BACKGROUND: Current photon-counting computed tomography (CT) systems utilize semiconductor detectors, such as cadmium telluride (CdTe), cadmium zinc telluride (CZT), and silicon (Si), which convert x-ray photons directly into charge pulses. An alternative approach is indirect detection, which involves Yttrium Orthosilicate (YSO) scintillators coupled with silicon photomultipliers (SiPMs). This presents an attractive and cost-effective option due to its low cost, high detection efficiency, low dark count rate, and high sensor gain.
OBJECTIVE: This study aims to establish a comprehensive quantitative imaging framework for three-energy-bin proof-of-concept photon-counting CT based on YSO/SiPM detectors developed in our group using multi-voltage threshold (MVT) digitizers and assess the feasibility of this spectral CT for material identification.
METHODS: We developed a proof-of-concept YSO/SiPM-based benchtop spectral CT system and established a pipeline for three-energy-bin photon-counting CT projection-domain processing. The empirical A-table method was employed for basis material decomposition, and the quantitative imaging performance of the spectral CT system was assessed. This evaluation included the synthesis errors of virtual monoenergetic images, electron density images, effective atomic number images, and linear attenuation coefficient curves. The validity of employing A-table methods for material identification in three-energy-bin spectral CT was confirmed through both simulations and experimental studies.
RESULTS: In both noise-free and noisy simulations, the thickness estimation experiments and quantitative imaging results demonstrated high accuracy. In the thickness estimation experiment using the practical spectral CT system, the mean absolute error for the estimated thickness of the decomposed Al basis material was 0.014 ± 0.010 mm, with a mean relative error of 0.66% ± 0.42%. Similarly, for the decomposed polymethyl methacrylate (PMMA) basis material, the mean absolute error in thickness estimation was 0.064 ± 0.058 mm, with a mean relative error of 0.70% ± 0.38%. Additionally, employing the equivalent thickness of the basis material allowed for accurate synthesis of 70 keV virtual monoenergetic images (relative error 1.85% ± 1.26%), electron density (relative error 1.81% ± 0.97%), and effective atomic number (relative error 2.64% ± 1.26%) of the tested materials. In addition, the average synthesis error of the linear attenuation coefficient curves in the energy range from 40 to 150 keV was 1.89% ± 1.07%.
CONCLUSIONS: Both simulation and experimental results demonstrate the accurate generation of 70 keV virtual monoenergetic images, electron density, and effective atomic number images using the A-table method. Quantitative imaging results indicate that the YSO/SiPM-based photon-counting detector is capable of accurately reconstructing virtual monoenergetic images, electron density images, effective atomic number images, and linear attenuation coefficient curves, thereby achieving precise material identification.

Objective.Sparse-view dual-energy spectral computed tomography (DECT) imaging is a challenging inverse problem. Due to the incompleteness of the collected data, the presence of streak artifacts can result in the degradation of reconstructed spectral images. The subsequent material decomposition task in DECT can further lead to the amplification of artifacts and noise.Approach.To address this problem, we propose a novel one-step inverse generation network (OIGN) for sparse-view dual-energy CT imaging, which can achieve simultaneous imaging of spectral images and materials. The entire OIGN consists of five sub-networks that form four modules, including the pre-reconstruction module, the pre-decomposition module, and the following residual filtering module and residual decomposition module. The residual feedback mechanism is introduced to synchronize the optimization of spectral CT images and materials.Main results.Numerical simulation experiments show that the OIGN has better performance on both reconstruction and material decomposition than other state-of-the-art spectral CT imaging algorithms. OIGN also demonstrates high imaging efficiency by completing two high-quality imaging tasks in just 50 seconds. Additionally, anti-noise testing is conducted to evaluate the robustness of OIGN.Significance.These findings have great potential in high-quality multi-task spectral CT imaging in clinical diagnosis.

