Potential Benefits of Photon-Counting CT in Dental Imaging: A Narrative Review

Background/Objectives: Advancements in oral imaging technology are continually shaping the landscape of dental diagnosis and treatment planning. Among these, photon-counting computed tomography (PCCT), introduced in 2021, has emerged as a promising, high-quality oral technology. Dental imaging typically requires a resolution beyond the standard CT systems achievable with the specialized cone-beam CT. PCCT can offer up to 100 µm resolution, improve soft-tissue contrast, and provide faster scanning times, which are crucial for detailed dental diagnosis and treatment planning. Using semiconductor detectors, PCCT produces sharper images and can potentially reduce the number of scans required, thereby decreasing patient radiation exposure. This review aimed to explore the potential benefits of PCCT in dental imaging. Methods: This review analyzed the literature on PCCT in dental imaging from January 2010 to February 2024, sourced from PubMed, Scopus, and Web of Science databases, focusing on high-resolution, patient safety, and diagnostic efficiency in dental structure assessment. We included English-language articles, case studies, letters, observational studies, and randomized controlled trials while excluding duplicates and studies unrelated to PCCT's application in dental imaging. Results: Studies have highlighted the superiority of PCCT in reducing artifacts, which are often problematic, compared to conventional CBCT and traditional CT scans, due to metallic dental implants, particularly when used with virtual monoenergetic imaging and iterative metal artifact reduction, thereby improving implant imaging. This review acknowledges limitations, such as the potential for overlooking other advanced imaging technologies, a narrow study timeframe, the lack of real-world clinical application data in this field, and costs. Conclusions: PCCT represents a promising advancement in dental imaging, offering high-resolution visuals, enhanced contrast, and rapid scanning with reduced radiation exposure.

Ultra-low-dose photon-counting CT of paranasal sinus: an in vivo comparison of radiation dose and image quality to cone-beam CT

PURPOSE

This study investigated the differences in subjective and objective image parameters as well as dose exposure of photon-counting CT (PCCT) compared to cone-beam CT (CBCT) in paranasal sinus imaging for the assessment of rhinosinusitis and sinonasal anatomy.

METHODS

This single-centre retrospective study included 100 patients, who underwent either clinically indicated PCCT or CBCT of the paranasal sinus. Two blinded experienced ENT radiologists graded image quality and delineation of specific anatomical structures on a 5-point Likert scale. In addition, contrast-to-noise ratio (CNR) and applied radiation doses were compared among both techniques.

RESULTS

Image quality and delineation of bone structures in paranasal sinus PCCT was subjectively rated superior by both readers compared to CBCT (P < .001). CNR was significantly higher for photon-counting CT (P < .001). Mean effective dose for PCCT examinations was significantly lower than for CBCT (0.038 mSv ± 0.009 vs. 0.14 mSv ± 0.011; P < .001).

CONCLUSION

In a performance comparison of PCCT and a modern CBCT scanner in paranasal sinus imaging, we demonstrated that first-use PCCT in clinical routine provides higher subjective image quality accompanied by higher CNR at close to a quarter of the dose exposure compared to CBCT.

Synergizing photon-counting CT with deep learning: potential enhancements in medical imaging

This review article highlights the potential of integrating photon-counting computed tomography (CT) and deep learning algorithms in medical imaging to enhance diagnostic accuracy, improve image quality, and reduce radiation exposure. The use of photon-counting CT provides superior image quality, reduced radiation dose, and material decomposition capabilities, while deep learning algorithms excel in automating image analysis and improving diagnostic accuracy. The integration of these technologies can lead to enhanced material decomposition and classification, spectral image analysis, predictive modeling for individualized medicine, workflow optimization, and radiation dose management. However, data requirements, computational resources, and regulatory and ethical concerns remain challenges that need to be addressed to fully realize the potential of this technology. The fusion of photon-counting CT and deep learning algorithms is poised to revolutionize medical imaging and transform patient care.

Analysis of Epicardial Adipose Tissue Texture in Relation to Coronary Artery Calcification in PCCT: The EAT Signature!

