Experimental feasibility of multi-energy photon-counting K-edge imaging in pre-clinical computed tomography

The Photon-Counting CT Enters the Field of Cochlear Implantation: Comparison to Angiography DynaCT and Conventional Multislice CT

INTRODUCTION

Cochlear duct length (CDL) measurement plays a role in the context of individualized cochlear implant (CI) surgery regarding an individualized selection and implantation of the CI electrode carrier and an efficient postoperative anatomy-based fitting process. The level of detail of the preoperative temporal bone CT scan depends on the imaging modality with major impact on CDL measurements and CI electrode contact position determination. The aim of this study was to evaluate the accuracy of perioperative CDL measurements and electrode contact determination in photon-counting CT (PCCT).

METHODS

Ten human fresh-frozen petrous bone specimens were examined with a first-generation PCCT. A clinically applicable radiation dose of 27.1 mGy was used. Scans were acquired before and after CI insertion. Postoperative measurement of the CDL was conducted using an otological planning software and 3D-curved multiplanar reconstruction. Investigation of electrode contact position was performed by two respective observers. Measurements were compared with a conventional multislice CT and to a high-resolution flat-panel volume CT with secondary reconstructions.

RESULTS

Pre- and postoperative CDL measurements in PCCT images showed no significant difference to high-resolution flat-panel volume CT. Postoperative CI electrode contact determination was also as precise as the flat-panel CT-based assessment. PCCT and flat-panel volume CT were equivalent concerning interobserver variability.

CONCLUSION

CDL measurement with PCCT was equivalent to flat-panel volume CT with secondary reconstructions. PCCT enabled highly precise postoperative CI electrode contact determination with substantial advantages over conventional multislice CT scanners.

Multimodal 2D and 3D microscopic mapping of growth cartilage by computational imaging techniques - a short review including new research

Being able to image the microstructure of growth cartilage is important for understanding the onset and progression of diseases such as osteochondrosis and osteoarthritis, as well as for developing new treatments and implants. Studies of cartilage using conventional optical brightfield microscopy rely heavily on histological staining, where the added chemicals provide tissue-specific colours. Other microscopy contrast mechanisms include polarization, phase- and scattering contrast, enabling non-stained or 'label-free' imaging that significantly simplifies the sample preparation, thereby also reducing the risk of artefacts. Traditional high-performance microscopes tend to be both bulky and expensive.Computational imagingdenotes a range of techniques where computers with dedicated algorithms are used as an integral part of the image formation process. Computational imaging offers many advantages like 3D measurements, aberration correction and quantitative phase contrast, often combined with comparably cheap and compact hardware. X-ray microscopy is also progressing rapidly, in certain ways trailing the development of optical microscopy. In this study, we first briefly review the structures of growth cartilage and relevant microscopy characterization techniques, with an emphasis on Fourier ptychographic microscopy (FPM) and advanced x-ray microscopies. We next demonstrate with our own results computational imaging through FPM and compare the images with hematoxylin eosin and saffron (HES)-stained histology. Zernike phase contrast, and the nonlinear optical microscopy techniques of second harmonic generation (SHG) and two-photon excitation fluorescence (TPEF) are explored. Furthermore, X-ray attenuation-, phase- and diffraction-contrast computed tomography (CT) images of the very same sample are presented for comparisons. Future perspectives on the links to artificial intelligence, dynamic studies andin vivopossibilities conclude the article.

Development of a separability index for task specific characterization of spectral computed tomography

PURPOSE

In this work, we define a signal detection based metrology to characterize the separability of two different multi-dimensional signals in spectral CT acquisitions.

METHOD

Signal response was modelled as a random process with a deterministic signal and stochastic noise component. A linear Hotelling observer was used to estimate a scalar test statistic distribution that predicts the likelihood of an intensity value belonging to a signal. Two distributions were estimated for two materials of interest and used to derive two metrics separability: a separability index (s') and the area under the curve of the test statistic distributions. Experimental and simulated data of photon-counting CT scanners were used to evaluate each metric. Experimentally, vials of iodine and gadolinium (2, 4, 8 mg/mL) were scanned at multiple tube voltages, tube currents and energy thresholds. Additionally, a simulated dataset with low tube current (10-150 mAs) and material concentrations (0.25-4 mg/mL) was generated.

RESULTS

Experimental data showed that conditions favorable for low noise and expression of k-edge signal produced the highest separability. Material concentration had the greatest impact on separability. The simulated data showed that under more difficult separation conditions, difference in material concentration still had the greatest impact on separability.

CONCLUSION

The results demonstrate the utility of a task specific metrology to measure the overlap in signal between different materials in spectral CT. Using experimental and simulated data, the separability index was shown to describe the relationship between image formation factors and the signal responses of material.

Approaches, advantages, and challenges to photon counting detector and multi-energy CT

Photon counting detector CT (PCD-CT) is the newest major development in CT technology and has been commercially available since 2021. It offers major technological advantages over current standard-of-care energy integrating detector CT (EID-CT) including improved spatial resolution, improved iodine contrast to noise ratio, multi-energy imaging, and reduced noise. This article serves as a foundational basis to the technical approaches and concepts of PCD-CT technology with primary emphasis on detector technology in direct comparison to EID-CT. The article also addresses current technological challenges to PCD-CT with particular attention to cross talk and its causes (e.g., Compton scattering, fluorescence, charge sharing, K-escape) as well as pile-up.

Photon-Counting Detector CT for Kidney Stone Detection in Excretory Phase CT-Comparison Between Virtual Non-contrast and Virtual Non-iodine Reconstructions in a 3D Printed Kidney Phantom

RATIONALE AND OBJECTIVES

To evaluate and compare the effectiveness of contrast media subtraction and kidney stone detection between a virtual non-iodine reconstruction algorithm (VNI; PureCalcium) and a virtual non-contrast (VNC) algorithm in excretory phase photon-counting detector computed tomography (PCD-CT), using a 3D printed kidney phantom under various tube voltages and radiation doses.

MATERIALS AND METHODS

A 3D-printed kidney phantom, holding Calcium Oxalate (CaOx) and uric acid stones within contrast-enhanced calyces, was created. The calyx density mirrored the average density observed in 200 excretory phase patients (916 HU at 110 kV). Imaging was conducted on a clinical dual-source PCD-CT at 120 kV and 140 kV, with radiation doses set at 5, 10, and 15 mGy. VNI and VNC algorithms were applied. Two blinded readers evaluated the image quality, along with the degree of contrast media and kidney stone subtraction, using visual scales. Krippendorff's alpha was calculated to determine inter-reader agreement, and the Chi-squared test was employed for comparing ordinal data.

RESULTS

Reader 2 rated overall image quality higher for VNI than VNC (4.90 vs. 4.00; P < .05), while Reader 1 found no significant difference (4.96 vs. 5.00; P > .05). Substantial agreement was observed between readers for contrast media subtraction in both VNC and VNI (Krippendorff's alpha range: 0.628-0.748). Incomplete contrast media subtraction occurred more frequently with VNI for both readers (Reader 1: 29% vs. 15%; P < .05; Reader 2: 24% vs. 20%; P > .05). Uric acid and smaller stones (<5 mm) were more likely to be subtracted than CaOx and larger stones in both VNC and VNI. Overall, a higher rate of stone subtraction was noted with VNI compared to VNC (Reader 1: 22% vs. 16%; Reader 2: 25% vs. 10%; P < .05). Neither radiation dose nor tube voltage significantly influenced stone subtraction (P > .05).

CONCLUSION

VNC demonstrated greater accuracy than VNI for contrast media subtraction and kidney stone visibility. Radiation dose and tube voltage had no significant impact. Nonetheless, both algorithms still exhibited frequent incomplete contrast media subtraction and partial kidney stone subtraction.

Multimaterial decomposition in dual-energy CT for characterization of clots from acute ischemic stroke patients

BACKGROUND

Nowadays, there is no method to quantitatively characterize the material composition of acute ischemic stroke thrombi prior to intervention, but dual-energy CT (DE-CT) offers imaging-based multimaterial decomposition. We retrospectively investigated the material composition of thrombi ex vivo using DE-CT with histological analysis as a reference.

METHODS

Clots of 70 patients with acute ischemic stroke were extracted by mechanical thrombectomy and scanned ex vivo in formalin-filled tubes with DE-CT. Multimaterial decomposition in the three components, i.e., red blood cells (RBC), white blood cells (WBC), and fibrin/platelets (F/P), was performed and compared to histology (hematoxylin/eosin staining) as reference. Attenuation and effective Z values were assessed, and histological composition was compared to stroke etiology according to the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) criteria.

