The ability of articular cartilage to withstand significant mechanical stresses during activities, such as walking or running, relies on its distinctive structure. Integrating detailed tissue properties into subject-specific biomechanical models is challenging due to the complexity of analyzing these characteristics. This limitation compromises the accuracy of models in replicating cartilage function and impacts predictive capabilities. To address this, methods revealing cartilage function at the constituent-specific level are essential. In this study, we demonstrated that computational modeling derived individual constituent-specific biomechanical properties could be predicted by a novel nanoparticle contrast-enhanced computer tomography (CECT) method. We imaged articular cartilage samples collected from the equine stifle joint (n = 60) using contrast-enhanced micro-computed tomography (µCECT) to determine contrast agents\' intake within the samples, and compared those to cartilage functional properties, derived from a fibril-reinforced poroelastic finite element model. Two distinct imaging techniques were investigated: conventional energy-integrating µCECT employing a cationic tantalum oxide nanoparticle (Ta2O5-cNP) contrast agent and novel photon-counting µCECT utilizing a dual-contrast agent, comprising Ta2O5-cNP and neutral iodixanol. The results demonstrate the capacity to evaluate fibrillar and non-fibrillar functionality of cartilage, along with permeability-affected fluid flow in cartilage. This finding indicates the feasibility of incorporating these specific functional properties into biomechanical computational models, holding potential for personalized approaches to cartilage diagnostics and treatment.

OBJECTIVE: To assess the impact of different quantum iterative reconstruction (QIR) levels on objective and subjective image quality of ultra-high resolution (UHR) coronary CT angiography (CCTA) images and to determine the effect of strength levels on stenosis quantification using photon-counting detector (PCD)-CT.
METHODS: A dynamic vessel phantom containing two calcified lesions (25 % and 50 % stenosis) was scanned at heart rates of 60, 80 and 100 beats per minute with a PCD-CT system. In vivo CCTA examinations were performed in 102 patients. All scans were acquired in UHR mode (slice thickness0.2 mm) and reconstructed with four different QIR levels (1-4) using a sharp vascular kernel (Bv64). Image noise, signal-to-noise ratio (SNR), sharpness, and percent diameter stenosis (PDS) were quantified in the phantom, while noise, SNR, contrast-to-noise ratio (CNR), sharpness, and subjective quality metrics (noise, sharpness, overall image quality) were assessed in patient scans.
RESULTS: Increasing QIR levels resulted in significantly lower objective image noise (in vitro and in vivo: both p < 0.001), higher SNR (both p < 0.001) and CNR (both p < 0.001). Sharpness and PDS values did not differ significantly among QIRs (all pairwise p > 0.008). Subjective noise of in vivo images significantly decreased with increasing QIR levels, resulting in significantly higher image quality scores at increasing QIR levels (all pairwise p < 0.001). Qualitative sharpness, on the other hand, did not differ across different levels of QIR (p = 0.15).
CONCLUSIONS: The QIR algorithm may enhance the image quality of CCTA datasets without compromising image sharpness or accurate stenosis measurements, with the most prominent benefits at the highest strength level.

OBJECTIVE: To evaluate the feasibility of using photon-counting detector computed tomography (PCD CT) to simultaneously quantify fat and iron content MATERIALS AND METHODS: Phantoms with pure fat, pure iron and fat-iron deposition were scanned by two tube voltages (120 and 140 kV) and two image quality (IQ) settings (80 and 145). Using an iron-specific three-material decomposition algorithm, virtual noniron (VNI) and virtual iron content (VIC) images were generated at quantum iterative reconstruction (QIR) strength levels 1-4.
RESULTS: Significant linear correlations were observed between known fat content (FC) and VNI for pure fat phantoms (r = 0.981-0.999, p < 0.001) and between known iron content (IC) and VIC for pure iron phantoms (r = 0.897-0.975, p < 0.001). In fat-iron phantoms, the measurement for fat content of 5-30% demonstrated good linearity between FC and VNI (r = 0.919-0.990, p < 0.001), and VNI were not affected by 75, 150, and 225 µmol/g iron overload (p = 0.174-0.519). The measurement for iron demonstrated a linear range of 75-225 µmol/g between IC and VIC (r = 0.961-0.994, p < 0.001) and VIC was not confounded by the coexisting 5%, 20%, and 30% fat deposition (p = 0.943-0.999). The Bland-Altman of fat and iron measurements were not significantly different at varying tube voltages and IQ settings (all p > 0.05). No significant difference in VNI and VIC at QIR 1-4.
CONCLUSIONS: PCD CT can accurately and simultaneously quantify fat and iron, including scan parameters with lower radiation dose.