(1) Background: Epicardial adipose tissue influences cardiac biology in physiological and pathological terms. As it is suspected to be linked to coronary artery calcification, identifying improved methods of diagnostics for these patients is important. The use of radiomics and the new Photon-Counting computed tomography (PCCT) may offer a feasible step toward improved diagnostics in these patients. (2) Methods: In this retrospective single-centre study epicardial adipose tissue was segmented manually on axial unenhanced images. Patients were divided into three groups, depending on the severity of coronary artery calcification. Features were extracted using pyradiomics. Mean and standard deviation were calculated with the Pearson correlation coefficient for feature correlation. Random Forest classification was applied for feature selection and ANOVA was performed for group comparison. (3) Results: A total of 53 patients (32 male, 21 female, mean age 57, range from 21 to 80 years) were enrolled in this study and scanned on the novel PCCT. "Original_glrlm_LongRunEmphasis", "original_glrlm_RunVariance", "original_glszm_HighGrayLevelZoneEmphasis", and "original_glszm_SizeZoneNonUniformity" were found to show significant differences between patients with coronary artery calcification (Agatston score 1-99/≥100) and those without. (4) Conclusions: Four texture features of epicardial adipose tissue are associated with coronary artery calcification and may reflect inflammatory reactions of epicardial adipose tissue, offering a potential imaging biomarker for atherosclerosis detection.

Imaging in Third Molar Surgery: A Clinical Update

Third molar surgery is one of the most common surgical procedures performed in oral and maxillofacial surgery. Considering the patient's young age and the often-elective nature of the procedure, a comprehensive preoperative evaluation of the surgical site, relying heavily on preoperative imaging, is key to providing accurate diagnostic work-up, evidence-based clinical decision making, and, when appropriate, indication-specific surgical planning. Given the rapid developments of dental imaging in the field, the aim of this article is to provide a comprehensive, up-to-date clinical overview of various imaging techniques related to perioperative imaging in third molar surgery, ranging from panoramic radiography to emerging technologies, such as photon-counting computed tomography and magnetic resonance imaging. Each modality's advantages, limitations, and recent improvements are evaluated, highlighting their role in treatment planning, complication prevention, and postoperative follow-ups. The integration of recent technological advances, including artificial intelligence and machine learning in biomedical imaging, coupled with a thorough preoperative clinical evaluation, marks another step towards personalized dentistry in high-risk third molar surgery. This approach enables minimally invasive surgical approaches while reducing inefficiencies and risks by incorporating additional imaging modality- and patient-specific parameters, potentially facilitating and improving patient management.

Leveraging 3D Bioprinting and Photon-Counting Computed Tomography to Enable Noninvasive Quantitative Tracking of Multifunctional Tissue Engineered Constructs

3D bioprinting is revolutionizing the fields of personalized and precision medicine by enabling the manufacturing of bioartificial implants that recapitulate the structural and functional characteristics of native tissues. However, the lack of quantitative and noninvasive techniques to longitudinally track the function of implants has hampered clinical applications of bioprinted scaffolds. In this study, multimaterial 3D bioprinting, engineered nanoparticles (NPs), and spectral photon-counting computed tomography (PCCT) technologies are integrated for the aim of developing a new precision medicine approach to custom-engineer scaffolds with traceability. Multiple CT-visible hydrogel-based bioinks, containing distinct molecular (iodine and gadolinium) and NP (iodine-loaded liposome, gold, methacrylated gold (AuMA), and Gd2 O3 ) contrast agents, are used to bioprint scaffolds with varying geometries at adequate fidelity levels. In vitro release studies, together with printing fidelity, mechanical, and biocompatibility tests identified AuMA and Gd2 O3 NPs as optimal reagents to track bioprinted constructs. Spectral PCCT imaging of scaffolds in vitro and subcutaneous implants in mice enabled noninvasive material discrimination and contrast agent quantification. Together, these results establish a novel theranostic platform with high precision, tunability, throughput, and reproducibility and open new prospects for a broad range of applications in the field of precision and personalized regenerative medicine.

Pericoronary radiomics texture features associated with hypercholesterolemia on a photon-counting-CT

Introduction

Pericoronary adipose tissue (PCAT) stands in complex bidirectional interaction with the surrounding arteries and is known to be connected to many cardiovascular diseases involving vascular inflammation. PCAT texture may be influenced by other cardiovascular risk factors such as hypercholesterolemia. The recently established photon-counting CT could improve texture analysis and help detect those changes by offering higher spatial resolution and signal-to-noise ratio.