RESULTS

Histological and imaging analysis showed the following correlation coefficients for RBC (r = 0.527, p < 0.001), WBC (r = 0.305, p = 0.020), and F/P (r = 0.525, p < 0.001). RBC-rich thrombi presented higher clot attenuation in Hounsfield units than F/P-rich thrombi (51 HU versus 42 HU, p < 0.01). In histological analysis, cardioembolic clots showed less RBC (40% versus 56%, p = 0.053) and more F/P (53% versus 36%, p = 0.024), similar to cryptogenic clots containing less RBC (34% versus 56%, p = 0.006) and more F/P (58% versus 36%, p = 0.003) than non-cardioembolic strokes. No difference was assessed for the mean WBC portions in all TOAST groups.

CONCLUSIONS

DE-CT has the potential to quantitatively characterize the material composition of ischemic stroke thrombi.

RELEVANCE STATEMENT

Using DE-CT, the composition of ischemic stroke thrombi can be determined. Knowledge of histological composition prior to intervention offers the opportunity to define personalized treatment strategies for each patient to accomplish faster recanalization and better clinical outcomes.

KEY POINTS

• Acute ischemic stroke clots present different recanalization success according to histological composition. • Currently, no method can determine clot composition prior to intervention. • DE-CT allows quantitative material decomposition of thrombi ex vivo in red blood cells, white blood cells, and fibrin/platelets. • Histological clot composition differs between stroke etiology. • Insights into the histological composition in situ offer personalized treatment strategies.

Edge-illumination spectral phase-contrast tomography

Following the rapid, but independent, diffusion of x-ray spectral and phase-contrast systems, this work demonstrates the first combination of spectral and phase-contrast computed tomography (CT) obtained by using the edge-illumination technique and a CdTe small-pixel (62μm) spectral detector. A theoretical model is introduced, starting from a standard attenuation-based spectral decomposition and leading to spectral phase-contrast material decomposition. Each step of the model is followed by quantification of accuracy and sensitivity on experimental data of a test phantom containing different solutions with known concentrations. An example of a micro CT application (20μm voxel size) on an iodine-perfusedex vivomurine model is reported. The work demonstrates that spectral-phase contrast combines the advantages of spectral imaging, i.e. high-Zmaterial discrimination capability, and phase-contrast imaging, i.e. soft tissue sensitivity, yielding simultaneously mass density maps of water, calcium, and iodine with an accuracy of 1.1%, 3.5%, and 1.9% (root mean square errors), respectively. Results also show a 9-fold increase in the signal-to-noise ratio of the water channel when compared to standard spectral decomposition. The application to the murine model revealed the potential of the technique in the simultaneous 3D visualization of soft tissue, bone, and vasculature. While being implemented by using a broad spectrum (pink beam) at a synchrotron radiation facility (Elettra, Trieste, Italy), the proposed experimental setup can be readily translated to compact laboratory systems including conventional x-ray tubes.

Ultra-High-Resolution and K-Edge Imaging of Prosthetic Heart Valves With Spectral Photon-Counting CT: A Phantom Study

BACKGROUND AND PURPOSE

The contribution of cardiac computed tomography (CT) for the detection and characterization of prosthetic heart valve (PHV) complications is still limited due mainly to artifacts. Computed tomography systems equipped with photon-counting detectors (PCDs) have the potential to overcome these limitations. Therefore, the aim of the study was to compare image quality of PHV with PCD-CT and dual-energy dual-layer CT (DEDL-CT).

MATERIALS AND METHODS

Two metallic and 3 biological PHVs were placed in a tube containing diluted iodinated contrast inside a thoracic phantom and scanned repeatedly at different angles on a DEDL-CT and PCD-CT. Two small lesions (~2 mm thickness; containing muscle and fat, respectively) were attached to the structure of 4 valves, placed inside the thoracic phantom, with and without an extension ring, and scanned again. Acquisition parameters were matched for the 2 CT systems and used for all scans. Metallic valves were scanned again with parameters adapted for tungsten K-edge imaging. For all valves, different metallic parts were measured on conventional images to assess their thickness and blooming artifacts. In addition, 6 parallelepipeds per metallic valve were drawn, and all voxels with density <3 times the standard deviation of the contrast media were recorded as an estimate of streak artifacts. For subjective analysis, 3 expert readers assessed conventional images of the valves, with and without lesions, and tungsten K-edge images. Conspicuity and sharpness of the different parts of the valve, the lesions, metallic, and blooming artifacts were scored on a 4-point scale. Measurements and scores were compared with the paired t test or Wilcoxon test.

RESULTS

The objective analysis showed that, with PCD-CT, valvular metallic structures were thinner and presented less blooming artifacts. Metallic artifacts were also reduced with PCD-CT (11 [interquartile (IQ) = 6] vs 40 [IQ = 13] % of voxels). Subjective analysis allowed noticing that some structures were visible or clearly visible only with PCD-CT. In addition, PCD-CT yielded better scores for the conspicuity and for the sharpness of all structures (all Ps < 0.006), except for the conspicuity of the leaflets of the mechanical valves, which were well visible with either technique (4 [IQ = 3] for both). Both blooming and streak artifacts were reduced with PCD-CT (P ≤ 0.01). Overall, the use of PCD-CT resulted in better conspicuity and sharpness of the lesions compared with DEDL-CT (both Ps < 0.02). In addition, only with PCD-CT some differences between the 2 lesions were detectable. Adding the extension ring resulted in reduced conspicuity and sharpness with DEDL-CT (P = 0.04 and P = 0.02, respectively) and only in reduced sharpness with PCD-CT (P = 0.04). Tungsten K-edge imaging allowed for the visualization of the only dense structure containing it, the leaflets, and it resulted in images judged having less blooming and metallic artifacts as compared with conventional PCD-CT images (P < 0.01).

CONCLUSIONS

With PCD-CT, objective and subjective image quality of metallic and biological PHVs is improved compared with DEDL-CT. Notwithstanding the improvements in image quality, millimetric lesions attached to the structure of the valves remain a challenge for PCD-CT. Tungsten K-edge imaging allows for even further reduction of artifacts.

Material decomposition with a prototype photon-counting detector CT system: expanding a stoichiometric dual-energy CT method via energy bin optimization and K-edge imaging

Objective.Computed tomography (CT) has advanced since its inception, with breakthroughs such as dual-energy CT (DECT), which extracts additional information by acquiring two sets of data at different energies. As high-flux photon-counting detectors (PCDs) become available, PCD-CT is also becoming a reality. PCD-CT can acquire multi-energy data sets in a single scan by spectrally binning the incident x-ray beam. With this, K-edge imaging becomes possible, allowing high atomic number (high-Z) contrast materials to be distinguished and quantified. In this study, we demonstrated that DECT methods can be converted to PCD-CT systems by extending the method of Bourqueet al(2014). We optimized the energy bins of the PCD for this purpose and expanded the capabilities by employing K-edge subtraction imaging to separate a high-atomic number contrast material.Approach.The method decomposes materials into their effective atomic number (Zeff) and electron density relative to water (ρe). The model was calibrated and evaluated using tissue-equivalent materials from the RMI Gammex electron density phantom with knownρevalues and elemental compositions. TheoreticalZeffvalues were found for the appropriate energy ranges using the elemental composition of the materials.Zeffvaried slightly with energy but was considered a systematic error. Anex vivobovine tissue sample was decomposed to evaluate the model further and was injected with gold chloride to demonstrate the separation of a K-edge contrast agent.Main results.The mean root mean squared percent errors on the extractedZeffandρefor PCD-CT were 0.76% and 0.72%, respectively and 1.77% and 1.98% for DECT. The tissue types in theex vivobovine tissue sample were also correctly identified after decomposition. Additionally, gold chloride was separated from theex vivotissue sample with K-edge imaging.Significance.PCD-CT offers the ability to employ DECT material decomposition methods, along with providing additional capabilities such as K-edge imaging.

Quantitative multi-energy micro-CT: A simulation and phantom study for simultaneous imaging of four different contrast materials using an energy integrating detector

Emerging from the development of single-energy Computed Tomography (CT) and Dual-Energy Computed Tomography, Multi-Energy Computed Tomography (MECT) is a promising tool allowing advanced material and tissue decomposition and thereby enabling the use of multiple contrast materials in preclinical research. The scope of this work was to evaluate whether a usual preclinical micro-CT system is applicable for the decomposition of different materials using MECT together with a matrix-inversion method and how different changes of the measurement-environment affect the results. A matrix-inversion based algorithm to differentiate up to five materials (iodine, iron, barium, gadolinium, residual material) by applying four different acceleration voltages/energy levels was established. We carried out simulations using different ratios and concentrations (given in fractions of volume units, VU) of the four different materials (plus residual material) at different noise-levels for 30 keV, 40 keV, 50 keV, 60 keV, 80 keV and 100 keV (monochromatic). Our simulation results were then confirmed by using region of interest-based measurements in a phantom-study at corresponding acceleration voltages. Therefore, different mixtures of contrast materials were scanned using a micro-CT. Voxel wise evaluation of the phantom imaging data was conducted to confirm its usability for future imaging applications and to estimate the influence of varying noise-levels, scattering, artifacts and concentrations. The analysis of our simulations showed the smallest deviation of 0.01 (0.003-0.15) VU between given and calculated concentrations of the different contrast materials when using an energy-combination of 30 keV, 40 keV, 50 keV and 100 keV for MECT. Subsequent MECT phantom measurements, however, revealed a combination of acceleration voltages of 30 kV, 40 kV, 60 kV and 100 kV as most effective for performing material decomposition with a deviation of 0.28 (0-1.07) mg/ml. The feasibility of our voxelwise analyses using the proposed algorithm was then confirmed by the generation of phantom parameter-maps that matched the known contrast material concentrations. The results were mostly influenced by the noise-level and the concentrations used in the phantoms. MECT using a standard micro-CT combined with a matrix inversion method is feasible at four different imaging energies and allows the differentiation of mixtures of up to four contrast materials plus an additional residual material.

