Components of carotid atherosclerotic plaque in spectral photon-counting CT with histopathologic comparison.

OBJECTIVES: This study aimed to demonstrate the effectiveness of spectral photon-counting CT (SPCCT) in quantifying fibrous cap (FC) thickness, FC area, and lipid-rich necrotic core (LRNC) area, in excised carotid atherosclerotic plaques by comparing it with histopathological measurements. METHODS: This is a single-center ex vivo cross-sectional observational study. Excised plaques of 20 patients (71 +/- 6 years; 13 men), obtained from carotid endarterectomy were scanned with SPCCT using standardized acquisition settings (120k Vp/19 µA; 7-18 keV, 18-30 keV, 30-45 keV, 45-75 keV, and 75-118 keV). FC thickness, FC area, and LRNC area were quantified and compared between high-resolution 3D multi-energy CT images and histopathology using the Wilcoxon signed-ranks test and Bland-Altman analysis. Images were interpreted twice by two radiologists separately, blinded to the histopathology; inter- and intra-rater reliability were assessed with the intra-class correlation coefficients (ICC). RESULTS: FC thickness and FC area did not show significant differences between the SPCCT-derived radiological measurements versus the histopathological measurements (p value range 0.15-0.51 for FC thickness and 0.053-0.30 for FC area). For the LRNC area, the p value was statistically non-significant for reader 1 (range 0.36-0.81). The Bland-Altman analysis showed mean difference and 95% confidence interval for FC thickness, FC area, and LRNC area, 0.04 (-0.36 to 0.12) square root mm, -0.18 (-0.34 to -0.02) log10 mm2 and 0.10 (-0.088. to 0.009) log10 mm2 respectively. CONCLUSION: The result demonstrated a viable technique for quantifying FC thickness, FC area, and LRNC area due to the combined effect of high spatial and energy resolution of SPCCT. KEY POINTS: • SPCCT can identify and quantify different components of carotid atherosclerotic plaque in ex vivo study. • Components of atherosclerotic plaque did not show significant differences between the SPCCT-derived radiological measurements versus the histopathological measurements.

Carotid Artery Plaque Calcifications: Lessons From Histopathology to Diagnostic Imaging.

The role of calcium in atherosclerosis is controversial and the relationship between vascular calcification and plaque vulnerability is not fully understood. Although calcifications are present in ≈50% to 60% of carotid plaques, their association with cerebrovascular ischemic events remains unclear. In this review, we summarize current understanding of carotid plaque calcification. We outline the role of calcium in atherosclerotic carotid disease by analyzing laboratory studies and histopathologic studies, as well as imaging findings to understand clinical implications of carotid artery calcifications. Differences in mechanism of calcium deposition express themselves into a wide range of calcification phenotypes in carotid plaques. Some patterns, such as rim calcification, are suggestive of plaques with inflammatory activity with leakage of the vasa vasourm and intraplaque hemorrhage. Other patterns such as dense, nodular calcifications may confer greater mechanical stability to the plaque and reduce the risk of embolization for a given degree of plaque size and luminal stenosis. Various distributions and patterns of carotid plaque calcification, often influenced by the underlying systemic pathological condition, have a different role in affecting plaque stability. Modern imaging techniques afford multiple approaches to assess geometry, pattern of distribution, size, and composition of carotid artery calcifications. Future investigations with these novel technologies will further improve our understanding of carotid artery calcification and will play an important role in understanding and minimizing stroke risk in patients with carotid plaques.

Deep learning based spectral CT imaging.

Spectral computed tomography (CT) has attracted much attention in radiation dose reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray energy spectrum is divided into several bins, each energy-bin-specific projection has a low signal-noise-ratio (SNR) than the current-integrating counterpart, which makes image reconstruction a unique challenge. Traditional wisdom is to use prior knowledge based iterative methods. However, this kind of methods demands a great computational cost. Inspired by deep learning, here we first develop a deep learning based reconstruction method; i.e., U-net with Lpp-norm, Total variation, Residual learning, and Anisotropic adaption (ULTRA). Specifically, we emphasize the various multi-scale feature fusion and multichannel filtering enhancement with a denser connection encoding architecture for residual learning and feature fusion. To address the image deblurring problem associated with the L22- loss, we propose a general Lpp-loss, p>0. Furthermore, the images from different energy bins share similar structures of the same object, the regularization characterizing correlations of different energy bins is incorporated into the Lpp- loss function, which helps unify the deep learning based methods with traditional compressed sensing based methods. Finally, the anisotropically weighted total variation is employed to characterize the sparsity in the spatial-spectral domain to regularize the proposed network In particular, we validate our ULTRA networks on three large-scale spectral CT datasets, and obtain excellent results relative to the competing algorithms. In conclusion, our quantitative and qualitative results in numerical simulation and preclinical experiments demonstrate that our proposed approach is accurate, efficient and robust for high-quality spectral CT image reconstruction.

