Spectral Photon-Counting Computed Tomography: Technical Principles and Applications in the Assessment of Cardiovascular Diseases

Spectral Photon-Counting Computed Tomography (SPCCT) represents a groundbreaking advancement in X-ray imaging technology. The core innovation of SPCCT lies in its photon-counting detectors, which can count the exact number of incoming x-ray photons and individually measure their energy. The first part of this review summarizes the key elements of SPCCT technology, such as energy binning, energy weighting, and material decomposition. Its energy-discriminating ability represents the key to the increase in the contrast between different tissues, the elimination of the electronic noise, and the correction of beam-hardening artifacts. Material decomposition provides valuable insights into specific elements' composition, concentration, and distribution. The capability of SPCCT to operate in three or more energy regimes allows for the differentiation of several contrast agents, facilitating quantitative assessments of elements with specific energy thresholds within the diagnostic energy range. The second part of this review provides a brief overview of the applications of SPCCT in the assessment of various cardiovascular disease processes. SPCCT can support the study of myocardial blood perfusion and enable enhanced tissue characterization and the identification of contrast agents, in a manner that was previously unattainable.

Updates on the Applications of Spectral Computed Tomography for Musculoskeletal Imaging

Spectral CT represents a novel imaging approach that can noninvasively visualize, quantify, and characterize many musculoskeletal pathologies. This modality has revolutionized the field of radiology by capturing CT attenuation data across multiple energy levels and offering superior tissue characterization while potentially minimizing radiation exposure compared to traditional enhanced CT scans. Despite MRI being the preferred imaging method for many musculoskeletal conditions, it is not viable for some patients. Moreover, this technique is time-consuming, costly, and has limited availability in many healthcare settings. Thus, spectral CT has a considerable role in improving the diagnosis, characterization, and treatment of gout, inflammatory arthropathies, degenerative disc disease, osteoporosis, occult fractures, malignancies, ligamentous injuries, and other bone-marrow pathologies. This comprehensive review will delve into the diverse capabilities of dual-energy CT, a subset of spectral CT, in addressing these musculoskeletal conditions and explore potential future avenues for its integration into clinical practice.

Reproducible spectral CT thermometry with liver-mimicking phantoms for image-guided thermal ablation

Objectives. Evaluate the reproducibility, temperature tolerance, and radiation dose requirements of spectral CT thermometry in tissue-mimicking phantoms to establish its utility for non-invasive temperature monitoring of thermal ablations.Methods. Three liver mimicking phantoms embedded with temperature sensors were individually scanned with a dual-layer spectral CT at different radiation dose levels during heating (35 °C-80 °C). Physical density maps were reconstructed from spectral results using varying reconstruction parameters. Thermal volumetric expansion was then measured at each temperature sensor every 5 °C in order to establish a correlation between physical density and temperature. Linear regressions were applied based on thermal volumetric expansion for each phantom, and coefficient of variation for fit parameters was calculated to characterize reproducibility of spectral CT thermometry. Additionally, temperature tolerance was determined to evaluate effects of acquisition and reconstruction parameters. The resulting minimum radiation dose to meet the clinical temperature accuracy requirement was determined for each slice thickness with and without additional denoising.Results. Thermal volumetric expansion was robustly replicated in all three phantoms, with a correlation coefficient variation of only 0.43%. Similarly, the coefficient of variation for the slope and intercept were 9.6% and 0.08%, respectively, indicating reproducibility of the spectral CT thermometry. Temperature tolerance ranged from 2 °C to 23 °C, decreasing with increased radiation dose, slice thickness, and iterative reconstruction level. To meet the clinical requirement for temperature tolerance, the minimum required radiation dose ranged from 20, 30, and 57 mGy for slice thickness of 2, 3, and 5 mm, respectively, but was reduced to 2 mGy with additional denoising.Conclusions. Spectral CT thermometry demonstrated reproducibility across three liver-mimicking phantoms and illustrated the clinical requirement for temperature tolerance can be met for different slice thicknesses. The reproducibility and temperature accuracy of spectral CT thermometry enable its clinical application for non-invasive temperature monitoring of thermal ablation.