Methods

In this retrospective, single-center, IRB-approved study, PCAT of the left and right coronary artery was manually segmented and radiomic features were extracted using pyradiomics. The study population consisted of a test collective and a validation collective. The collectives were each divided into two groups defined by the presence or absence of hypercholesterolemia, taken from self-reported conditions and confirmed by medical records. Mean and standard deviation were calculated with Pearson correlation coefficient for correlation of features and visualized as boxplots and heatmaps using R statistics. Random forest feature selection was performed to identify differentiating features between the two groups. 66 patients were enrolled in this study (34 female, mean age 58 years).

Results

Two radiomics features allowing differentiation between PCAT texture of the groups were identified (p-values between 0.013 and 0.24) and validated. Patients with hypercholesterolemia presented with a greater concentration of high-density values as indicated through analysis of specific texture features as "gldm_HighGrayLevelEmphasis" (23.95 vs. 22.99) and "glrlm_HighGrayLevelRunEmphasis" (24.21 vs. 23.31).

Discussion

Texture analysis of PCAT allowed differentiation between patients with and without hypercholesterolemia offering a potential imaging biomarker for this specific cardiovascular risk factor.

Dual-Source Photon-Counting Computed Tomography-Part III: Clinical Overview of Vascular Applications beyond Cardiac and Neuro Imaging

Photon-counting computed tomography (PCCT) is an emerging technology that is expected to radically change clinical CT imaging. PCCT offers several advantages over conventional CT, which can be combined to improve and expand the diagnostic possibilities of CT angiography. After a brief description of the PCCT technology and its main advantages we will discuss the new opportunities brought about by PCCT in the field of vascular imaging, while addressing promising future clinical scenarios.

Dual-Source Photon-Counting Computed Tomography-Part I: Clinical Overview of Cardiac CT and Coronary CT Angiography Applications

The photon-counting detector (PCD) is a new computed tomography detector technology (photon-counting computed tomography, PCCT) that provides substantial benefits for cardiac and coronary artery imaging. Compared with conventional CT, PCCT has multi-energy capability, increased spatial resolution and soft tissue contrast with near-null electronic noise, reduced radiation exposure, and optimization of the use of contrast agents. This new technology promises to overcome several limitations of traditional cardiac and coronary CT angiography (CCT/CCTA) including reduction in blooming artifacts in heavy calcified coronary plaques or beam-hardening artifacts in patients with coronary stents, and a more precise assessment of the degree of stenosis and plaque characteristic thanks to its better spatial resolution. Another potential application of PCCT is the use of a double-contrast agent to characterize myocardial tissue. In this current overview of the existing PCCT literature, we describe the strengths, limitations, recent applications, and promising developments of employing PCCT technology in CCT.

Dual Source Photon-Counting Computed Tomography-Part II: Clinical Overview of Neurovascular Applications

Photon-counting detector (PCD) is a novel computed tomography detector technology (photon-counting computed tomography-PCCT) that presents many advantages in the neurovascular field, such as increased spatial resolution, reduced radiation exposure, and optimization of the use of contrast agents and material decomposition. In this overview of the existing literature on PCCT, we describe the physical principles, the advantages and the disadvantages of conventional energy integrating detectors and PCDs, and finally, we discuss the applications of the PCD, focusing specifically on its implementation in the neurovascular field.

Development and Application of a Virtual Imaging Trial framework for Airway Quantifications via CT

Chronic obstructive pulmonary disease (COPD) is one of the top three causes of death worldwide, characterized by emphysema and bronchitis. Airway measurements reflect the severity of bronchitis and other airway-related diseases. Airway structures can be objectively evaluated with quantitative computed tomography (CT). The accuracy of such quantifications is limited by the spatial resolution and image noise characteristics of the imaging system and can be potentially improved with the emerging photon-counting CT (PCCT) technology. This study evaluated the quantitative performance of PCCT against energy-integrating CT (EICT) systems for airway measurements, and further identified optimum CT imaging parameters for such quantifications. The study was performed using a novel virtual imaging framework by developing the first library of virtual patients with bronchitis. These virtual patients were developed based on CT images of confirmed COPD patients with varied bronchitis severity. The human models were virtually imaged at 6.3 and 12.6 mGy dose levels using a scanner-specific simulator (DukeSim), synthesizing clinical PCCT and EICT scanners (NAEOTOM Alpha, FLASH, Siemens). The projections were reconstructed with two algorithms and kernels at different matrix sizes and slice thicknesses. The CT images were used to quantify clinically relevant airway measurements ("Pi10" and "WA%") and compared against their ground truth values. Compared to EICT, PCCT provided more accurate Pi10 and WA% measurements by 63.1% and 68.2%, respectively. For both technologies, sharper kernels and larger matrix sizes led to more reliable bronchitis quantifications. This study highlights the potential advantages of PCCT against EICT in characterizing bronchitis utilizing a virtual imaging platform.