Ultra-high-resolution photon-counting detector computed tomography of the lungs: Phantom and clinical assessment of radiation dose and image quality

PURPOSE

Photon-counting-detector computed tomography (PCD-CT) offers enhanced noise reduction, spatial resolution, and image quality in comparison to energy-integrated-detectors CT (EID-CT). These hypothesized improvements were compared using PCD-CT ultra-high (UHR) and standard-resolution (SR) scan-modes.

METHODS

Phantom scans were obtained with both EID-CT and PCD-CT (UHR, SR) on an adult body-phantom. Radiation dose was measured and noise levels were compared at a minimum achievable slice thickness of 0.5 mm for EID-CT, 0.2 mm for PCD-CT-UHR and 0.4 mm for PCD-CT-SR. Signal-to-noise ratios (SNR) and contrast-to-noise ratios (CNR) were calculated for five tissue densities. Additionally, data from 25 patients who had PCD-CT of chest were reconstructed at 1 mm and 0.2 mm (UHR) slice-thickness and compared quantitatively (SNR) and qualitatively (noise, quality, sharpness, bone details).

RESULTS

Phantom PCD-CT-UHR and PCD-CT-SR scans had similar measured radiation dose (16.0mGy vs 15.8 mGy). Phantom PCD-CT-SR (0.4 mm) had lower noise level in comparison to EID-CT (0.5 mm) (9.0HU vs 9.6HU). PCD-CT-UHR (0.2 mm) had slightly higher noise level (11.1HU). Phantom PCD-CT-SR (0.4 mm) had higher SNR in comparison to EID-CT (0.5 mm) while achieving higher resolution (Bone 115 vs 96, Acrylic 14 vs 14, Polyethylene 11 vs 10). SNR was slightly lower across all densities for PCD-CT UHR (0.2 mm). Interestingly, CNR was highest in the 0.2 mm PCD-CT group; PCD-CT CNR was 2.45 and 2.88 times the CNR for 0.5 mm EID-CT for acrylic and poly densities. Clinical comparison of SNR showed predictably higher SNR for 1 mm (30.3 ± 10.7 vs 14.2 ± 7, p = 0.02). Median subjective ratings were higher for 0.2 mm UHR vs 1 mm PCD-CT for nodule contour (4.6 ± 0.3 vs 3.6 ± 0.1, p = 0.02), bone detail (5 ± 0 vs 4 ± 0.1, p = 0.001), image quality (5 ± 0.1 vs 4.6 ± 0.4, p = 0.001), and sharpness (5 ± 0.1 vs 4 ± 0.2).

CONCLUSION

Both UHR and SR PCD-CT result in similar radiation dose levels. PCD-CT can achieve higher resolution with lower noise level in comparison to EID-CT.

Gadolinium K-edge angiography with a spectral photon counting CT in atherosclerotic rabbits

PURPOSE

The purpose of this study was to investigate the feasibility of gadolinium-K-edge-angiography (angio-Gd-K-edge) with gadolinium-based contrast agents (GBCAs) as obtained with spectral photon counting CT (SPCCT) in atherosclerotic rabbits.

MATERIALS AND METHODS

Seven atherosclerotic rabbits underwent angio-SPCCT acquisitions with two GBCAs, with similar intravenous injection protocol. Conventional and angio-Gd-K-edge images were reconstructed with the same parameters. Regions of interest were traced in different locations of the aorta and its branches. Hounsfield unit values, Gd concentrations, signal-to-noise (SNR) and contrast-to-noise (CNR) were calculated and compared. The maximum diameter and the diameter of the aorta in regard to atherosclerotic plaques were measured by two observers. Images were subjectively evaluated regarding vessels' enhancement, artefacts, border sharpness and overall image quality.

RESULTS

In the analyzable six rabbits, Gd-K-edge allowed visualization of target vessels and no other structures. HU values and Gd concentrations were greatest in the largest artery (descending aorta, 5.6 ± 0.8 [SD] mm), and lowest in the smallest (renal arteries, 2.1 ± 0.3 mm). While greater for conventional images, CNR and SNR were satisfactory for both images (all P < 0.001). For one observer there were no statistically significant differences in either maximum or plaque-diameters (P = 0.45 and all P > 0.05 in post-hoc analysis, respectively). For the second observer, there were no significant differences for images reconstructed with the same parameters (all P < 0.05). All subjective criteria scored higher for conventional images compared to K-edge (all P < 0.01), with the highest scores for enhancement (4.3-4.4 vs. 3.1-3.4).

CONCLUSION

With SPCCT, angio-Gd-K-edge after injection of GBCAs in atherosclerotic rabbits is feasible and allows for angiography-like visualization of small arteries and for the reliable measurement of their diameters.

Constrained one-step material decomposition reconstruction of head CT data from a silicon photon-counting prototype

BACKGROUND

Spectral CT material decomposition provides quantitative information but is challenged by the instability of the inversion into basis materials. We have previously proposed the constrained One-Step Spectral CT Image Reconstruction (cOSSCIR) algorithm to stabilize the material decomposition inversion by directly estimating basis material images from spectral CT data. cOSSCIR was previously investigated on phantom data.

PURPOSE

This study investigates the performance of cOSSCIR using head CT datasets acquired on a clinical photon-counting CT (PCCT) prototype. This is the first investigation of cOSSCIR for large-scale, anatomically complex, clinical PCCT data. The cOSSCIR decomposition is preceded by a spectrum estimation and nonlinear counts correction calibration step to address nonideal detector effects.

METHODS

Head CT data were acquired on an early prototype clinical PCCT system using an edge-on silicon detector with eight energy bins. Calibration data of a step wedge phantom were also acquired and used to train a spectral model to account for the source spectrum and detector spectral response, and also to train a nonlinear counts correction model to account for pulse pileup effects. The cOSSCIR algorithm optimized the bone and adipose basis images directly from the photon counts data, while placing a grouped total variation (TV) constraint on the basis images. For comparison, basis images were also reconstructed by a two-step projection-domain approach of Maximum Likelihood Estimation (MLE) for decomposing basis sinograms, followed by filtered backprojection (MLE + FBP) or a TV minimization algorithm (MLE + TVmin ) to reconstruct basis images. We hypothesize that the cOSSCIR approach will provide a more stable inversion into basis images compared to two-step approaches. To investigate this hypothesis, the noise standard deviation in bone and soft-tissue regions of interest (ROIs) in the reconstructed images were compared between cOSSCIR and the two-step methods for a range of regularization constraint settings.

RESULTS

cOSSCIR reduced the noise standard deviation in the basis images by a factor of two to six compared to that of MLE + TVmin , when both algorithms were constrained to produce images with the same TV. The cOSSCIR images demonstrated qualitatively improved spatial resolution and depiction of fine anatomical detail. The MLE + TVmin algorithm resulted in lower noise standard deviation than cOSSCIR for the virtual monoenergetic images (VMIs) at higher energy levels and constraint settings, while the cOSSCIR VMIs resulted in lower noise standard deviation at lower energy levels and overall higher qualitative spatial resolution. There were no statistically significant differences in the mean values within the bone region of images reconstructed by the studied algorithms. There were statistically significant differences in the mean values within the soft-tissue region of the reconstructed images, with cOSSCIR producing mean values closer to the expected values.

CONCLUSIONS

The cOSSCIR algorithm, combined with our previously proposed spectral model estimation and nonlinear counts correction method, successfully estimated bone and adipose basis images from high resolution, large-scale patient data from a clinical PCCT prototype. The cOSSCIR basis images were able to depict fine anatomical details with a factor of two to six reduction in noise standard deviation compared to that of the MLE + TVmin two-step approach.

Photon-Counting Computed Tomography - Basic Principles, Potenzial Benefits, and Initial Clinical Experience

BACKGROUND

Photon-counting computed tomography (PCCT) is a promising new technology with the potential to fundamentally change today's workflows in the daily routine and to provide new quantitative imaging information to improve clinical decision-making and patient management.

METHOD

The content of this review is based on an unrestricted literature search on PubMed and Google Scholar using the search terms "Photon-Counting CT", "Photon-Counting detector", "spectral CT", "Computed Tomography" as well as on the authors' experience.