Multi-energy spectral photon-counting computed tomography (MARS) for detection of arthroplasty implant failure.

To determine whether state-of-the-art multi-energy spectral photon-counting computed tomography (MARS) can detect knee arthroplasty implant failure not detected by standard pre-operative imaging techniques. A total knee arthroplasty (TKA) removed from a patient was reviewed. The extracted prosthesis [NexGen Legacy Posterior Stabilized (LPS) TKA] was analyzed as were pre-operative imaging examination and compared with a MARS-CT examination obtained of the extracted TKA prosthesis. Radiographs, fluoroscopy, ultrasound and MRI preoperatively did not reveal the cause of the implant failure. MARS CT images of the extracted prosthesis clearly showed the presence of posteromedial polyethylene and tibial tray wear which is compatible with the clinical appearance of the extracted TKA. MARS can identify polyethylene insert and metallic tibial tray wear as a cause of TKA failure, that could not be identified with on standard pre-operative imaging. Although clinical MARS CT system is still under development, this case does illustrate its potential clinical usefulness. This is the first study to document how MARS CT imaging can detect orthopedic implant failure not detected by standard current imaging techniques. This system has a potential clinical application in orthopedic patients.

Quantitative imaging performance of MARS spectral photon-counting CT for radiotherapy.

PURPOSE: To evaluate the quantitative imaging performance of a spectral photon-counting computed tomography (SPCCT) scanner for radiotherapy applications. An experimental comparison of the quantitative performance of a Siemens dual-energy CT (DECT) and a MARS SPCCT scanner is performed to estimate physical properties relevant to radiotherapy of human substitute materials and contrast agent solutions. In human substitute materials, the accuracy of quantities relevant to photon therapy, proton therapy, and Monte-Carlo simulations, such as the electron density, proton stopping power, and elemental composition is evaluated. For contrast agent solutions, the accuracy of the contrast agent concentrations and the virtual non-contrast (VNC) electron density is evaluated. METHODS: Human tissue substitute phantoms (Gammex 467 and 472) as well as diluted solutions of contrast agents (iodine and gadolinium based) are scanned with two commercial systems: a Siemens dual-source CT (SOMATOM Definition Flash, Siemens Healthineers, Forchheim, Germany) and a MARS spectral photon-counting micro-CT (MARS V5.2, MARS Bioimaging Ltd., Christchurch, New Zealand). Material decomposition is performed in a maximum a posteriori framework with an optimized material basis tailored to characterize either human substitute materials or contrast agents in the context of experimental multi-energy CT data. RESULTS: The root-mean-square error (RMSE) of the electron density calculated over all Gammex inserts is reduced from 1.09 to 0.89% when going from DECT to SPCCT. For the proton stopping power, the RMSE is reduced from 1.92 to 0.89%. Elemental mass fractions of hydrogen, carbon, nitrogen, oxygen, and calcium are more accurately estimated with the MARS scanner. The RMSE on the iodine-based contrast agents concentration is reduced from 0.27 to 0.12 mg/mL with SPCCT, and the VNC electron density from 0.40 to 0.22%. CONCLUSION: In the present phantom study, a MARS photon-counting scanner provides superior accuracy compared to a Siemens SOMATOM Definition Flash DECT scanner to quantify physical parameters relevant to radiotherapy. This work experimentally demonstrates the benefits of using more energies to characterize human tissue equivalent materials. This highlights the potential of SPCCT for particle therapy, where more accurate tissue characterization is needed, as well as for Monte-Carlo based planning, which requires accurate elemental mass fractions.

Increased separability of K-edge nanoparticles by photon-counting detectors for spectral micro-CT.