Purpose. Fast kV-switching (FKS) and dual-layer flat-panel detector (DL-FPD) technologies have been actively studied as promising dual-energy spectral imaging solutions for FPD-based cone-beam computed tomography (CT). However, cone-beam CT (CBCT) spectral imaging is known to face challenges in obtaining accurate and robust material discrimination performance. That is because the energy separation by either FKS or DL-FPD, alone, is still limited, along with apparently unpaired signal levels in the effective low- and high-energy projections in real applications, not to mention the x-ray scatter in cone-beam scan which will make the material decomposition almost impossible if no correction is applied. To further improve CBCT spectral imaging capability, this work aims to promote a source-detector joint multi-energy spectral imaging solution which takes advantages of both FKS and DL-FPD, and to conduct a feasibility study on the first tabletop CBCT system with the joint spectral imaging capability developed.Methods. For CBCT, development of multi-energy spectral imaging can be jointly realized by using an x-ray source with a generator whose kilo-voltages can alternate in tens of Hertz (i.e. FKS), and a DL-FPD whose top- and bottom-layer projections corresponds to different effective energy levels. Thanks to the complimentary characteristics inherent in FKS and DL-FPD, the overall energy separation will be significantly better when compared with FKS or DL-FPD alone, and the x-ray photon detection efficiency will be also improved when compared with FKS alone. In this work, a noise performance analysis using the Cramér-Rao lower bound (CRLB) method is conducted. The CRLB for basis material after a projection-domain material decomposition is derived, followed by a set of numerical calculations of CRLBs, for the FKS, the DL-FPD and the joint solution, respectively. To compensate for the slightly angular mismatch between low- and high- projections in FKS, a dual-domain projection completion scheme is implemented. Afterwards material decomposition from the complete projection data is carried out by using the maximum-likelihood method, followed by reconstruction of basis material and virtual monochromatic images (VMI). In this work, the first FKS and DL-FPD jointly enabled multi-energy tabletop CBCT system, to the best of our knowledge, has been developed in our laboratory. To evaluate its spectral imaging performance, a set of physics experiments are conducted, where the multi-energy and head phantoms are scanned using the 80/105/130 kVp switching pairs and projection data are collected using a prototype DL-FPD, whose both top and bottom layer of panels are composed of 550μm of cesium iodine (CsI) scintillators with no intermediate metal filter in-between.Results. The numerical simulations show that the joint spectral imaging solution can lead to a significant improvement in energy separation and lower noise levels in most of material decomposition cases. The physics experiments confirmed the feasibility and superiority of the joint spectral imaging, whose CNRs in the selected regions of interest of the multi-energy phantom were boosted by an average improvement of 21.9%, 20.4% for water basis images and 32.8%, 62.8% for iodine images when compared with that of the FKS and DL-FPD, respectively. For the head phantom case, the joint spectral imaging can effectively reduce the streaking artifacts as well, and the standard deviation in the selected regions of interest are reduced by an average decrement of 19.5% and 8.1% for VMI when compared with that of the FKS and DL-FPD, respectively.Conclusions. A feasibility study of the joint spectral imaging solution for CBCT by utilizing both the FKS and DL-FPD was conducted, with the first tabletop CBCT system having such a capability being developed, which exhibits improved CNR and is more effective in avoiding streaking artifacts as expected.

