Measuring Identification and Quantification Errors in Spectral CT Material Decomposition

Targeted K-Edge Nanoprobes From Praseodymium and Hafnium for Ratiometric Tracking of Dual Biomarkers using Spectral Photon Counting CT

Utilizing metal nanoprobes with unique K-edge identities to visualize complementary biological activities simultaneously can provide valuable information about complex biological processes. This study describes the design and preparation of an innovative pair of K-edge metal nanoprobes and demonstrates the feasibility of their simultaneous quantitative detection using spectral photon-counting computed tomography (SPCCT). Glycosaminoglycan (GAG) capped nanoparticles (ca. 15-20 nm) targeting two distinct components of the cartilage tissue, namely, aggrecan (acan) and aggrecanase (acanase) are designed and synthesized. These targeted nanoparticles comprised of praseodymium (Pr) and hafnium (Hf), with well-separated K-edge energies, enable simultaneous and ratiometric imaging of dual biomarkers in cartilage tissue. Following extensive physico-chemical characterization of the ligand-targeted particles, the feasibility of homing dual biomarkers in vitro is demonstrated. The material discrimination and simultaneous quantification of these targeted particles are also achieved and corroborated with inductively coupled plasmon spectroscopy. For the first time, the use of praseodymium is reported as a contrast agent for SPCCT imaging and demonstrates the ability to pair it with hafnium nanoprobes for multicontrast imaging of diseases. Importantly, the potential for ratiometric molecular imaging and tracking of osteoarthritis (OA) progression is shown with SPCCT K-edge based imaging approach.

Ex-vivo atherosclerotic plaque characterization using spectral photon-counting CT: Comparing material quantification to histology

BACKGROUND AND AIMS

Atherosclerotic plaques are characterized as being vulnerable to rupture based on a series of histologically defined features, including a lipid-rich necrotic core, spotty calcification and ulceration. Existing imaging modalities have limitations in their ability to distinguish between different materials and structural features. We examined whether X-ray spectral photon-counting computer tomography (SPCCT) images were able to distinguish key plaque features in a surgically excised specimen from the carotid artery with comparison to histological images.

METHODS

An excised carotid plaque was imaged in the diagnostic X-ray energy range of 30-120 keV using a small-bore SPCCT scanner equipped with a Medipix3RX photon-counting spectral X-ray detector with a cadmium telluride (CdTe) sensor. Material identification and quantification (MIQ) images of the carotid plaque were generated using proprietary MIQ software at 0.09 mm volumetric pixels (voxels). The plaque was sectioned, stained and photographed at high resolution for comparison.

RESULTS

A lipid-rich core with spotty calcification was identified in the MIQ images and confirmed by histology. MIQ showed a core region containing lipid, with a mean concentration of 260 mg lipid/ml corresponding to a mean value of -22HU. MIQ showed calcified regions with mean concentration of 41 mg Ca/ml corresponded to a mean value of 123HU. An ulceration of the carotid wall at the bifurcation was identified to be lipid-lined, with a small calcification identified near the breach of the artery wall.

CONCLUSIONS

SPCCT derived material identification and quantification images showed hallmarks of vulnerable plaque including a lipid-rich necrotic core, spotty calcifications and ulcerations.

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.

Characterization of arterial plaque composition with dual energy computed tomography: a simulation study