Clinical application of spectral CT perfusion scanning in evaluating the blood supply source of portal vein tumor thrombus in hepatocellular carcinoma

Purpose

To evaluate the characteristic of blood supply of liver portal vein tumor thrombus (PVTT) using perfusion indexes and spectral parameters.

Methods

Between July 2020 and December 2022, the study enrolled 25 liver cancer patients completed with PVTT (male=20, female=5; age 41-74 years (59.48 ± 9.12)) from the Interventional Department of Jiangsu Cancer Hospital. There were 11 cases of type III PVTT, 12 of type II PVTT, and 2 of type I PVTT (Cheng's classification). All patients underwent spectral perfusion scans through dual-layer spectral detector computed tomography. The PVTTs were divided into proximal and distal groups based on the distance between the tumor thrombus and the main portal vein. The perfusion analysis was performed on the 120-kVp conventional images to generate hepatic perfusion index (HPI). The spectral based images (SBIs) during the artery and venous peak phases were extracted from the perfusion data. The iodine map and 40&100-keV virtual monoenergetic image (VMI) were generated from SBI data. HPI, iodine concentration (IC), CT value at 40 and 100-keV, and spectral slope (40-100keV) of the primary lesion, proximal and distal PVTT, and liver parenchyma were measured and compared. The correlation between the primary lesion and proximal and distal PVTT was analyzed.

Results

The IC and spectral slope during the arterial and venous peak phases and HPI of the primary lesion, proximal PVTT, and distal PVTT were highly correlated (P<0.001). The differences between the IC and spectral slope during the arterial and venous peak phases and HPI of the primary lesion, proximal PVTT were statistically significant (P<0.001). The differences between the IC during venous peak phase and HPI of primary lesion, distal PVTT were statistically significant (P<0.001), and there was no statistically significant difference in arterial phase IC, arterial and venous phase spectral slopes.

Conclusion

The IC, slope, and HPI of the distal and proximal PVTT were highly correlated with the primary lesion, indicating that PVTT was similar to the primary lesion in the liver that they were both mainly supplied by the hepatic artery. However, there was still significant heterogeneity between the proximal PVTT and the primary lesion, while the difference in the distal PVTT was relatively small.

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.

Spectral Dual-Layer Computed Tomography Can Predict the Invasiveness of Ground-Glass Nodules: A Diagnostic Model Combined with Thymidine Kinase-1

OBJECTIVES

Few studies have explored the use of spectral dual-layer detector-based computed tomography (SDCT) parameters, thymidine kinase-1 (TK1), and tumor abnormal protein (TAP) for the detection of ground-glass nodules (GGNs). Therefore, we aimed to evaluate the quantitative and qualitative parameters generated from SDCT for predicting the pathological subtypes of GGN-featured lung adenocarcinoma combined with TK1 and TAP.

MATERIAL AND METHODS

Between July 2021 and September 2022, 238 patients with GGNs were retrospectively enrolled in this study. SDCT and tests for TK1 and TAP were performed preoperatively, and the lesions were divided into glandular precursor lesions (PGL), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC), according to the pathological results. A receiver operating characteristic (ROC) curve was used to compare the diagnostic performance of these parameters. Multivariate logistic regression analysis was performed to construct a joint diagnostic model and create a nomogram.

RESULTS

This study included 238 GGNs, including 41 atypical adenomatous hyperplasias (AAH), 62 adenocarcinomas in situ (AIS), 49 MIA, and 86 IAC, with a high proportion of women, non-smokers, and pure ground-glass nodule (pGGN). CT100 keV (a/v), electronic density (EDW) (a/v), Daverage, Dsolid, TK1, and TAP of MIA and IAC were higher than those of PGL. The effective atomic number (Zeff (a/v)) was lower in MIA and IAC than in PGL (all p < 0.05). Logistic regression analysis showed that Zeff (a), EDW (a), TK1, Daverage, and internal bronchial morphology were crucial factors in predicting the aggressiveness of GGN. Zeff (a) had the highest diagnostic performance with an area under the ROC curve (AUC) = 0.896, followed by EDW (a) (AUC = 0.838) and CT100 keVa (AUC = 0.819). The diagnostic model and nomogram constructed using these five parameters (Zeff (a) + EDW (a) + CT100 keVa + Daverage + TK1) had an AUC = 0.933, which was higher than the individual parameters (p < 0.05).