RESULTS

The fundamental difference with respect to the currently established energy-integrating CT detectors is that PCCT allows counting of every single photon at the detector level. Based on the identified literature, PCCT phantom measurements and initial clinical studies have demonstrated that the new technology allows improved spatial resolution, reduced image noise, and new possibilities for advanced quantitative image postprocessing.

CONCLUSION

For clinical practice, the potential benefits include fewer beam hardening artifacts, radiation dose reduction, and the use of new contrast agents. In this review, we will discuss basic technical principles and potential clinical benefits and demonstrate first clinical use cases.

KEY POINTS

· Photon-counting computed tomography (PCCT) has been implemented in the clinical routine. · Compared to energy-integrating detector CT, PCCT allows the reduction of electronic image noise. · PCCT provides increased spatial resolution and a higher contrast-to-noise ratio. · The novel detector technology allows the quantification of spectral information.

CITATION FORMAT

· Stein T, Rau A, Russe MF et al. Photon-Counting Computed Tomography - Basic Principles, Potenzial Benefits, and Initial Clinical Experience. Fortschr Röntgenstr 2023; 195: 691 - 698.

Quantification of cartilage and subchondral bone cysts on knee specimens based on a spectral photon-counting computed tomography

Spectral photon-counting computed tomography (SPCCT) is a new technique with the capability to provide mono-energetic (monoE) images with high signal to noise ratio. We demonstrate the feasibility of SPCCT to characterize at the same time cartilage and subchondral bone cysts (SBCs) without contrast agent in osteoarthritis (OA). To achieve this goal, 10 human knee specimens (6 normal and 4 with OA) were imaged with a clinical prototype SPCCT. The monoE images at 60 keV with isotropic voxels of 250 × 250 × 250 µm3 were compared with monoE synchrotron radiation CT (SR micro-CT) images at 55 keV with isotropic voxels of 45 × 45 × 45 µm3 used as benchmark for cartilage segmentation. In the two OA knees with SBCs, the volume and density of SBCs were evaluated in SPCCT images. In 25 compartments (lateral tibial (LT), medial tibial, (MT), lateral femoral (LF), medial femoral and patella), the mean bias between SPCCT and SR micro-CT analyses were 101 ± 272 mm3 for cartilage volume and 0.33 mm ± 0.18 for mean cartilage thickness. Between normal and OA knees, mean cartilage thicknesses were found statistically different (0.005 < p < 0.04) for LT, MT and LF compartments. The 2 OA knees displayed different SBCs profiles in terms of volume, density, and distribution according to size and location. SPCCT with fast acquisitions is able to characterize cartilage morphology and SBCs. SPCCT can be used potentially as a new tool in clinical studies in OA.

Standardization and Quantitative Imaging With Photon-Counting Detector CT

ABSTRACT

Computed tomography (CT) images display anatomic structures across 3 dimensions and are highly quantitative; they are the reference standard for 3-dimensional geometric measurements and are used for 3-dimensional printing of anatomic models and custom implants, as well as for radiation therapy treatment planning. The pixel intensity in CT images represents the linear x-ray attenuation coefficient of the imaged materials after linearly scaling the coefficients into a quantity known as CT numbers that is conveyed in Hounsfield units. When measured with the same scanner model, acquisition, and reconstruction parameters, the mean CT number of a material is highly reproducible, and quantitative applications of CT scanning that rely on the measured CT number, such as for assessing bone mineral density or coronary artery calcification, are well established. However, the strong dependence of CT numbers on x-ray beam spectra limits quantitative applications and standardization from achieving robust widespread success. This article reviews several quantitative applications of CT and the challenges they face, and describes the benefits brought by photon-counting detector (PCD) CT technology. The discussed benefits of PCD-CT include that it is inherently multienergy, expands material decomposition capabilities, and improves spatial resolution and geometric quantification. Further, the utility of virtual monoenergetic images to standardize CT numbers is discussed, as virtual monoenergetic images can be the default image type in PCD-CT due to the full-time spectral nature of the technology.

New Contrast Media for K-Edge Imaging With Photon-Counting Detector CT

ABSTRACT

The recent technological developments in photon-counting detector computed tomography (PCD-CT) and the introduction of the first commercially available clinical PCD-CT unit open up new exciting opportunities for contrast media research. With PCD-CT, the efficacy of available iodine-based contrast media improves, allowing for a reduction of iodine dosage or, on the other hand, an improvement of image quality in low contrast indications. Virtual monoenergetic image reconstructions are routinely available and enable the virtual monoenergetic image energy to be adapted to the diagnostic task.A key property of PCD-CT is the ability of spectral separation in combination with improved material decomposition. Thus, the discrimination of contrast media from intrinsic or pathological tissues and the discrimination of 2 or more contrasting elements that characterize different tissues are attractive fields for contrast media research. For these approaches, K-edge imaging in combination with high atomic number elements such as the lanthanides, tungsten, tantalum, or bismuth plays a central role.The purpose of this article is to present an overview of innovative contrast media concepts that use high atomic number elements. The emphasis is on improving contrast enhancement for cardiovascular plaque imaging, stent visualization, and exploring new approaches using 2 contrasting elements. Along with the published research, new experimental findings with a contrast medium that incorporates tungsten are included.Both the literature review and the new experimental data demonstrate the great potential and feasibility for new contrast media to significantly increase diagnostic performance and to enable new clinical fields and indications in combination with PCD-CT.

First Experience With a Whole-Body Spectral Photon-Counting CT Clinical Prototype

ABSTRACT

Spectral photon-counting computed tomography (SPCCT) technology holds great promise for becoming the next generation of computed tomography (CT) systems. Its technical characteristics have many advantages over conventional CT imaging. For example, SPCCT provides better spatial resolution, greater dose efficiency for ultra-low-dose and low-dose protocols, and tissue contrast superior to that of conventional CT. In addition, SPCCT takes advantage of several known approaches in the field of spectral CT imaging, such as virtual monochromatic imaging and material decomposition imaging. In addition, SPCCT takes advantage of a new approach in this field, known as K-edge imaging, which allows specific and quantitative imaging of a heavy atom-based contrast agent. Hence, the high potential of SPCCT systems supports their ongoing investigation in clinical research settings. In this review, we propose an overview of our clinical research experience of a whole-body SPCCT clinical prototype, to give an insight into the potential benefits for clinical human imaging on image quality, diagnostic confidence, and new approaches in spectral CT imaging.

Dual-Energy CT in Cardiothoracic Imaging: Current Developments

This article describes the technical principles and clinical applications of dual-energy computed tomography (DECT) in the context of cardiothoracic imaging with a focus on current developments and techniques. Since the introduction of DECT, different vendors developed distinct hard and software approaches for generating multi-energy datasets and multiple DECT applications that were developed and clinically investigated for different fields of interest. Benefits for various clinical settings, such as oncology, trauma and emergency radiology, as well as musculoskeletal and cardiovascular imaging, were recently reported in the literature. State-of-the-art applications, such as virtual monoenergetic imaging (VMI), material decomposition, perfused blood volume imaging, virtual non-contrast imaging (VNC), plaque removal, and virtual non-calcium (VNCa) imaging, can significantly improve cardiothoracic CT image workflows and have a high potential for improvement of diagnostic accuracy and patient safety.

Research on accuracy of material identification based on photon counting spectral CT

BACKGROUND

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.

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Direct Multi-Material Reconstruction via Iterative Proximal Adaptive Descent for Spectral CT Imaging

Spectral computed tomography (spectral CT) is a promising medical imaging technology because of its ability to provide information on material characterization and quantification. However, with an increasing number of basis materials, the nonlinearity of measurements causes difficulty in decomposition. In addition, noise amplification and beam hardening further reduce image quality. Thus, improving the accuracy of material decomposition while suppressing noise is pivotal for spectral CT imaging. This paper proposes a one-step multi-material reconstruction model as well as an iterative proximal adaptive decent method. In this approach, a proximal step and a descent step with adaptive step size are designed under the forward-backward splitting framework. The convergence analysis of the algorithm is further discussed according to the convexity of the optimization objective function. For simulation experiments with different noise levels, the peak signal-to-noise ratio (PSNR) obtained by the proposed method increases approximately 23 dB, 14 dB, and 4 dB compared to those of other algorithms. Magnified areas of thorax data further demonstrated that the proposed method has a better ability to preserve details in tissues, bones, and lungs. Numerical experiments verify that the proposed method efficiently reconstructed the material maps, and reduced noise and beam hardening artifacts compared with the state-of-the-art methods.

An experimental framework for assessing the detective quantum efficiency of spectroscopic x-ray detectors

BACKGROUND

Assessing the performance of spectroscopic x-ray detectors (SXDs) requires measurement of the frequency-dependent detective quantum efficiency (DQE). Analytical expressions of the task-based DQE and task-independent DQE of SXDs have been presented in the literature, but standardizable experimental methods for measuring them have not. The task-based DQE quantifies the efficiency with which an SXD uses the x-ray quanta incident upon it to either quantify or detect a basis material (e.g., soft tissue or bone) of interest. The task-independent DQE is akin to the conventional DQE in that it is independent of the basis material to be detected or quantified.