To investigate the feasibility of quantifying the chemical composition of coronary artery plaque in terms of water, lipid, protein, and calcium contents using dual-energy computed tomography (CT) in a simulation study. A CT simulation package was developed based on physical parameters of a clinical CT scanner. A digital thorax phantom was designed to simulate coronary arterial plaques in the range of 2-5 mm in diameter. Both non-calcified and calcified plaques were studied. The non-calcified plaques were simulated as a mixture of water, lipid, and protein, while the calcified plaques also contained calcium. The water, lipid, protein, and calcium compositions of the plaques were selected to be within the expected clinical range. A total of 95 plaques for each lesion size were simulated using the CT simulation package at 80 and 135 kVp. Half-value layer measurements were made to make sure the simulated dose was within the range of clinical dual energy scanning protocols. Dual-energy material decomposition using a previously developed technique was performed to determine the volumetric fraction of water, lipid, protein, and calcium contents in each plaque. For non-calcified plaque, the total volume conservation provides the third constrain for three-material decomposition with dual energy CT. For calcified plaque, a fourth criterion was introduced from a previous report suggesting a linear correlation between water and protein contents in soft tissue. For non-calcified plaque, the root mean-squared error (RMSE) of the image-based decomposition was estimated to be 0.7%, 1.5%, and 0.3% for water, lipid, and protein contents, respectively. As for the calcified plaques, the RMSE of the 5 mm plaques were estimated to be 5.6%, 5.7%, 0.2%, and 3.1%, for water, lipid, calcium, and protein contents, respectively. The RMSE increases as the plaque size reduces. The simulation results indicate that chemical composition of coronary arterial plaques can be quantified using dual-energy CT. By accurately quantifying the content of a coronary plaque lesion, our decomposition method may provide valuable insight for the assessment and stratification of coronary artery disease.

Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network

Hyperspectral X-ray CT (HXCT) technology provides not only structural imaging but also the information of material components therein. The main purpose of this study is to investigate the effect of various reconstruction algorithms on reconstructed X-ray absorption spectra (XAS) of components shown in the CT image by means of HXCT. In this paper, taking 3D printing polymer as an example, seven kinds of commonly used polymers such as thermoplastic elastomer (TPE), carbon fiber reinforced polyamide (PA-CF), acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), ultraviolet photosensitive resin (UV9400), polyethylene terephthalate glycol (PETG), and polyvinyl alcohol (PVA) were selected as samples for hyperspectral CT reconstruction experiments. Seven kinds of 3D printing polymer and two interfering samples were divided into a training set and test sets. First, structural images of specimens were reconstructed by Filtered Back-Projection (FBP), Algebra Reconstruction Technique (ART) and Maximum-Likelihood Expectation-Maximization (ML-EM). Secondly, reconstructed XAS were extracted from the pixels of region of interest (ROI) compartmentalized in the images. Thirdly, the results of principal component analysis (PCA) demonstrated that the first four principal components contain the main features of reconstructed XAS, so we adopted Artificial Neural Network (ANN) trained by the reconstructed XAS expressed by the first four principal components in the training set to identify that the XAS of corresponding polymers exist in both of test sets from the training set. The result of ANN displays that FBP has the best performance of classification, whose ten-fold cross-validation accuracy reached 99%. It suggests that hyperspectral CT reconstruction is a promising way of getting image features and material features at the same time, which can be used in medical imaging and nondestructive testing.

Spectral Photon-Counting Molecular Imaging for Quantification of Monoclonal Antibody-Conjugated Gold Nanoparticles Targeted to Lymphoma and Breast Cancer: An In Vitro Study

The purpose of the present study was to demonstrate an in vitro proof of principle that spectral photon-counting CT can measure gold-labelled specific antibodies targeted to specific cancer cells. A crossover study was performed with Raji lymphoma cancer cells and HER2-positive SKBR3 breast cancer cells using a MARS spectral CT scanner. Raji cells were incubated with monoclonal antibody-labelled gold, rituximab (specific antibody to Raji cells), and trastuzumab (as a control); HER2-positive SKBR3 breast cancer cells were incubated with monoclonal antibody-labelled gold, trastuzumab (specific antibody to HER2-positive cancer cells), and rituximab (as a control). The calibration vials with multiple concentrations of nonfunctionalised gold nanoparticles were used to calibrate spectral CT. Spectral imaging results showed that the Raji cells-rituximab-gold and HER2-positive cells-trastuzumab-gold had a quantifiable amount of gold, 5.97 mg and 0.78 mg, respectively. In contrast, both cell lines incubated with control antibody-labelled gold nanoparticles had less gold attached (1.22 mg and 0.15 mg, respectively). These results demonstrate the proof of principle that spectral molecular CT imaging can identify and quantify specific monoclonal antibody-labelled gold nanoparticles taken up by Raji cells and HER2-positive SKBR3 breast cancer cells. The present study reports the future potential of spectral molecular imaging in detecting tumour heterogeneity so that treatment can be tuned accordingly, leading to more effective personalised medicine.