Ultra-High-Resolution Coronary CT Angiography With Photon-Counting Detector CT: Feasibility and Image Characterization

OBJECTIVES

The aim of this study was to evaluate the feasibility and quality of ultra-high-resolution coronary computed tomography angiography (CCTA) with dual-source photon-counting detector CT (PCD-CT) in patients with a high coronary calcium load, including an analysis of the optimal reconstruction kernel and matrix size.

MATERIALS AND METHODS

In this institutional review board-approved study, 20 patients (6 women; mean age, 79 ± 10 years; mean body mass index, 25.6 ± 4.3 kg/m 2 ) undergoing PCD-CCTA in the ultra-high-resolution mode were included. Ultra-high-resolution CCTA was acquired in an electrocardiography-gated dual-source spiral mode at a tube voltage of 120 kV and collimation of 120 × 0.2 mm. The field of view (FOV) and matrix sizes were adjusted to the resolution properties of the individual reconstruction kernels using a FOV of 200 × 200 mm 2 or 150 × 150 mm 2 and a matrix size of 512 × 512 pixels or 1024 × 1024 pixels, respectively. Images were reconstructed using vascular kernels of 8 sharpness levels (Bv40, Bv44, Bv56, Bv60, Bv64, Bv72, Bv80, and Bv89), using quantum iterative reconstruction (QIR) at a strength level of 4, and a slice thickness of 0.2 mm. Images with the Bv40 kernel, QIR at a strength level of 4, and a slice thickness of 0.6 mm served as the reference. Image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), vessel sharpness, and blooming artifacts were quantified. For subjective image quality, 2 blinded readers evaluated image noise and delineation of coronary artery plaques and the adjacent vessel lumen using a 5-point discrete visual scale. A phantom scan served to characterize image noise texture by calculating the noise power spectrum for every reconstruction kernel.

RESULTS

Maximum spatial frequency (f peak ) gradually shifted to higher values for reconstructions with the Bv40 to Bv64 kernel (0.15 to 0.56 mm -1 ), but not for reconstructions with the Bv72 to Bv89 kernel. Ultra-high-resolution CCTA was feasible in all patients (median calcium score, 479). In patients, reconstructions with the Bv40 kernel and a slice thickness of 0.6 mm showed largest blooming artifacts (55.2% ± 9.8%) and lowest vessel sharpness (477.1 ± 73.6 ΔHU/mm) while achieving highest SNR (27.4 ± 5.6) and CNR (32.9 ± 6.6) and lowest noise (17.1 ± 2.2 HU). Considering reconstructions with a slice thickness of 0.2 mm, image noise, SNR, CNR, vessel sharpness, and blooming artifacts significantly differed across kernels (all P 's < 0.001). With higher kernel sharpness, SNR and CNR continuously decreased, whereas image noise and vessel sharpness increased, with highest sharpness for the Bv89 kernel (2383.4 ± 787.1 ΔHU/mm). Blooming artifacts continuously decreased for reconstructions with the Bv40 (slice thickness, 0.2 mm; 52.8% ± 9.2%) to the Bv72 kernel (39.7% ± 9.1%). Subjective noise was perceived by both readers in agreement with the objective measurements. Considering delineation of coronary artery plaques and the adjacent vessel lumen, reconstructions with the Bv64 and Bv72 kernel (for both, median score of 5) were favored by the readers providing an excellent anatomic delineation of plaque characteristics and vessel lumen.

CONCLUSIONS

Ultra-high-resolution CCTA with PCD-CT is feasible and enables the visualization of calcified coronaries with an excellent image quality, high sharpness, and reduced blooming. Coronary plaque characterization and delineation of the adjacent vessel lumen are possible with an optimal quality using Bv64 kernel, a FOV of 200 × 200 mm 2 , and a matrix size of 512 × 512 pixels.