CONCLUSIONS

Multiple quantitative and functional parameters can be selected based on SDCT, especially Zeff (a) and EDW (a), which have high sensitivity and specificity for predicting GGNs' invasiveness. Additionally, the combination of TK1 can further improve diagnostic performance, and using a nomogram is helpful for individualized predictions.

Spectral Computed Tomography-Derived Iodine Content and Tumor Response in the Follow-Up of Neuroendocrine Tumors-A Single-Center Experience

Spectral computed tomography (SCT) allows iodine content (IC) calculation for characterization of hypervascularized neoplasms and thus might help in the staging of neuroendocrine tumors (NETs). This single-center prospective study analyzed the association between SCT-derived IC and tumor response in the follow-up of metastasized NETs. Twenty-six patients with a median age of 70 years (range 51-85) with histologically proven NETs and a total of 78 lesions underwent SCT for staging. Because NETS are rare, no primary NET types were excluded. Lesions and intralesional hotspots were measured in virtual images and iodine maps. Tumor response was classified as progressive or nonprogressive at study endpoint. Generalized estimating equations served to estimate associations between IC and tumor response, additionally stratified by lesion location. Most commonly affected sites were the lymph nodes, liver, pancreas, and bones. Median time between SCT and endpoint was 64 weeks (range 5-260). Despite statistical imprecision in the estimate, patients with higher IC in lymphonodular metastases had lower odds for disease progression (adjusted OR = 0.21, 95% CI: 0.02-2.02). Opposite tendencies were observed in hepatic and pancreatic metastases in unadjusted analyses, which vanished after adjusting for therapy and primary tumor grade.

Study on the scanning protocols for measuring bone mineral density by gemstone CT spectral imaging based on European spine phantom

BACKGROUND

Gemstone spectral computed tomography (GSCT) has been used to measure bone mineral density (BMD) in human vertebrae and animal models gradually.

PURPOSE

To investigate the effect of scanning protocols for BMD measurements by GSCT using the European spine phantom (ESP) and its accuracy and precision.

MATERIAL AND METHODS

The ESP number 145 containing three hydroxyapatite (HAP) inserts with densities of 50, 100, and 200 mg/cm³ were labeled as L₁, L₂, and L₃, respectively. Quantitative CT (QCT) protocol and 14 groups of scanning protocols configured by GSCT were used to repeatedly scan the ESP 10 times. Their measurements were compared with the true values of ESP and their relative standard deviation and relative error were calculated.

RESULTS

The measured values of the three inserts at different exposure levels were statistically significant (P < 0.05). The measured values in the 0.8 s/r 260 mA group, 0.5 s/r 630 mA group, and 0.6 s/r 640 mA group were not significantly different from the actual ESP values for L₁ and L₂. However, the measured values at all the parameters were significantly different from the actual values for the L₃.

CONCLUSION

CT gemstone spectral imaging can accurately and quantitatively measure the HAP value of ESP, but the results of BMD will be affected by the scanning protocols. The best scanning parameter of ESP measured by GSCT was 0.8 s/r 260 mA, taking dose into consideration, and the measurement accuracy of vertebrae with low BMD was higher than that of QCT under this parameter.

Association between quantitative spectral CT parameters, Ki-67 expression, and invasiveness in lung adenocarcinoma manifesting as ground-glass nodules

BACKGROUND

Few studies about lung ground-glass nodules (GGNs) have been done using non-enhancement spectral computed tomography (CT) imaging.