BACKGROUND: X-ray CT/micro-CT methods with photon-counting detectors (PCDs) and high Z materials are a hot research topic. One method using PCDs allows for spectral imaging in 5 energy windows while conventional X-ray detectors only collect energy-integrating data. OBJECTIVE: To demonstrate the enhanced separation of contrast materials by using PCDs, multivariate analysis, and linear discriminant methods. METHODS: Phantoms containing iodine and aqueous nanomaterials were scanned on a MARS spectral micro-CT. Image volumes were segmented into separate material-specific populations. Contrast comparisons were made by calculating T2 test statistics in the univariate, pseudo-conventional and multivariate, spectral CT data sets. Separability after Fisher discriminant analysis (FDA) was also assessed. RESULTS: The T2 values calculated for material comparisons increased as a result of the spectral expansion. The majority of the tested contrast agents showed increased T2 values by a factor of ∼2 -3. The total significant T2 statistics in the pure and mixed lanthanide image sets increased in the spectral data set. CONCLUSION: This work consolidates the groundwork for photon-counting-based material decomposition with micro-CT, facilitating future development of novel nanomaterials and their preclinical applications.

Beam profile assessment in spectral CT scanners.

In this paper, we present a method that uses a combination of experimental and modeled data to assess properties of x-ray beam measured using a small-animal spectral scanner. The spatial properties of the beam profile are characterized by beam profile shape, the angular offset along the rotational axis, and the photon count difference between experimental and modeled data at the central beam axis. Temporal stability of the beam profile is assessed by measuring intra- and interscan count variations. The beam profile assessment method was evaluated on several spectral CT scanners equipped with Medipix3RX-based detectors. On a well-calibrated spectral CT scanner, we measured an integral count error of 0.5%, intrascan count variation of 0.1%, and an interscan count variation of less than 1%. The angular offset of the beam center ranged from 0.8° to 1.6° for the studied spectral CT scanners. We also demonstrate the capability of this method to identify poor performance of the system through analyzing the deviation of the experimental beam profile from the model. This technique can, therefore, aid in monitoring the system performance to obtain a robust spectral CT; providing the reliable quantitative images. Furthermore, the accurate offset parameters of a spectral scanner provided by this method allow us to incorporate a more realistic form of the photon distribution in the polychromatic-based image reconstruction models. Both improvements of the reliability of the system and accuracy of the volume reconstruction result in a better discrimination and quantification of the imaged materials.

A Hybrid 2D/3D User Interface for Radiological Diagnosis.

This paper presents a novel 2D/3D desktop virtual reality hybrid user interface for radiology that focuses on improving 3D manipulation required in some diagnostic tasks. An evaluation of our system revealed that our hybrid interface is more efficient for novice users and more accurate for both novice and experienced users when compared to traditional 2D only interfaces. This is a significant finding because it indicates, as the techniques mature, that hybrid interfaces can provide significant benefit to image evaluation. Our hybrid system combines a zSpace stereoscopic display with 2D displays, and mouse and keyboard input. It allows the use of 2D and 3D components interchangeably, or simultaneously. The system was evaluated against a 2D only interface with a user study that involved performing a scoliosis diagnosis task. There were two user groups: medical students and radiology residents. We found improvements in completion time for medical students, and in accuracy for both groups. In particular, the accuracy of medical students improved to match that of the residents.

Quantitative imaging of excised osteoarthritic cartilage using spectral CT.

OBJECTIVES: To quantify iodine uptake in articular cartilage as a marker of glycosaminoglycan (GAG) content using multi-energy spectral CT. METHODS: We incubated a 25-mm strip of excised osteoarthritic human tibial plateau in 50 % ionic iodine contrast and imaged it using a small-animal spectral scanner with a cadmium telluride photon-processing detector to quantify the iodine through the thickness of the articular cartilage. We imaged both spectroscopic phantoms and osteoarthritic tibial plateau samples. The iodine distribution as an inverse marker of GAG content was presented in the form of 2D and 3D images after applying a basis material decomposition technique to separate iodine in cartilage from bone. We compared this result with a histological section stained for GAG. RESULTS: The iodine in cartilage could be distinguished from subchondral bone and quantified using multi-energy CT. The articular cartilage showed variation in iodine concentration throughout its thickness which appeared to be inversely related to GAG distribution observed in histological sections. CONCLUSIONS: Multi-energy CT can quantify ionic iodine contrast (as a marker of GAG content) within articular cartilage and distinguish it from bone by exploiting the energy-specific attenuation profiles of the associated materials. KEY POINTS: • Contrast-enhanced articular cartilage and subchondral bone can be distinguished using multi-energy CT. • Iodine as a marker of glycosaminoglycan content is quantifiable with multi-energy CT. • Multi-energy CT could track alterations in GAG content occurring in osteoarthritis.