The aim of this phantom study was to assess the detectability and volumetric accuracy of pulmonary nodules on photon-counting detector CT (PCD-CT) at different low-dose levels compared to conventional energy-integrating detector CT (EID-CT). In-house fabricated artificial nodules of different shapes (spherical, lobulated, spiculated), sizes (2.5-10 mm and 5-1222 mm3), and densities (-330 HU and 100 HU) were randomly inserted into an anthropomorphic thorax phantom. The phantom was scanned with a low-dose chest protocol with PCD-CT and EID-CT, in which the dose with PCD-CT was lowered from 100% to 10% with respect to the EID-CT reference dose. Two blinded observers independently assessed the CT examinations of the nodules. A third observer measured the nodule volumes using commercial software. The influence of the scanner type, dose, observer, physical nodule volume, shape, and density on the detectability and volumetric accuracy was assessed by a multivariable regression analysis. In 120 CT examinations, 642 nodules were present. Observer 1 and 2 detected 367 (57%) and 289 nodules (45%), respectively. With PCD-CT and EID-CT, the nodule detectability was similar. The physical nodule volumes were underestimated by 20% (range 8-52%) with PCD-CT and 24% (range 9-52%) with EID-CT. With PCD-CT, no significant decrease in the detectability and volumetric accuracy was found at dose reductions down to 10% of the reference dose (p > 0.05). The detectability and volumetric accuracy were significantly influenced by the observer, nodule volume, and a spiculated nodule shape (p < 0.05), but not by dose, CT scanner type, and nodule density (p > 0.05). Low-dose PCD-CT demonstrates potential to detect and assess the volumes of pulmonary nodules, even with a radiation dose reduction of up to 90%.

Objective. The purpose of this study is to assess its human images and its unique capabilities such as the \'on demand\' higher spatial resolution and multi-spectral imaging of photon-counting-detector (PCD)-CT.Approach. In this study, the FDA 510(k) cleared mobile PCD-CT (OmniTom Elite) was used. To this end, we imaged internationally certified CT phantoms and a human cadaver head to evaluate the feasibility of high resolution (HR) and multi-energy imaging. We also demonstrate the performance of PCD-CT via first-in-human imaging by scanning three human volunteers.Main results. At the 5 mm slice thickness, routinely used in diagnostic head CT, the first human PCD-CT images were diagnostically equivalent to the EID-CT scanner. The HR acquisition mode of PCD-CT achieved a resolution of 11 line-pairs (lp)/cm as compared to 7 lp cm-1using the same kernel (posterior fossa-kernel) in the standard acquisition mode of EID-CT. For the quantitative multi-energy CT performance, the measured CT numbers in virtual mono-energetic images (VMI) of iodine inserts in the Gammex Multi-Energy CT phantom (model 1492, Sun Nuclear Corporation, USA) matched the manufacturer reference values with mean percent error of 3.25%. Multi-energy decomposition with PCD-CT demonstrated the separation and quantification of iodine, calcium, and water.Significance. PCD-CT can achieve multi-resolution acquisition modes without physically changing the CT detector. It can provide superior spatial resolution compared with the standard acquisition mode the conventional mobile EID-CT. Quantitative spectral capability of PCD-CT can provide accurate, simultaneous multi-energy images for material decomposition and VMI generation using a single exposure.

OBJECTIVE: A tetrahedron beam (TB) X-ray system with a linear X-ray source array and a linear detector array positioned orthogonal to each other may overcome the X-ray scattering problem of traditional cone-beam X-ray systems. We developed a TB imaging benchtop system using a linear array X-ray source to demonstrate the principle and benefits of TB imaging.
METHODS: A multi-pixel thermionic emission X-ray (MPTEX) source with 48 focal spots in 4-mm spacing was developed in-house. The X-ray beams are collimated to a stack of fan beams that are converged to a 6-mm wide multi-row photon-counting detector (PCD). The data collected with a sequential scan of the sources at a fixed view angle were synthesized to a 2D radiography image by a shift-and-add algorithm. The data collected with a full rotation of the system were reconstructed into 3D TB CT (TBCT) images using an Feldkamp, Davis, and Kress (FDK)-based computed tomography (CT) algorithm modified for the TB geometry.
RESULTS: With an 18.8-cm long source array and a 35-cm long detector array, the TB benchtop system provides a 25-cm cross-sectional and 8-cm axial field of view (FOV). The scatter-to-primary ratio (SPR) was approximately 17% for TB, as compared with 120% for cone beam geometry. The TBCT system enables reconstructions in two-dimensional radiography and three-dimensional volumetric CT. The TBCT images were free of \"cupping\" artifacts and have similar image quality as diagnostic helical CT.
CONCLUSIONS: A TB imaging benchtop imaging system was successfully developed with MPTEX source and PCD. Phantom and animal cadaver imaging demonstrated that the TB system can produce satisfactory radiographic X-ray images and 3D CT images with image quality comparable to diagnostic helical CTs.