New Composite Contrast Agents Based on Ln and Graphene Matrix for Multi-Energy Computed Tomography

The subject of the current research study is aimed at the development of novel types of contrast agents (CAs) for multi-energy computed tomography (CT) based on Ln-graphene composites, which include Ln (Ln = La, Nd, and Gd) nanoparticles with a size of 2-3 nm, acting as key contrasting elements, and graphene nanoflakes (GNFs) acting as the matrix. The synthesis and surface modifications of the GNFs and the properties of the new CAs are presented herein. The samples have had their characteristics determined using X-ray photoelectron spectroscopy, X-Ray diffraction, transmission electron microscopy, thermogravimetric analysis, and Raman spectroscopy. Multi-energy CT images of the La-, Nd-, and Gd-based CAs demonstrating their visualization and discriminative properties, as well as the possibility of a quantitative analysis, are presented.

Compton coincidence in silicon photon-counting CT detectors

Purpose: Compton interactions amount to a significant fraction of the registered counts in a silicon detector. In a Compton interaction, only a part of the photon energy is deposited and a single incident photon can result in multiple counts unless tungsten shielding is used. Deep silicon has proved to be a competitive material for photon-counting CT detectors, but to improve the performance further, one possibility is to use coincidence techniques to identify Compton-scattered photons and reconstruct their incident energies. Approach: In a detector with no tungsten shielding, incident photons can interact through a series of interactions. Based on the position and energy of each interaction, probability-based methods can be used to estimate the incident photon energy. Here, we present a maximum likelihood estimation framework along with an alternative method to estimate the incident photon energy and position in a silicon detector. Results: Assuming one incident photon per time frame, we show that the incident photon energy can be estimated with a mean error of - 0.07 ± 0.03    keV and an RMS error of 3.36 ± 0.02    keV for a realistic case in which we assume a detector with limited energy and spatial resolution. The interaction position was estimated with a mean error of - 2 ± 11    μ m in x direction and 7 ± 11    μ m in y direction. Corresponding RMS errors of 1.09 ± 0.01 and 1.10 ± 0.01    mm were achieved in x and y , respectively. Conclusions: The presented results show the potential of using probability-based methods to improve the performance of silicon detectors for CT.

Silicon photomultiplier-based scintillation detectors for photon-counting CT: A feasibility study

PURPOSE

The implementation of photon-counting detectors is widely expected to be the next breakthrough in X-ray computed tomography (CT) instrumentation. A small number of prototype scanners equipped with direct-conversion detectors based on room-temperature semiconductors, such as CdTe and CdZnTe (CZT), are currently installed at medical centers. Here, we investigate the feasibility of using silicon photomultiplier (SiPM)-based scintillation detectors in photon-counting computed tomography (PCCT) scanners, as a potential alternative to CdTe and CZT detectors.

METHODS

We introduce a model that allows us to compute the expected energy resolution as well as the expected pulse shape and associated rate capability of SiPM-based PCCT detectors. The model takes into account SiPM saturation and optical crosstalk, because these phenomena may substantially affect the performance of SiPM-based PCCT detectors with sub-mm pixels. We present model validation experiments using a single-pixel detector consisting of a 0.9 × 0.9 × 1.0 mm³ LuAP:Ce scintillation crystal coupled to a 1 × 1 mm² SiPM. We subsequently use the validated model to compute the expected performance of the fast scintillators LYSO:Ce, LuAP:Ce, and LaBr₃ :Ce, coupled to currently available SiPMs, as well as to a more advanced SiPM prototype with improved dynamic range, for sub-mm pixel sizes.

RESULTS

The model was found to be in good agreement with the validation experiments, both with respect to energy resolution and pulse shape. It shows how saturation progressively degrades the energy resolution of detectors equipped with currently available SiPMs as the pixel size decreases. Moreover, the expected pulse duration is relatively long (~200 ns) with these SiPMs. However, when LuAP:Ce and LaBr₃ :Ce are coupled to the more advanced SiPM prototype, the pulse duration improves to less than 60 ns, which is in the same order of magnitude as pulses from CdTe and CZT detectors. It follows that sufficient rate capability can be achieved with pixel sizes of 400 μm or smaller. Moreover, LaBr₃ :Ce detectors can provide an energy resolution of 11.5%-13.5% at 60 keV, comparable to CdTe and CZT detectors.