BACKGROUND: Cone-beam CT (CBCT) has been extensively employed in industrial and medical applications, such as image-guided radiotherapy and diagnostic imaging, with a growing demand for quantitative imaging using CBCT. However, conventional CBCT can be easily compromised by scatter and beam hardening artifacts, and the entanglement of scatter and spectral effects introduces additional complexity.
OBJECTIVE: The intertwined scatter and spectral effects within CBCT pose significant challenges to the quantitative performance of spectral imaging. In this work, we present the first attempt to develop a stationary spectral modulator with flying focal spot (SMFFS) technology as a promising, low-cost approach to accurately solving the x-ray scattering problem and physically enabling spectral imaging in a unified framework, and with no significant misalignment in data sampling of spectral projections.
METHODS: To deal with the intertwined scatter-spectral challenge, we propose a novel scatter-decoupled material decomposition (SDMD) method for SMFFS, which consists of four steps in total, including (1) spatial resolution-preserved and noise-suppressed multi-energy \"residual\" projection generation free from scatter, based on a hypothesis of scatter similarity; (2) first-pass material decomposition from the generated multi-energy residual projections in non-penumbra regions, with a structure similarity constraint to overcome the increased noise and penumbra effect; (3) scatter estimation for complete data; and (4) second-pass material decomposition for complete data by using a multi-material spectral correction method. Monte Carlo simulations of a pure-water cylinder phantom with different focal spot deflections are conducted to validate the scatter similarity hypothesis. Both numerical simulations using a clinical abdominal CT dataset, and physics experiments on a tabletop CBCT system using a Gammex multi-energy CT phantom and an anthropomorphic chest phantom, are carried out to demonstrate the feasibility of CBCT spectral imaging with SMFFS and our proposed SDMD method.
RESULTS: Monte Carlo simulations show that focal spot deflections within a range of 2 mm share quite similar scatter distributions overall. Numerical simulations demonstrate that SMFFS with SDMD method can achieve better material decomposition and CT number accuracy with fewer artifacts. In physics experiments, for the Gammex phantom, the average error of the mean values ( E RMSE ROI $E^{\\text{ROI}}_{\\text{RMSE}}$ ) in selected regions of interest (ROIs) of virtual monochromatic image (VMI) at 70 keV is 8 HU in SMFFS cone-beam (CB) scan, and 19 and 210 HU in sequential 80/120 kVp (dual kVp, DKV) CB scan with and without scatter correction, respectively. For the chest phantom, the E RMSE ROI $E^{\\text{ROI}}_{\\text{RMSE}}$ in selected ROIs of VMIs is 12 HU for SMFFS CB scan, and 15 and 438 HU for sequential 80/140 kVp CB scan with and without scatter correction, respectively. Also, the non-uniformity among selected regions of the chest phantom is 14 HU for SMFFS CB scan, and 59 and 184 HU for the DKV CB scan with and without a traditional scatter correction method, respectively.
CONCLUSIONS: We propose a SDMD method for CBCT with SMFFS. Our preliminary results show that SMFFS can enable spectral imaging with simultaneous scatter correction for CBCT and effectively improve its quantitative imaging performance.

UNASSIGNED: Slow kVp switching technique is an important approach to realize dual-energy CT (DECT) imaging, but its performance has not been thoroughly investigated yet.
UNASSIGNED: This study aims at comparing and evaluating the DECT imaging performance of different slow kVp switching protocols, and thus helps determining the optimal system settings.
UNASSIGNED: To investigate the impact of energy separation, two different beam filtration schemes are compared: the stationary beam filtration and dynamic beam filtration. Moreover, uniform tube voltage modulation and weighted tube voltage modulation are compared along with various modulation frequencies. A model-based direct decomposition algorithm is employed to generate the water and iodine material bases. Both numerical and physical experiments are conducted to verify the slow kVp switching DECT imaging performance.
UNASSIGNED: Numerical and experimental results demonstrate that the material decomposition is less sensitive to beam filtration, voltage modulation type and modulation frequency. As a result, robust material-specific quantitative decomposition can be achieved in slow kVp switching DECT imaging.
UNASSIGNED: Quantitative DECT imaging can be implemented with slow kVp switching under a variety of system settings.

The accurate screening of osteoporosis is important for identifying persons at risk. The diagnosis of bone conditions using dual X-ray absorptiometry is limited to extracting areal bone mineral density (BMD) and fails to provide any structural information. Computed tomography (CT) is excellent for morphological imaging but not ideal for material quantification. Advanced photon-counting detector CT (PCD-CT) possesses high spectral sensitivity and material decomposition capabilities to simultaneously determine qualitative and quantitative information. In this study, we explored the diagnostic utility of PCD-CT to provide high-resolution 3-D imaging of bone microarchitecture and composition for the sensitive diagnosis of bone in untreated and ovariectomized rats. PCD-CT accurately decomposed the calcium content within hydroxyapatite phantoms (r = 0.99). MicroCT analysis of tibial bone revealed significant differences in the morphological parameters between the untreated and ovariectomized samples. However, differences in the structural parameters of the mandible between the treatment groups were not observed. BMD determined with microCT and calcium concentration decomposed using PCD-CT differed significantly between the treatment groups in both the tibia and mandible. Quantitative analysis with PCD-CT is sensitive in determining the distribution of calcium and water components in bone and may have utility in the screening and diagnosis of bone conditions such as osteoporosis.

