatial resolution and soft-tissue contrast to characterise surfaces, disruption, calcification of cartilage, bone osteophytes, and bone cysts. Analysis of attenuation versus energy revealed different energy fingerprint according to tissues. The volumes and numbers of bone cyst were quantified.ConclusionsVirtual monoenergetic images may provide direct visualisation of both cartilage and bone details. Thus, unenhanced PCSCT appears to be a new modality for characterising the knee joint with the potential to increase the diagnostic capability of computed tomography for joint diseases and osteoarthritis.