OBJECTIVE: To assess the potential of spectral photon-counting (PC) radiography (SPCR) for the detection and characterization of monosodium urate (MSU) and calcium hydroxyapatite (HA) crystals, based on effective atomic number (Zeff) values derived from specific X-ray attenuation characteristics at different energy levels.
METHODS: Suspensions of either pure agar, synthetic MSU (200 mg/ml) or HA (100 and 150 mg/ml) crystals in agar were sealed in industry-standard polystyrene vials and supported on a 2.5-mm-thick plastic table. Samples were scanned using a vendor microfocus X-ray tube and a spectral PC detector prototype with four energy thresholds per acquisition (15, 25, 30, and 35 keV). Material decomposition calibration was performed using polymethyl methacrylate (PMMA) and polyvinylchloride (PVC) slabs. Using a custom post-processing software based on polynomial material decomposition, Zeff of the respective samples were computed. All samples were additionally scanned using dual-energy CT (DECT, 80 kV and tin-filtered 150 kV) and analyzed with a proprietary post-processing algorithm for gout.
RESULTS: MSU crystal suspension attenuated significantly less than both HA samples. MSU and HA suspensions differed significantly in Zeff (mean ± SD: 7.74 ± 0.28 vs. 9.43 ± 0.41, p < .001). Zeff values from SPCR were comparable to DECT-based reference values (p = 0.16) and were independent of the radiation dose level (0.18 - 18 mAs, p = 1).
CONCLUSIONS: This in vitro feasibility study demonstrates the potential of SPCR for discriminating MSU from HA crystal suspensions based on Zeff differences. Further studies have to corroborate these initial findings ex vivo and in vivo, and to compare the diagnostic performance of SPCR with DECT in imaging of crystal-associated arthropathies.

Material discrimination is an important application of dual-energy computed tomography (CT) techniques. Projection decomposition is a key problem for pre-reconstruction material discrimination. In this study, we focused on the pre-reconstruction space based on the photoelectric and Compton effect decomposition model to characterize different material components, and proposed an efficient method to calculate the projection decomposition coefficient. We converted the complex projection integral into a linear equation by calculating the equivalent monochromatic energy from the high and low energy spectrum. Meanwhile, we constructed a dual-energy CT system based on a photon-counting detector to take small animal scan and material discrimination analysis. Finally, the results of simulation and experimental study demonstrated the feasibility of our proposed new method, and explained the characteristics of photoelectric absorption and Compton scattering reconstruction images.

OBJECTIVE: A dual-energy material decomposition method using photon-counting spectral mammography was investigated as a non-invasive diagnostic approach to differentiate between Type I calcifications, consisting of calcium oxalate dihydrate or weddellite compounds that are more often associated with benign lesions, and Type II calcifications containing hydroxyapatite that are predominantly associated with malignant tumors.
METHODS: The study was carried out by numerical simulation to assess the feasibility of the proposed approach. A pencil-beam geometry was modeled, and the total number of x-rays transported through a breast embedded with microcalcifications of different types and sizes were simulated by a one-pixel detector. Material decomposition using two energy bins was then applied to characterize the simulated calcifications into hydroxyapatite and weddellite using maximum-likelihood estimation, taking into account the polychromatic source, and the energy dependent attenuation. Simulation tests were carried out for different dose levels, energy windows and calcification sizes for multiple noise realizations.
RESULTS: The results were analyzed using receiver operating characteristic (ROC) analysis. Classification between Type I and Type II calcifications achieved by analyzing a single microcalcification showed moderate accuracy. However, simultaneously analyzing several calcifications within the cluster provided area under the ROC curve of greater than 99% for radiation dose greater than 4.8 mGy mean glandular dose.
CONCLUSIONS: Simulation results indicated that photon-counting spectral mammography with dual energy material decomposition has the potential to be used as a non-invasive method for discrimination between Type I and Type II microcalcifications that can potentially improve early breast cancer diagnosis and reduce the number of negative breast biopsies. Additional studies using breast specimens and clinical data should be performed to further explore the feasibility of this approach.