PURPOSE

The purpose of this paper is to develop an experimental framework to present a method for experimental analysis of the DQE of SXDs, including the task-based DQE and task-independent DQE.

METHODS

We develop methods to measure the frequency-dependent DQE for task of quantifying or detecting a perturbation in a known basis material. We also develop methods for measuring a task-independent DQE. We show that the task-based DQEs and the task-independent DQE can be measured using a modest extension of the methods prescribed by International Electrotechnical Commission (IEC). Specifically, measuring the task-independent DQE requires measuring the modulation transfer function (MTF) and noise power spectrum (NPS) of each energy-bin image, in addition to the cross NPS between energy-bin images. Measuring the task-based DQEs requires an additional measurement of the transmission fraction through a thin basis-material absorber. We implemented the developed methods using standardized IEC x-ray spectra, aluminum (Al) and polymethyl methacrylyte (PMMA) basis materials, and a cadmium telluride (CdTe) SXD equipped with two energy bins and analog charge summing (ACS) for charge-sharing suppression. We also performed a regression analysis to determine whether or not the task-independent DQE is predictive of the task-based DQEs.

RESULTS

Experimental results of the task-based DQEs were consistent with simulation results presented in the literature. In general, and as expected, ACS increased the task-based DQEs and task-independent DQE. This effect was most pronounced for quantification tasks, in some instances yielding a five-fold increase in the DQE. For both spectra, with and without ACS for charge sharing correction, the task-based DQEs were linearly related to the task-independent DQE, as demonstrated by R² -values ranging from 0.89 to 1.00.

CONCLUSIONS

We have extended experimental DQE analysis to SXDs that count photons in multiple energy bins in a single x-ray exposure. The developed framework is an extension of existing IEC methods, and provides a standardized approach to assessing the performance of SXDs.

Photon-Counting Computed Tomography (PCCT): Technical Background and Cardio-Vascular Applications

Photon-counting computed tomography (PCCT) is a new advanced imaging technique that is going to transform the standard clinical use of computed tomography (CT) imaging. Photon-counting detectors resolve the number of photons and the incident X-ray energy spectrum into multiple energy bins. Compared with conventional CT technology, PCCT offers the advantages of improved spatial and contrast resolution, reduction of image noise and artifacts, reduced radiation exposure, and multi-energy/multi-parametric imaging based on the atomic properties of tissues, with the consequent possibility to use different contrast agents and improve quantitative imaging. This narrative review first briefly describes the technical principles and the benefits of photon-counting CT and then provides a synthetic outline of the current literature on its use for vascular imaging.

Dual- and multi-energy CT for particle stopping-power estimation: current state, challenges and potential

Range uncertainty has been a key factor preventing particle radiotherapy from reaching its full physical potential. One of the main contributing sources is the uncertainty in estimating particle stopping power (ρ_s) within patients. Currently, theρ_sdistribution in a patient is derived from a single-energy CT (SECT) scan acquired for treatment planning by converting CT number expressed in Hounsfield units (HU) of each voxel toρ_susing a Hounsfield look-up table (HLUT), also known as the CT calibration curve. HU andρ_sshare a linear relationship with electron density but differ in their additional dependence on elemental composition through different physical properties, i.e. effective atomic number and mean excitation energy, respectively. Because of that, the HLUT approach is particularly sensitive to differences in elemental composition between real human tissues and tissue surrogates as well as tissue variations within and among individual patients. The use of dual-energy CT (DECT) forρ_sprediction has been shown to be effective in reducing the uncertainty inρ_sestimation compared to SECT. The acquisition of CT data over different x-ray spectra yields additional information on the material elemental composition. Recently, multi-energy CT (MECT) has been explored to deduct material-specific information with higher dimensionality, which has the potential to further improve the accuracy ofρ_sestimation. Even though various DECT and MECT methods have been proposed and evaluated over the years, these approaches are still only scarcely implemented in routine clinical practice. In this topical review, we aim at accelerating this translation process by providing: (1) a comprehensive review of the existing DECT/MECT methods forρ_sestimation with their respective strengths and weaknesses; (2) a general review of uncertainties associated with DECT/MECT methods; (3) a general review of different aspects related to clinical implementation of DECT/MECT methods; (4) other potential advanced DECT/MECT applications beyondρ_sestimation.

Monochromatic computed tomography using laboratory-scale setup

In this article, we demonstrate the viability of highly monochromatic full-field X-ray absorption near edge structure based tomography using a laboratory-scale Johann-type X-ray absorption spectrometer utilising a conventional X-ray tube source. In this proof-of-concept, by using a phantom embedded with elemental Se, Na[Formula: see text]SeO[Formula: see text], and Na[Formula: see text]SeO[Formula: see text], we show that the three-dimensional distributions of Se in different oxidation states can be mapped and distinguished from the phantom matrix and each other with absorption edge contrast tomography. The presented method allows for volumetric analyses of chemical speciation in mm-scale samples using low-brilliance X-ray sources, and represents a new analytic tool for materials engineering and research in many fields including biology and chemistry.

An introduction to photon-counting detector CT (PCD CT) for radiologists

The basic performance of photon-counting detector computed tomography (PCD CT) is superior to conventional CT (energy-integrating detector CT: EID CT) because its spatial- and contrast resolution of soft tissues is higher, and artifacts are reduced. Because the X-ray photon energy separation is better with PCD CT than conventional EID-based dual-energy CT, it has the potential to improve virtual monochromatic- and virtual non-contrast images, material decomposition including quantification of the iodine distribution, and K-edge imaging. Therefore, its clinical applicability may be increased. Although the image quality of PCD CT scans is superior to that of EID CT currently, further improvement may be possible. The introduction of iterative image reconstruction and reconstruction with deep convolutional neural networks will be useful.

Fast X-ray diffraction (XRD) tomography for enhanced identification of materials

X-ray computed tomography (CT) is a commercially established modality for imaging large objects like passenger luggage. CT can provide the density and the effective atomic number, which is not always sufficient to identify threats like explosives and narcotics, since they can have a similar composition to benign plastics, glass, or light metals. In these cases, X-ray diffraction (XRD) may be better suited to distinguish the threats. Unfortunately, the diffracted photon flux is typically much weaker than the transmitted one. Measurement of quality XRD data is therefore slower compared to CT, which is an economic challenge for potential customers like airports. In this article we numerically analyze a novel low-cost scanner design which captures CT and XRD signals simultaneously, and uses the least possible collimation to maximize the flux. To simulate a realistic instrument, we propose a forward model that includes the resolution-limiting effects of the polychromatic spectrum, the detector, and all the finite-size geometric factors. We then show how to reconstruct XRD patterns from a large phantom with multiple diffracting objects. We include a reasonable amount of photon counting noise (Poisson statistics), as well as measurement bias (incoherent scattering). Our XRD reconstruction adds material-specific information, albeit at a low resolution, to the already existing CT image, thus improving threat detection. Our theoretical model is implemented in GPU (Graphics Processing Unit) accelerated software which can be used to further optimize scanner designs for applications in security, healthcare, and manufacturing quality control.

Theoretical comparison of energy-resolved and digital-subtraction angiography

BACKGROUND

X-ray coronary angiography is a sub-optimal vascular imaging technique because it cannot suppress un-enhanced anatomy that may obscure the visualization of coronary artery disease.

PURPOSE

The purpose of this paper is to evaluate the theoretical image quality of energy-resolved x-ray angiography (ERA) implemented with spectroscopic x-ray detectors (SXDs), which may overcome limitations of x-ray coronary angiography.

METHODS

We modeled the large-area signal-difference-to-noise (SDNR) of contrast-enhanced vasculature in ERA images and compared with that of digital-subtraction angiography (DSA), which served as a gold standard vascular imaging technique. To this end, we used calibrated numerical models of the response of cadmium telluride SXDs including the effects of charge sharing, electronic noise, and energy thresholding. Our models assumed zero scatter, no pulse pile up and small signals such that image contrast is approximately linear in the area density of contrast agents. For DSA, we similarly modeled x-ray detection by cesium iodide energy-integrating detectors using validated numerical models. For ERA, we investigated iodine and gadolinium (Gd) contrast agents, two-material and three-material decompositions, analog charge summing for charge sharing correction, and optimized image quality with respect to the tube voltage and energy thresholds assuming cadmium telluride SXDs with three energy bins.

RESULTS

Our analysis reveals that a three-material decomposition using iodine contrast agents will require x-ray exposures that are approximately 400 times those of DSA to achieve the same SDNR as DSA in coronary applications, and is therefore not feasible in a clinical setting. However, three-material decompositions with Gd contrast agents have the potential to provide SDNR that is ∼45% of that of DSA for matched patient air kerma. For two-material decompositions that suppress soft-tissue, ERA has the potential to produce images with SDNR that is 50%-75% of that of DSA for matched patient air kermas but lower levels of tube loading. Achieving these SDNR levels will require the use of analog charge summing for charge sharing correction, which increased SDNR by up to a factor of 1.7 depending on the contrast agent and whether or not a two-material or three-material decomposition was assumed.