PURPOSE

To examine the association between spectral CT parameters, Ki-67 expression, and invasiveness in lung adenocarcinoma manifesting as GGNs.

MATERIAL AND METHODS

Spectral CT parameters were analyzed in 106 patients with lung GGNs. The Ki-67 labeling index (Ki-67 LI) was measured, and patients were divided into low expression and high expression groups according to the number of positive-stained cells (low expression ≤10%; high expression >10%). Spectral CT parameters were compared between low and high expression groups. The correlation between spectral CT parameters and Ki-67 LI was estimated by Spearman correlation analysis. Cases were divided into a preinvasive and minimally invasive adenocarcinoma (MIA) group (atypical adenomatous hyperplasia, adenocarcinoma in situ, and MIA) and invasive adenocarcinoma (IA) group. Spectral CT parameters were compared between the two groups. The diagnostic performance was evaluated using receiver operating characteristic analysis.

RESULTS

There were significant differences in water concentration of lesions (WCL) and monochromatic CT values between the low and high expression groups. CT 40 keV had the highest correlation coefficient with Ki-67 LI. WCL and monochromatic CT values were significantly higher in the IA group than in the pre/MIA group. The value of area under the curve of CT 40 keV was 0.946 (95% confidence interval=0.905-0.988) for differentiating the two groups; the cutoff was -280.66 Hu.

CONCLUSION

Spectral CT is an effective non-invasive method for the prediction of proliferation and invasiveness in lung adenocarcinoma manifesting as GGNs.

Hepatic dual-contrast CT imaging: slow triple kVp switching CT with CNN-based sinogram completion and material decomposition

Purpose: Dual-contrast protocols are a promising clinical multienergy computed tomography (CT) application for focal liver lesion detection and characterization. One avenue to enable multienergy CT is the introduction of photon-counting detectors (PCD). Although clinical translation is highly desired because of the diagnostic benefits of PCDs, it will still be a decade or more before they are broadly available. In our work, we investigated an alternative solution that can be implemented on widely used conventional CT systems (single source and integrating detector) to perform multimaterial spectral decomposition for dual-contrast imaging. Approach: We propose to slowly alternate the x-ray tube voltage between 3 kVp levels so each kVp level covers a few degrees of gantry rotation. This leads to the challenge of sparsely sampled projection data in each energy level. Performing the material decomposition (MD) in the sinogram domain is not directly possible as the projection images of the three energy levels are not angularly aligned. In order to overcome this challenge, we developed a convolutional neural network (CNN) framework for sparse sinogram completion (SC) and MD. To evaluate the feasibility of the slow kVp switching scheme, simulation studies of an abdominal phantom, which included liver lesions, were conducted. Results: The line-integral SC network yielded sinograms with a pixel-wise RMSE < 0.05 of the line-integrals compared to the ground truth. This provided acceptable image quality up to a switching angle of 9 deg per kVp. The MD network we developed allowed us to differentiate iodine and gadolinium in the sinogram domain. The average relative quantification errors for iodine and gadolinium were below 10%. Conclusions: We developed a slow triple kVp switching data acquisition scheme and a CNN-based data processing pipeline. Results from a digital phantom validation illustrate the potential for future applications of dual-contrast agent protocols on practically available single-energy CT systems.

Dual-Energy Heart CT: Beyond Better Angiography-Review

Heart CT has undergone substantial development from the use of calcium scores performed on electron beam CT to modern 256+-row CT scanners. The latest big step in its evolution was the invention of dual-energy scanners with much greater capabilities than just performing better ECG-gated angio-CT. In this review, we present the unique features of dual-energy CT in heart diagnostics.

Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning

The spectral computed tomography (CT) has huge advantages by providing accurate material information. Unfortunately, due to the instability or overdetermination of material decomposition model, the accuracy of material decomposition can be compromised in practice. Very recently, the dictionary learning based image-domain material decomposition (DLIMD) can obtain high accuracy for material decompositions from reconstructed spectral CT images. This method can explore the correlation of material components to some extent by training a unified dictionary from all material images. In addition, the dictionary learning based prior as a penalty is applied on material components independently, and many parameters would be carefully elaborated in practice. Because the concentration of contrast agent in clinical applications is low, it can result in data inconsistency for dictionary based representation during the iteration process. To avoid the aforementioned limitations and further improve the accuracy of materials, we first construct a generalized dictionary learning based image-domain material decomposition (GDLIMD) model. Then, the material tensor image is unfolded along the mode-1 to enhance the correlation of different materials. Finally, to avoid the data inconsistency of low iodine contrast, a normalization strategy is employed. Both physical phantom and tissue-synthetic phantom experiments demonstrate the proposed GDLIMD method outperforms the DLIMD and direct inversion (DI) methods.

Harnessing X-Ray Energy-Dependent Attenuation of Bismuth-Based Nanoprobes for Accurate Diagnosis of Liver Fibrosis

Timely detection of liver fibrosis by X-ray computed tomography (CT) can prevent its progression to fatal liver diseases. However, it remains quite challenging because conventional CT can only identify the difference in density instead of X-ray attenuation characteristics. Spectral CT can generate monochromatic imaging to specify X-ray attenuation characteristics of the scanned matter. Herein, an X-ray energy-dependent attenuation strategy originated from bismuth (Bi)-based nanoprobes (BiF3 @PDA@HA) is proposed for the accurate diagnosis of liver fibrosis. Bi element in BiF3 @PDA@HA can exhibit characteristic attenuation depending on different levels of X-ray energy via spectral CT, and that is challenging for conventional CT. In this study, selectively accumulating BiF3 @PDA@HA nanoprobes in the hepatic fibrosis areas can significantly elevate CT value for 40 Hounsfield units on 70 keV monochromatic images, successfully differentiating from healthy livers and achieving the diagnosis of liver fibrosis. Furthermore, the enhancement produced by the BiF3 @PDA@HA nanoprobes in vivo increases as the monochromatic energy decreases from 70 to 40 keV, optimizing the conspicuity of the diseased areas. As a proof of concept, the strategically designed nanoprobes with energy-dependent attenuation characteristics not only expand the scope of CT application, but also hold excellent potential for precise imaging-based disease diagnosis.

Dual-Energy CT Follow-Up After Stroke Thrombolysis Alters Assessment of Hemorrhagic Complications

Background and Purpose: We aimed to determine whether dual-energy CT (DECT) follow-up can differentiate contrast staining (CS) from intracranial hemorrhage (ICH) in stroke patients treated with intravenous thrombolysis (IVT), who had undergone acute stroke imaging using CT angiography (CTA), and CT perfusion (CTP).

Materials and Methods: Between November 2012 and January 2018, 168 patients at our comprehensive stroke center underwent DECT follow-up within 36 h after IVT and acute CTA with or without CTP but did not receive intra-arterial imaging or treatment. Two independent readers evaluated plain monochromatic CT (pCT) alone and compared this with a second reading of a combined DECT approach using pCT and water- and iodine-weighted images, establishing and grading the ICH diagnosis, per Heidelberg and Safe Implementation of Treatments in Stroke Monitoring Study (SITS-MOST) classifications.

Results: On pCT alone within 36 h, 31/168 (18.5%) patients had findings diagnosed as ICH. Using combined DECT (cDECT) changed ICH diagnosis to “CS only” in 3/168 (1.8%) patients, constituting 3/31 (9.7%) of cases with initially pCT-diagnosed ICH. These three cases had pCT diagnoses of one SAH, one minor, and one more extensive petechial hemorrhage (hemorrhagic infarction types 1 and 2), respectively. pCT alone had a 100% sensitivity, 98% specificity, 90% positive predictive value (PPV), 100% negative predictive value (NPV), and 98% accuracy for any ICH, compared to the cDECT. Inter-reader agreement for ICH classification using pCT compared to DECT was weighted kappa 0.92 (95% CI 0.87–0.98) vs. 0.91 (0.85–0.95).