CONCLUSIONS

This work provides first evidence that it may be feasible to develop SiPM-based scintillation detectors for PCCT that can compete with CdTe and CZT detectors in terms of energy resolution and rate capability.

Development of a scanner-specific simulation framework for photon-counting computed tomography

The aim of this study was to develop and validate a simulation platform that generates photon-counting CT images of voxelized phantoms with detailed modeling of manufacturer-specific components including the geometry and physics of the x-ray source, source filtrations, anti-scatter grids, and photon-counting detectors. The simulator generates projection images accounting for both primary and scattered photons using a computational phantom, scanner configuration, and imaging settings. Beam hardening artifacts are corrected using a spectrum and threshold dependent water correction algorithm. Physical and computational versions of a clinical phantom (ACR) were used for validation purposes. The physical phantom was imaged using a research prototype photon-counting CT (Siemens Healthcare) with standard (macro) mode, at four dose levels and with two energy thresholds. The computational phantom was imaged with the developed simulator with the same parameters and settings used in the actual acquisition. Images from both the real and simulated acquisitions were reconstructed using a reconstruction software (FreeCT). Primary image quality metrics such as noise magnitude, noise ratio, noise correlation coefficients, noise power spectrum, CT number, in-plane modulation transfer function, and slice sensitivity profiles were extracted from both real and simulated data and compared. The simulator was further evaluated for imaging contrast materials (bismuth, iodine, and gadolinium) at three concentration levels and six energy thresholds. Qualitatively, the simulated images showed similar appearance to the real ones. Quantitatively, the average relative error in image quality measurements were all less than 4% across all the measurements. The developed simulator will enable systematic optimization and evaluation of the emerging photon-counting computed tomography technology.

Measuring arterial wall perfusion using photon-counting computed tomography (CT): improving CT number accuracy of artery wall using image deconvolution

Changes in arterial wall perfusion mark the onset of atherosclerosis. A characteristic change is the increased spatial density of vasa vasorum (VV), the microvessels in the arterial walls. Measuring this increased VV (IVV) density using contrast-enhanced computed tomography (CT) has had limited success due to blooming effects from contrast media. If the system point-spread function (PSF) is known, then the blooming effect can be modeled as a convolution between the true signal and the PSF. We report the application of image deconvolution to improve the CT number accuracy in the arterial wall of a phantom and in a porcine model of IVV density, both scanned using a whole-body research photon-counting CT scanner. A 3D-printed carotid phantom filled with three concentrations of iodinated contrast material was scanned to assess blooming and its effect on wall CT number accuracy. The results showed a reduction in blooming effects following image deconvolution, and, consequently, a better delineation between lumen and wall was achieved. Results from the animal experiment showed improved CT number difference between the carotid with IVV density and the normal carotid artery after deconvolution, enabling the detection of VV proliferation, which may serve as an early indicator of atherosclerosis.

Spectral response model for a multibin photon-counting spectral computed tomography detector and its applications

Variations among detector channels in computed tomography can lead to ring artifacts in the reconstructed images and biased estimates in projection-based material decomposition. Typically, the ring artifacts are corrected by compensation methods based on flat fielding, where transmission measurements are required for a number of material-thickness combinations. Phantoms used in these methods can be rather complex and require an extensive number of transmission measurements. Moreover, material decomposition needs knowledge of the individual response of each detector channel to account for the detector inhomogeneities. For this purpose, we have developed a spectral response model that binwise predicts the response of a multibin photon-counting detector individually for each detector channel. The spectral response model is performed in two steps. The first step employs a forward model to predict the expected numbers of photon counts, taking into account parameters such as the incident x-ray spectrum, absorption efficiency, and energy response of the detector. The second step utilizes a limited number of transmission measurements with a set of flat slabs of two absorber materials to fine-tune the model predictions, resulting in a good correspondence with the physical measurements. To verify the response model, we apply the model in two cases. First, the model is used in combination with a compensation method which requires an extensive number of transmission measurements to determine the necessary parameters. Our spectral response model successfully replaces these measurements by simulations, saving a significant amount of measurement time. Second, the spectral response model is used as the basis of the maximum likelihood approach for projection-based material decomposition. The reconstructed basis images show a good separation between the calcium-like material and the contrast agents, iodine and gadolinium. The contrast agent concentrations are reconstructed with more than 94% accuracy.