CONCLUSIONS

We conclude that three-material ERA implemented with Gd contrast agents and two-material ERA implemented with either iodine or Gd contrast agents, should be investigated as alternatives to DSA in situations where motion artifacts preclude the use of DSA, such as in coronary imaging.

Spectral calibration of photon-counting detectors at high photon flux

BACKGROUND

Calibration of photon-counting detectors (PCDs) is necessary for quantitatively accurate spectral computed tomography (CT), but the calibration process can be complicated by nonlinear flux-dependent physical factors such as pulse pile-up.

PURPOSE

This work develops a method for spectral sensitivity calibration of a PCD-based spectral CT system that incorporates nonlinear flux dependence and can thus be employed at high photon flux.

METHODS

A calibration model for the spectral response and polynomial flux dependence is proposed, which incorporates prior x-ray source spectrum and PCD models and that has a small set of parameters for adjusting to the spectral CT system of interest. The model parameters are determined by fitting transmission data from a known object of known composition: a step-wedge phantom composed of different thicknesses of aluminum, a bone equivalent, and polymethyl methacrylate (PMMA), a soft-tissue equivalent. This fitting employs Tikhonov regularization, and the regularization strength and the polynomial order for the intensity modeling are determined by bias and variance analysis. The spectral calibration and nonlinear intensity correction is validated on transmission measurements through a third material, Teflon, at different x-ray photon flux levels.

RESULTS

The nonlinear intensity dependence is determined to be accurately accounted for with a third-order polynomial. The calibrated spectral CT model accurately predicts Teflon transmission to within 1% for flux levels up to 50% of the detector maximum.

CONCLUSIONS

The proposed PCD calibration method enables accurate physical modeling necessary for quantitative imaging in spectral CT. Furthermore, the model applies to high flux settings so that acquisition times will not be limited by restricting the spectral CT system to low flux levels.

XCIST-an open access x-ray/CT simulation toolkit

Objective. X-ray-based imaging modalities including mammography and computed tomography (CT) are widely used in cancer screening, diagnosis, staging, treatment planning, and therapy response monitoring. Over the past few decades, improvements to these modalities have resulted in substantially improved efficacy and efficiency, and substantially reduced radiation dose and cost. However, such improvements have evolved more slowly than would be ideal because lengthy preclinical and clinical evaluation is required. In many cases, new ideas cannot be evaluated due to the high cost of fabricating and testing prototypes. Wider availability of computer simulation tools could accelerate development of new imaging technologies. This paper introduces the development of a new open-access simulation environment for x-ray-based imaging. The main motivation of this work is to publicly distribute a fast but accurate ray-tracing x-ray and CT simulation tool along with realistic phantoms and 3D reconstruction capability, building on decades of developments in industry and academia.Approach. The x-ray-based Cancer Imaging Simulation Toolkit (XCIST) is developed in the context of cancer imaging, but can more broadly be applied. XCIST is physics-based, written in Python and C/C++, and currently consists of three major subsets: digital phantoms, the simulator itself (CatSim), and image reconstruction algorithms; planned future features include a fast dose-estimation tool and rigorous validation. To enable broad usage and to model and evaluate new technologies, XCIST is easily extendable by other researchers. To demonstrate XCIST's ability to produce realistic images and to show the benefits of using XCIST for insight into the impact of separate physics effects on image quality, we present exemplary simulations by varying contributing factors such as noise and sampling.Main results. The capabilities and flexibility of XCIST are demonstrated, showing easy applicability to specific simulation problems. Geometric and x-ray attenuation accuracy are shown, as well as XCIST's ability to model multiple scanner and protocol parameters, and to attribute fundamental image quality characteristics to specific parameters.Significance. This work represents an important first step toward the goal of creating an open-access platform for simulating existing and emerging x-ray-based imaging systems. While numerous simulation tools exist, we believe the combined XCIST toolset provides a unique advantage in terms of modeling capabilities versus ease of use and compute time. We publicly share this toolset to provide an environment for scientists to accelerate and improve the relevance of their research in x-ray and CT.

Ytterbium Nanoparticle Contrast Agents for Conventional and Spectral Photon-Counting CT and Their Applications for Hydrogel Imaging

Significant work has been done to develop nanoparticle contrast agents for computed tomography (CT), with a focus on identifying safer and more effective formulations. Contrast agents for spectral photon-counting computed tomography (SPCCT), a fast-growing imaging modality derived from conventional CT, have also recently gained considerable attention. In this study, we explored the synthesis of ultrasmall ytterbium nanoparticles (YbNP) and demonstrated that, potentially, they can be used as conventional CT and SPCCT contrast agents. These nanoparticles were tested in vitro for their cytotoxicity and contrast-generating properties with a variety of imaging systems. When scanned with conventional CT and SPCCT at clinically relevant energies, YbNP are significantly more attenuating than gold nanoparticles (AuNP), the contrast agents that have been most well studied. Furthermore, YbNP were studied for their potential application for labeling and monitoring hydrogels. The presence of the YbNP payload in hydrogels allowed for hydrogel localization and tracking in vivo. Additionally, the in vivo imaging results revealed that YbNP generate higher contrast when compared to AuNP used as a label. In summary, this is the first research study to examine ultrasmall YbNP as conventional CT and SPCCT contrast agents, as well as using them in a hydrogel system to make it radiopaque. These findings underscore YbNP's utility as CT and SPCCT contrast agents, as well as their potential for tracking hydrogels in vivo.

Photon-Counting Detectors in Computed Tomography: A Review

Photon-counting computed tomography (CT) is a new technique that has the potential to revolutionize clinical CT and is predicted to be the next significant advancement. In recent years, tremendous research has been conducted to demonstrate the developments in hardware assembly and its working principles. The articles in this review were obtained by conducting a search of the MEDLINE database. Photon-counting detectors (PCDs) provide excellent quality diagnostic images with high spatial resolution, reduced noise, artifacts, increased contrast-to-noise ratio, and multienergy data acquisition as compared with conventionally used energy-integrating detector (EID). The search covered articles published between 2011 and 2021. The title and abstract of each article were reviewed as determined by the search strategy. From these, eligible studies and articles that provided the working and clinical application of PCDs were selected. This article aims to provide a systematic review of the basic working principles of PCDs, emphasize the uses and clinical applications of PCDs, and compare it to EIDs. It provides a nonmathematical explanation and understanding of photon-counting CT systems for radiologists as well as clinicians.

One-step iterative reconstruction approach based on eigentissue decomposition for spectral photon-counting computed tomography

Purpose: We propose a one-step tissue characterization method for spectral photon-counting computed tomography (SPCCT) using eigentissue decomposition (ETD), tailored for highly accurate human tissue characterization in radiotherapy. Methods: The approach combines a Poisson likelihood, a spatial prior, and a quantitative prior constraining eigentissue fractions based on expected values for tabulated tissues. There are two regularization parameters: α for the quantitative prior, and β for the spatial prior. The approach is validated in a realistic simulation environment for SPCCT. The impact of α and β is evaluated on a virtual phantom. The framework is tested on a virtual patient and compared with two sinogram-based two-step methods [using respectively filtered backprojection (FBP) and an iterative method for the second step] and a post-reconstruction approach with the same quantitative prior. All methods use ETD. Results: Optimal performance with respect to bias or RMSE is achieved with different combinations of α and β on the cylindrical phantom. Evaluated in tissues of the virtual patient, the one-step framework outperforms two-step and post-reconstruction approaches to quantify proton-stopping power (SPR). The mean absolute bias on the SPR is 0.6% (two-step FBP), 0.6% (two-step iterative), 0.6% (post-reconstruction), and 0.2% (one-step optimized for low bias). Following the same order, the RMSE on the SPR is 13.3%, 2.5%, 3.2%, and 1.5%. Conclusions: Accurate and precise characterization with ETD can be achieved with noisy SPCCT data without the need to rely on post-reconstruction methods. The one-step framework is more accurate and precise than two-step methods for human tissue characterization.

Spectral Photon-Counting Computed Tomography: A Review on Technical Principles and Clinical Applications

Photon-counting computed tomography (CT) is a technology that has attracted increasing interest in recent years since, thanks to new-generation detectors, it holds the promise to radically change the clinical use of CT imaging. Photon-counting detectors overcome the major limitations of conventional CT detectors by providing very high spatial resolution without electronic noise, providing a higher contrast-to-noise ratio, and optimizing spectral images. Additionally, photon-counting CT can lead to reduced radiation exposure, reconstruction of higher spatial resolution images, reduction of image artifacts, optimization of the use of contrast agents, and create new opportunities for quantitative imaging. The aim of this review is to briefly explain the technical principles of photon-counting CT and, more extensively, the potential clinical applications of this technology.