Conclusion: Compared to pCT, DECT within 36 h after IV thrombolysis for acute ischemic stroke, changes the radiological diagnosis of post-treatment ICH to “CS only” in a small proportion of patients. Studies are warranted of whether the altered radiological reports have an impact on patient management, for example initiation timing of antithrombotic secondary prevention.

Stepwise analysis of potential accuracy-influencing factors of iodine quantification on a fast kVp-switching second-generation dual-energy CT: from 3D-printed phantom to a simple solution in clinical routine use

Background

Measurement of iodine concentration from dual-energy or spectral computed tomography (CT) provides useful diagnostic information especially in patients suffering from malignant tumors of various origins.

Purpose

The purpose of this study was to systematically investigate the accuracy of the measurement of iodine concentration, focusing on potential influencing factors and assessing its suitability for routine clinical use.

Material and Methods

First, a 3D-printed cylindrical phantom was used to assess reliability of dual-energy CT-based iodine concentration measurement. Second, a semi-anthropomorphic phantom was used to evaluate the potential impact of positional variation of the target volume as typically seen in clinical scans. Finally, a reference vial was placed on the body surface of 38 patients undergoing abdominal dual-energy CT to analyze correlations between applied doses and patient diameters.

Results

The position of the target volume within the cylindrical phantom and the applied dose level significantly influenced the magnitude of measured iodine concentrations ( P < 0.001). We also found a significant difference in accuracy depending on target volume position in the semi-anthropomorphic phantom ( P = 0.028). In patient scans, we observed an error of 19.6 ± 5.6% in iodine concentration measurements of a reference and significant, moderate to strong, negative correlations between measured iodine concentration, maximum patient diameter, and applied dose (maximum sagittal diameter: r = −0.455, P = 0.004; maximum coronal diameter: r=−0.517, P = 0.001; CTDIvol: r = −0.385, P = 0.017)

Conclusion

Dual-energy CT-based iodine concentration measurement should be interpreted with caution. In clinical examinations, placement of a reference vial could be a potential solution to relativize errors.

Block matching frame based material reconstruction for spectral CT

Spectral computed tomography (CT) has a great potential in material identification and decomposition. To achieve high-quality material composition images and further suppress the x-ray beam hardening artifacts, we first propose a one-step material reconstruction model based on Taylor's first-order expansion. Then, we develop a basic material reconstruction method named material simultaneous algebraic reconstruction technique (MSART). Considering the local similarity of each material image, we incorporate a powerful block matching frame (BMF) into the material reconstruction (MR) model and generate a BMF based MR (BMFMR) method. Because the BMFMR model contains the L 0-norm problem, we adopt a split-Bregman method for optimization. The numerical simulation and physical phantom experiment results validate the correctness of the material reconstruction algorithms and demonstrate that the BMF regularization outperforms the total variation and no-local mean regularizations.

Fully connected neural network for virtual monochromatic imaging in spectral computed tomography

Spectral computed tomography (SCT) has advantages in multienergy material decomposition for material discrimination and quantitative image reconstruction. However, due to the nonideal physical effects of photon counting detectors, including charge sharing, pulse pileup and K -escape, it is difficult to obtain precise system models in practical SCT systems. Serious spectral distortion is unavoidable, which introduces error into the decomposition model and affects material decomposition accuracy. Recently, neural networks demonstrated great potential in image segmentation, object detection, natural language processing, etc. By adjusting the interconnection relationship among internal nodes, it provides a way to mine information from data. Considering the difficulty in modeling SCT system spectra and the superiority of data-driven characteristics of neural networks, we proposed a spectral information extraction method for virtual monochromatic attenuation maps using a simple fully connected neural network without knowing spectral information. In our method, virtual monochromatic linear attenuation coefficients can be obtained directly through our neural network, which could contribute to further material recognition. Our method also provides outstanding performance on denoising and artifacts suppression. It can be furnished for SCT systems with different settings of energy bins or thresholds. Various substances available can be used for training. The trained neural network has a good generalization ability according to our results. The testing mean square errors are about 1 × 10 - 05    cm - 2 .