BACKGROUND: Flat panel detector (FPD) based cone-beam computed tomography (CT) has made tremendous progress in the last two decades, with many new and advanced medical and industrial applications keeping emerging from diagnostic imaging and image guidance for radiotherapy and interventional surgery. The current cone-beam CT (CBCT), however, is still suboptimal for head CT scan which requires a high standard of image quality. While the dual-layer FPD technology is under extensive development and is promising to further advance CBCT from qualitative anatomic imaging to quantitative dual-energy CT, its potential of enabling head CBCT applications has not yet been fully investigated.
OBJECTIVE: The relatively moderate energy separation from the dual-layer FPD and the overall low signal level especially at the bottom-layer detector, could raise significant challenges in performing high-quality dual-energy material decomposition (MD). In this work, we propose a hybrid, physics and model guided, MD algorithm that attempts to fully use the detected x-ray signals and prior-knowledge behind head CBCT using dual-layer FPD.
METHODS: Firstly, a regular projection-domain MD is performed as initial results of our approach and for comparison as conventional method. Secondly, based on the combined projection, a dual-layer multi-material spectral correction (dMMSC) is applied to generate beam hardening free images. Thirdly, the dMMSC corrected projections are adopted as a physics-model based guidance to generate a hybrid MD. A set of physics experiments including fan-beam scan and cone-beam scan using a head phantom and a Gammex Multi-Energy CT phantom are conducted to validate our proposed approach.
RESULTS: The combined reconstruction could reduce noise by about 10% with no visible resolution degradation. The fan-beam studies on the Gammex phantom demonstrated an improved MD performance, with the averaged iodine quantification error for the 5-15 mg/ml iodine inserts reduced from about 5.6% to 3.0% by the hybrid method. On fan-beam scan of the head phantom, our proposed hybrid MD could significantly reduce the streak artifacts, with CT number nonuniformity (NU) in the selected regions of interest (ROIs) reduced from 23 Hounsfield Units (HU) to 4.2 HU, and the corresponding noise suppressed from 31 to 6.5 HU. For cone-beam scan, after scatter correction (SC) and cone-beam artifact reduction (CBAR), our approach can also significantly improve image quality, with CT number NU in the selected ROI reduced from 24.2 to 6.6 HU and the noise level suppressed from 22.1 to 8.2 HU.
CONCLUSIONS: Our proposed physics and model guided hybrid MD for dual-layer FPD based head CBCT can significantly improve the robustness of MD and suppress the low-signal artifact. This preliminary feasibility study also demonstrated that the dual-layer FPD is promising to enable head CBCT spectral imaging.

BACKGROUND: The effective atomic number (Zeff ) is widely applied to the identification of unknown materials. One method to determine the Zeff is material-decomposition-based spectral X-ray imaging. The method relies on certain approximations of the X-ray interaction cross-sections such as empirical model coefficients. The impact of such approximations on the accuracy of Zeff quantification has not been fully investigated.
OBJECTIVE: To perform an error analysis of the material-decomposition-based Zeff quantification method and propose a coefficient calibration-in-groups method to improve the modeling accuracy and reduce the Zeff quantification error.
METHODS: The model of the material-decomposition-based Zeff quantification method relies on the dependence of the interaction cross-sections  (σPE ) on the atomic number Z and corresponding coefficient, that is, σ PE ∝ Z m $\\sigma _\\mathrm{PE}\\propto Z^m$ . In this work, all the data is from the National Institute of Standards and Technology (NIST) website. First, the coefficient m is calibrated through a logarithmic fitting method to quickly determine the m values for any certain energy and Zeff ranges. Then materials including elements and common compounds with Zeff ranging from 6-20 are selected as the objects whose effective atomic numbers are to be quantified. Different combinations of basis materials are applied to decompose the object materials and their quantification errors are analyzed. With the help of error analysis, the object materials are divided into high-error and low-error groups based on the decomposition coefficient ratio a m i n / a m a x $a_{min}/a_{max}$ , which is found to have a strong correlation with error, and their coefficients are calibrated in groups. Finally, the average errors of three m selection strategies: (1) using an empirical m value of 3.94, which is also considered a standard method; (2) using a single m value, which is calibrated through the logarithmic fitting method; (3) using different m values calibrated in groups, are calculated to test the effectiveness of our method.
RESULTS: The approximation of the X-ray interaction cross-section leads to certain errors in Zeff quantification and the error distributions for different basis materials are different. The average errors for most basis material combinations (C(6)/Ca(20), C(6)/Al(13), Al(13)/Ca(20), C(6)/Ne(10), Na(11)/P(15)) are lower than 0.5, maintaining good average accuracy. While the average error for S(16)/Ca(20) is up to 0.8461, leading to more misjudgments on atomic number. Meanwhile, the error distribution regularity can be observed. The Pearson\'s correlation coefficients of absolute errors and decomposition coefficient ratios are 0.743, 0.8432 and 0.7126 for basis material combinations C(6)/Ca(20), C(6)/Al(13) and Al(13)/Ca(20), indicating a good correlation. The method using either empirical m value of 3.94 or single calibrated m value of 4.619 has relatively high average errors. The proposed method using different m values calibrated in groups has the lowest average errors 0.254, 0.203 and 0.169, which are reduced by 21.6%(0.07), 3.8%(0.008) and 62.9%(0.286) respectively compared with the standard method.
CONCLUSIONS: The error analysis demonstrates that the approximation of X-ray interaction cross-sections leads to inevitable errors, while also revealing certain error distribution regularity. The coefficient calibrated-in-groups method has better modeling accuracy and has effectively reduced the error compared with the standard method using a single empirical m value of 3.94.