Cardiovascular risk stratification by coronary computed tomography angiography imaging: current state-of-the-art

Current cardiovascular risk stratification by use of clinical risk score systems or plasma biomarkers is good but less than satisfactory in identifying patients at residual risk for coronary events. Recent clinical evidence puts now further emphasis on the role of coronary anatomy assessment by coronary computed tomography angiography (CCTA) for the management of patients with stable ischaemic heart disease. Available computed tomography (CT) technology allows the quantification of plaque burden, identification of high-risk plaques, or the functional assessment of coronary lesions for ischaemia detection and revascularization for refractory angina symptoms. The current CT armamentum is also further enhanced by perivascular Fat Attenuation Index (FAI), a non-invasive metric of coronary inflammation, which allows for the first time the direct quantification of the residual vascular inflammatory burden. Machine learning and radiomic features' extraction and spectral CT for tissue characterization are also expected to maximize the diagnostic and prognostic yield of CCTA. The combination of anatomical, functional, and biological information on coronary circulation by CCTA offers a unique toolkit for the risk stratification of patients, and patient selection for targeted aggressive prevention strategies. We hereby provide a review of the current state-of-the art in the field and discuss how integrating the full capacities of CCTA into clinical care pathways opens new opportunities for the tailored management of coronary artery disease.

Addressing CT metal artifacts using photon-counting detectors and one-step spectral CT image reconstruction

PURPOSE

The constrained one-step spectral CT image reconstruction (cOSSCIR) algorithm with a nonconvex alternating direction method of multipliers optimizer is proposed for addressing computed tomography (CT) metal artifacts caused by beam hardening, noise, and photon starvation. The quantitative performance of cOSSCIR is investigated through a series of photon-counting CT simulations.

METHODS

cOSSCIR directly estimates basis material maps from photon-counting data using a physics-based forward model that accounts for beam hardening. The cOSSCIR optimization framework places constraints on the basis maps, which we hypothesize will stabilize the decomposition and reduce streaks caused by noise and photon starvation. Another advantage of cOSSCIR is that the spectral data need not be registered, so that a ray can be used even if some energy window measurements are unavailable. Photon-counting CT acquisitions of a virtual pelvic phantom with low-contrast soft tissue texture and bilateral hip prostheses were simulated. Bone and water basis maps were estimated using the cOSSCIR algorithm and combined to form a virtual monoenergetic image for the evaluation of metal artifacts. The cOSSCIR images were compared to a "two-step" decomposition approach that first estimated basis sinograms using a maximum likelihood algorithm and then reconstructed basis maps using an iterative total variation constrained least-squares optimization (MLE+TV min $_{\text{min}}$ ). Images were also compared to a nonspectral TV min $_{\text{min}}$ reconstruction of the total number of counts detected for each ray with and without normalized metal artifact reduction (NMAR) applied. The simulated metal density was increased to investigate the effects of increasing photon starvation. The quantitative error and standard deviation in regions of the phantom were compared across the investigated algorithms. The ability of cOSSCIR to reproduce the soft-tissue texture, while reducing metal artifacts, was quantitatively evaluated.

RESULTS

Noiseless simulations demonstrated the convergence of the cOSSCIR and MLE+TV min $_{\text{min}}$ algorithms to the correct basis maps in the presence of beam-hardening effects. When noise was simulated, cOSSCIR demonstrated a quantitative error of -1 HU, compared to 2 HU error for the MLE+TV min $_{\text{min}}$ algorithm and -154 HU error for the nonspectral TV min $_{\text{min}}$ +NMAR algorithm. For the cOSSCIR algorithm, the standard deviation in the central iodine region of interest was 20 HU, compared to 299 HU for the MLE+TV min $_{\text{min}}$ algorithm, 41 HU for the MLE+TV min $_{\text{min}}$ +Mask algorithm that excluded rays through metal, and 55 HU for the nonspectral TV min $_{\text{min}}$ +NMAR algorithm. Increasing levels of photon starvation did not impact the bias or standard deviation of the cOSSCIR images. cOSSCIR was able to reproduce the soft-tissue texture when an appropriate regularization constraint value was selected.

CONCLUSIONS

By directly inverting photon-counting CT data into basis maps using an accurate physics-based forward model and a constrained optimization algorithm, cOSSCIR avoids metal artifacts due to beam hardening, noise, and photon starvation. The cOSSCIR algorithm demonstrated improved stability and accuracy compared to a two-step method of decomposition followed by reconstruction.

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

Background

Dual-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.

Methods

Seven 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.

Results

The 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.

Conclusions

Virtual 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.

Dual-energy CT: minimal essentials for radiologists

Dual-energy CT, the object is scanned at two different energies, makes it possible to identify the characteristics of materials that cannot be evaluated on conventional single-energy CT images. This imaging method can be used to perform material decomposition based on differences in the material-attenuation coefficients at different energies. Dual-energy analyses can be classified as image data-based- and raw data-based analysis. The beam-hardening effect is lower with raw data-based analysis, resulting in more accurate dual-energy analysis. On virtual monochromatic images, the iodine contrast increases as the energy level decreases; this improves visualization of contrast-enhanced lesions. Also, the application of material decomposition, such as iodine- and edema images, increases the detectability of lesions due to diseases encountered in daily clinical practice. In this review, the minimal essentials of dual-energy CT scanning are presented and its usefulness in daily clinical practice is discussed.

Photon-counting CT via interleaved/gapped spectral channels: Feasibility and imaging performance

PURPOSE

Compared to energy-integration, photon-counting x-ray detection facilitates the spectral channelization (energy binning) in spectral CT and thus offers the opportunity of implementing data acquisition via sophisticated schemes, e.g., gapping or interleaving in spectral channels. In this article, we report our investigation of the performance of material decomposition based spectral imaging in photon-counting CT implemented in such data acquisition schemes, and their comparison with the benchmark scheme and other schemes without spectral gapping or interleaving.

MATERIALS AND METHODS

Using a deliberately designed anthropomorphic head phantom that mimics the intracranial soft tissues and bony structures, a simulation study is carried out with the focus on two-material decomposition-based spectral imaging in photon-counting CT, under both ideal and realistic detector spectral responses. The projection data are acquired in four spectral channels, and then are sorted to implement the schemes of gapping ((ch₁ , ch₃ ); (ch₂ , ch₄ ); (ch₁ , ch₄ )) and interleaving ((ch₁ , ch₃ )+(ch₂ , ch₄ ); (ch₁ , ch₄ )+(ch₂ , ch₃ ); ((ch₁ +ch₃ ), (ch₂ +ch₄ )); ((ch₁ +ch₄ ), (ch₂ +ch₃ ))) in spectral channels, in addition to the benchmark scheme ((ch₁ +ch₂ ), (ch₃ +ch₄ )) and other conventional schemes (ch₁ , ch₂ ), (ch₂ , ch₃ ) and (ch₃ , ch₄ ), where 'ch' denotes channel, '+' denote addition, and (·,·) the operation of material decomposition and image reconstruction. Using the contrast-to-noise ratio between targeted regions of interest as the figure of merit, we study the performance of spectral imaging (material specific and virtual monochromatic) associated with these spectral channelization schemes.

RESULTS

Under ideal detector spectral response, the scheme (ch₁ , ch₄ ) outperforms the benchmark scheme ((ch₁ +ch₂ ), (ch₃ +ch₄ )) and others in gapped and/or interleaved spectral channelization in material specific imaging, while the interleaved scheme (ch₁ , ch₄ )+(ch₂ , ch₃ ) performs the best in virtual monochromatic imaging. Notably, only about half x-ray dose is utilized in the scheme (ch₁ , ch₄ ) for image formation. Under realistic detector spectral response, the difference in imaging performance over all spectral channelization schemes diminishes, along with degradation in each scheme's individual performance. The results suggest that (i) different strategy in spectral channelization may have to be exercised in material specific imaging and virtual monochromatic imaging, respectively, and (ii) the spectral distortion in realistic detector's response due to charge-sharing, Compton scattering and fluorescent escaping should be mitigated as much as possible.

CONCLUSION

The spectral channelization schemes and associated imaging performance reported herein are novel and thus informative to the community, which may further the understanding of physical fundamentals and design principles for material decomposition based spectral imaging in photon-counting CT and other x-ray related imaging modalities. This article is protected by copyright. All rights reserved.