Spatial-Spectral Cube Matching Frame for Spectral CT Reconstruction

Spectral computed tomography (CT) reconstructs the same scanned object from projections of multiple narrow energy windows, and it can be used for material identification and decomposition. However, the multi-energy projection dataset has a lower signal-noise-ratio (SNR), resulting in poor reconstructed image quality. To address this thorny problem, we develop a spectral CT reconstruction method, namely spatial-spectral cube matching frame (SSCMF). This method is inspired by the following three facts: i) human body usually consists of two or three basic materials implying that the reconstructed spectral images have a strong sparsity; ii) the same basic material component in a single channel image has similar intensity and structures in local regions. Different material components within the same energy channel share similar structural information; iii) multi-energy projection datasets are collected from the subject by using different narrow energy windows, which means images reconstructed from different energy-channels share similar structures. To explore those information, we first establish a tensor cube matching frame (CMF) for a BM4D denoising procedure. Then, as a new regularizer, the CMF is introduced into a basic spectral CT reconstruction model, generating the SSCMF method. Because the SSCMF model contains an L0-norm minimization of 4D transform coefficients, an effective strategy is employed for optimization. Both numerical simulations and realistic preclinical mouse studies are performed. The results show that the SSCMF method outperforms the state-of-the-art algorithms, including the simultaneous algebraic reconstruction technique, total variation minimization, total variation plus low rank, and tensor dictionary learning.

Overcoming detector limitations of x-ray photon counting for preclinical microcomputed tomography

Spectral computed tomography (CT) using photon counting detectors (PCDs) can provide accurate tissue composition measurements by utilizing the energy dependence of x-ray attenuation in different materials. PCDs are especially suited for K-edge imaging, revealing the spatial distribution of select imaging probes through quantitative material decomposition. We report on a prototype spectral micro-CT system with a CZT-based PCD (DxRay, Inc.) that has 16 × 16    pixels of 0.5 × 0.5    mm 2 , a thickness of 3 mm, and four energy thresholds. Due to the PCD's limited size ( 8 × 8    mm 2 ), our system uses a translate-rotate projection acquisition strategy to cover a field of view relevant for preclinical imaging ( ∼ 4.5    cm ). Projection corrections were implemented to minimize artifacts associated with dead pixels and projection stitching. A sophisticated iterative algorithm was used to reconstruct both phantom and ex vivo mouse data. To achieve preclinically relevant spatial resolution, we trained a convolutional neural network to perform pan-sharpening between low-resolution PCD data ( 247 - μ m voxels) and high-resolution energy-integrating detector data ( 82 - μ m voxels), recovering a high-resolution estimate of the spectral contrast suitable for material decomposition. Long-term, preclinical spectral CT systems such as ours could serve in the developing field of theranostics (therapy and diagnostics) for cancer research.

Association between spectral computed tomography images and clinicopathological features in advanced gastric adenocarcinoma

To investigate the role of spectral computed tomography (CT)-generated iodine concentration (IC) in the evaluation of clinicopathological features of advanced gastric adenocarcinoma (AGC), 42 patients who underwent abdominal enhanced CT with spectral imaging mode were selected for the present study. The IC of the primary lesion in the arterial phase (IC_AP) and portal venous phase (IC_VP) was measured and the IC of the aorta was used for a normalized iodine concentration (nIC). Micro-vessel density (MVD) and lymphatic vessel density (LVD) were detected using immunohistochemical assays against cluster of differentiation 34 and D2-40, respectively. Other clinicopathological characteristics were also documented. The IC parameters were revealed to be significantly increased in the high-MVD group, particularly for the nIC_VP (P=0.002). Additionally, the nIC_AP revealed a significant difference (P=0.041) between the high- and low-LVD group. The nIC_AP and nIC_VP were increased in the poorly differentiated group compared with the moderately differentiated group (P=0.040 and P=0.011, respectively). The ICs and MVD demonstrated a statistically significant positive linear correlation. nIC_VP was able to be used to discriminate between the moderately and poorly differentiated carcinomas, with an area under the receiver operating characteristic curve of 0.759. However, IC demonstrated no correlation with serosal involvement, lymph node metastasis, LVD, and nodular or metastatic tumors. The results of the present study suggest that the nIC_VP value may serve as a non-invasive marker for the angiogenesis of, and the differentiations between, patients with AGC.