Photon counting spectral CT is a significant direction in the development of CT technology and material identification is an important application of spectral CT. However, spectrum estimation in photon counting spectral CT is highly complex and may affect quantification accuracy of material identification.
To address the problem of energy spectrum estimation in photon-counting spectral CT, this study investigates empirical material decomposition algorithms to achieve accurate quantitative decomposition of the effective atomic number.
The spectrum is first calibrated using the empirical dual-energy calibration (EDEC) method and the effective atomic number is then quantitatively estimated based on the EDEC method. The accuracy of estimating the effective atomic number of materials under different calibration conditions is investigated by designing different calibration phantoms, and accurate quantitation is achieved using suitable calibration settings. Last, the validity of this method is verified through simulations and experimental studies.
The results demonstrate that the error in estimating the effective atomic number is reduced to within 4% for low and medium Z materials, thereby enabling accurate material identification.
The empirical dual-energy correction method can solve the problem of energy spectrum estimation in photon counting spectral CT. Accurate effective atomic number estimation can be achieved with suitable calibration.

OBJECTIVE: The purpose of this study was to evaluate the diagnostic accuracy when using various base material pairs (BMPs) in dual-energy computed tomography (DECT), and to establish corresponding diagnostic standards for assessing bone status through comparison with quantitative computed tomography (QCT).
METHODS: This prospective study enrolled a total of 469 patients who underwent both non-enhanced chest CT scans under conventional kVp and abdominal DECT. The bone densities of hydroxyapatite (water), hydroxyapatite (fat), hydroxyapatite (blood), calcium (water), and calcium (fat) (DHAP (water), DHAP (fat), DHAP (blood), DCa (water), and DCa (fat)) in the trabecular bone of vertebral bodies (T11-L1) were measured, along with bone mineral density (BMD) via QCT. Intraclass correlation coefficient (ICC) analysis was used to assess the agreement of the measurements. Spearman\'s correlation test was performed to analyze the relationship between the DECT- and QCT-derived BMD. Receiver operator characteristic (ROC) curves were generated to determine the optimal diagnostic thresholds of various BMPs for diagnosing osteopenia and osteoporosis.
RESULTS: A total of 1371 vertebral bodies were measured, and QCT identified 393 with osteoporosis and 442 with osteopenia. Strong correlations were observed between DHAP (water), DHAP (fat), DHAP (blood), DCa (water), and DCa (fat) and the QCT-derived BMD. DHAP (water) showed the best predictive capability for osteopenia and osteoporosis. The area under the ROC curve, sensitivity, and specificity for identifying osteopenia were 0.956, 86.88%, and 88.91% with DHAP (water) ≤ 107.4 mg/cm3, respectively. The corresponding values for identifying osteoporosis were 0.999, 99.24%, and 99.53% with DHAP (water) ≤ 89.62 mg/cm3, respectively.
CONCLUSIONS: Bone density measurement using various BMPs in DECT enables the quantification of vertebral BMD and the diagnosis of osteoporosis, with DHAP (water) having the highest diagnostic accuracy.