Imaging of Dysfunctional Elastogenesis in Atherosclerosis Using an Improved Gadolinium-Based Tetrameric MRI Probe Targeted to Tropoelastin

Dysfunctional elastin turnover plays a major role in the progression of atherosclerotic plaques. Failure of tropoelastin cross-linking into mature elastin leads to the accumulation of tropoelastin within the growing plaque, increasing its instability. Here we present Gd₄-TESMA, an MRI contrast agent specifically designed for molecular imaging of tropoelastin within plaques. Gd₄-TESMA is a tetrameric probe composed of a tropoelastin-binding peptide (the VVGS-peptide) conjugated with four Gd(III)-DOTA-monoamide chelates. It shows a relaxivity per molecule of 34.0 ± 0.8 mM^-1 s^-1 (20 MHz, 298 K, pH 7.2), a good binding affinity to tropoelastin (K_D = 41 ± 12 μM), and a serum half-life longer than 2 h. Gd₄-TESMA accumulates specifically in atherosclerotic plaques in the ApoE^-/- murine model of plaque progression, with 2 h persistence of contrast enhancement. As compared to the monomeric counterpart (Gd-TESMA), the tetrameric Gd₄-TESMA probe shows a clear advantage regarding both sensitivity and imaging time window, allowing for a better characterization of atherosclerotic plaques.

Application of High-Z Gold Nanoparticles in Targeted Cancer Radiotherapy-Pharmacokinetic Modeling, Monte Carlo Simulation and Radiobiological Effect Modeling

High-Z gold nanoparticles (AuNPs) conjugated to a targeting antibody can help to improve tumor control in radiotherapy while simultaneously minimizing radiotoxicity to adjacent healthy tissue. This paper summarizes the main findings of a joint research program which applied AuNP-conjugates in preclinical modeling of radiotherapy at the Klinikum rechts der Isar, Technical University of Munich and Helmholtz Zentrum München. A pharmacokinetic model of superparamagnetic iron oxide nanoparticles was developed in preparation for a model simulating the uptake and distribution of AuNPs in mice. Multi-scale Monte Carlo simulations were performed on a single AuNP and multiple AuNPs in tumor cells at cellular and molecular levels to determine enhancements in the radiation dose and generation of chemical radicals in close proximity to AuNPs. A biologically based mathematical model was developed to predict the biological response of AuNPs in radiation enhancement. Although simulations of a single AuNP demonstrated a clear dose enhancement, simulations relating to the generation of chemical radicals and the induction of DNA strand breaks induced by multiple AuNPs showed only a minor dose enhancement. The differences in the simulated enhancements at molecular and cellular levels indicate that further investigations are necessary to better understand the impact of the physical, chemical, and biological parameters in preclinical experimental settings prior to a translation of these AuNPs models into targeted cancer radiotherapy.

A detective quantum efficiency for spectroscopic X-ray imaging detectors

PURPOSE

Spectroscopic X-ray detectors (SXDs) are under development for X-ray imaging applications. Recent efforts to extend the detective quantum efficiency (DQE) to SXDs impose a barrier to experimentation and/or do not provide a task-independent measure of detector performance. The purpose of this article is to define a task-independent DQE for SXDs that can be measured using a modest extension of established DQE-metrology methods.

METHODS

We defined a task-independent spectroscopic DQE and performed a simulation study to determine the relationship between the zero-frequency DQE and the ideal-observer signal-to-noise ratio (SNR) of low-frequency soft-tissue, bone, iodine, and gadolinium signals. In our simulations, we used calibrated models of the spatioenergetic response of cadmium telluride (CdTe) and cadmium-zinc-telluride (CdZnTe) SXDs. We also measured the zero-frequency DQE of a CdTe detector with two energy bins and of a CdZnTe detector with up to six energy bins for an RQA9 spectrum and compared with model predictions.

RESULTS

The spectroscopic DQE accounts for spectral distortions, energy-bin-dependent spatial resolution, interbin spatial noise correlations, and intrabin spatial noise correlations; it is mathematically equivalent to the squared SNR per unit fluence of the generalized least-squares estimate of the height of an X-ray impulse in a uniform noisy background. The zero-frequency DQE has a strong linear relationship with the ideal-observer SNR of low-frequency soft-tissue, bone, iodine, and gadolinium signals, and can be expressed in terms of the product of the quantum efficiency and a Swank noise factor that accounts for DQE degradation due to, for example, charge sharing (CS) and electronic noise. The spectroscopic Swank noise factor of the CdTe detector was measured to be 0.81 ± 0.04 and 0.83 ± 0.04 with and without anticoincidence logic for CS suppression, respectively. The spectroscopic Swank noise factor of the CdZnTe detector operated with four energy bins was measured to be 0.82 ± 0.02 which is within 5% of the theoretical value.

CONCLUSIONS

The spectroscopic DQE defined here is (1) task-independent, (2) can be measured using a modest extension of existing DQE-metrology methods, and (3) is predictive of the ideal-observer SNR of soft-tissue, bone, iodine, and gadolinium signals. For CT applications, the combination of CS and electronic noise in CdZnTe spectroscopic detectors will degrade the zero-frequency DQE by 10 %-20 % depending on the electronic noise level and pixel size.

Improving digital charge sharing compensation in photon counting detectors with a low-threshold comparator

PURPOSE

Charge sharing is a major non-ideality in photon counting detectors (PCDs) and can increase variance in material decomposition images. Analog charge summing (ACS) is an effective mechanism for charge sharing compensation (CSC), but is complex to implement and may limit the maximum count rate of the PCD. Digital CSC mechanisms such as digital count summing (DCS) may be simpler to implement; however, earlier simulation studies suggest that digital CSC only provides half the benefit of ACS. We propose including an additional low-threshold comparator (LTC) underneath the noise floor of the PCD to improve the effectiveness of digital CSC.

METHODS

We simulated a PCD with four or eight equally spaced energy bins. X-ray photons arrived on the PCD following a Poisson distribution, and charge was allocated to PCD pixels following Monte Carlo techniques. Gaussian electronic noise was added with standard deviation of 2 keV and the signals were processed with four CSC schemes: no CSC, ACS, DCS, and DCS with LTC. The energy bins were placed from 25 to 100 keV at 25 keV intervals (for four bins) or from 25 to 112.5 keV at 12.5 keV intervals (for eight bins), and the LTC threshold was placed at 8 keV in both cases. The binned counts were transformed into estimates of water and iodine material thickness using a linear estimator that was fitted to the data. Our simulations were performed in the low-flux limit without any pileup, assuming a 120 kVp spectrum, 25 cm water object, and 0.3 mm PCD pixel size.

RESULTS

All CSC schemes decreased variance in basis material decomposition. In the four-bin PCD, the relative dose efficiencies (inverse of the variance) for iodine material decomposition were 1.0, 2.4, 3.2, and 4.3 for a PCD without CSC, DCS without LTC, DCS with LTC, and ACS, respectively. In the eight-bin PCD, the relative dose efficiencies were 1.1, 2.5, 3.1, and 4.8, respectively. In a sensitivity analysis, electronic noise had a stronger deleterious effect on ACS than DCS. In simulated visual images, DCS and ACS improved high frequency contrast in material decomposition images.

CONCLUSIONS

Introducing an LTC may reduce the performance differential between DCS and ACS. These findings have been derived from simulation studies only and have not been validated experimentally.

Iterative material decomposition for spectral CT using self-supervised Noise2Noise prior

Compared to conventional computed tomography (CT), spectral CT can provide the capability of material decomposition, which can be used in many clinical diagnosis applications. However, the decomposed images can be very noisy due to the dose limit in CT scanning and the noise magnification of the material decomposition process. To alleviate this situation, we proposed an iterative one-step inversion material decomposition algorithm with a Noise2Noise prior. The algorithm estimated material images directly from projection data and used a Noise2Noise prior for denoising. In contrast to supervised deep learning methods, the designed Noise2Noise prior was built based on self-supervised learning and did not need external data for training. In our method, the data consistency term and the Noise2Noise network were alternatively optimized in the iterative framework, respectively, using a separable quadratic surrogate (SQS) and the Adam algorithm. The proposed iterative algorithm was validated and compared to other methods on simulated spectral CT data, preclinical photon-counting CT data and clinical dual-energy CT data. Quantitative analysis showed that our proposed method performs promisingly on noise suppression and structure detail recovery.

A data-driven maximum likelihood classification for nanoparticle agent identification in photon-counting CT

The nanoparticle agent, combined with a targeting factor reacting with lesions, enables specific CT imaging. Thus, the identification of the nanoparticle agents has the potential to improve clinical diagnosis. Thanks to the energy sensitivity of the photon-counting detector (PCD), it can exploit the K-edge of the nanoparticle agents in the clinical x-ray energy range to identify the agents. In this paper, we propose a novel data-driven approach for nanoparticle agent identification using the PCD. We generate two sets of training data consisting of PCD measurements from calibration phantoms, one in the presence of nanoparticle agent and the other in the absence of the agent. For a given sinogram of PCD counts, the proposed method calculates the normalized log-likelihood sinogram for each class (class 1: with the agent, class 2: without the agent) using theKnearest neighbors (KNN) estimator, backproject the sinograms, and compare the backprojection images to identify the agent. We also proved that the proposed algorithm is equivalent to the maximum likelihood-based classification. We studied the robustness of dose reduction with gold nanoparticles as the K-edge contrast media and demonstrated that the proposed method identifies targets with different concentrations of the agents without background noise.