Numerical observer for atherosclerotic plaque classification in spectral computed tomography

Spectral computed tomography (SCT) generates better image quality than conventional computed tomography (CT). It has overcome several limitations for imaging atherosclerotic plaque. However, the literature evaluating the performance of SCT based on objective image assessment is very limited for the task of discriminating plaques. We developed a numerical-observer method and used it to assess performance on discrimination vulnerable-plaque features and compared the performance among multienergy CT (MECT), dual-energy CT (DECT), and conventional CT methods. Our numerical observer was designed to incorporate all spectral information and comprised two-processing stages. First, each energy-window domain was preprocessed by a set of localized channelized Hotelling observers (CHO). In this step, the spectral image in each energy bin was decorrelated using localized prewhitening and matched filtering with a set of Laguerre-Gaussian channel functions. Second, the series of the intermediate scores computed from all the CHOs were integrated by a Hotelling observer with an additional prewhitening and matched filter. The overall signal-to-noise ratio (SNR) and the area under the receiver operating characteristic curve (AUC) were obtained, yielding an overall discrimination performance metric. The performance of our new observer was evaluated for the particular binary classification task of differentiating between alternative plaque characterizations in carotid arteries. A clinically realistic model of signal variability was also included in our simulation of the discrimination tasks. The inclusion of signal variation is a key to applying the proposed observer method to spectral CT data. Hence, the task-based approaches based on the signal-known-exactly/background-known-exactly (SKE/BKE) framework and the clinical-relevant signal-known-statistically/background-known-exactly (SKS/BKE) framework were applied for analytical computation of figures of merit (FOM). Simulated data of a carotid-atherosclerosis patient were used to validate our methods. We used an extended cardiac-torso anthropomorphic digital phantom and three simulated plaque types (i.e., calcified plaque, fatty-mixed plaque, and iodine-mixed blood). The images were reconstructed using a standard filtered backprojection (FBP) algorithm for all the acquisition methods and were applied to perform two different discrimination tasks of: (1) calcified plaque versus fatty-mixed plaque and (2) calcified plaque versus iodine-mixed blood. MECT outperformed DECT and conventional CT systems for all cases of the SKE/BKE and SKS/BKE tasks (all [Formula: see text]). On average of signal variability, MECT yielded the SNR improvements over other acquisition methods in the range of 46.8% to 65.3% (all [Formula: see text]) for FBP-Ramp images and 53.2% to 67.7% (all [Formula: see text]) for FBP-Hanning images for both identification tasks. This proposed numerical observer combined with our signal variability framework is promising for assessing material characterization obtained through the additional energy-dependent attenuation information of SCT. These methods can be further extended to other clinical tasks such as kidney or urinary stone identification applications.

Optimization of beam quality for photon-counting spectral computed tomography in head imaging: simulation study

Head computed tomography (CT) plays an important role in the comprehensive evaluation of acute stroke. Photon-counting spectral detectors, as promising candidates for use in the next generation of x-ray CT systems, allow for assigning more weight to low-energy x-rays that generally contain more contrast information. Most importantly, the spectral information can be utilized to decompose the original set of energy-selective images into several basis function images that are inherently free of beam-hardening artifacts, a potential advantage for further improving the diagnosis accuracy. We are developing a photon-counting spectral detector for CT applications. The purpose of this work is to determine the optimal beam quality for material decomposition in two head imaging cases: nonenhanced imaging and K-edge imaging. A cylindrical brain tissue of 16-cm diameter, coated by a 6-mm-thick bone layer and 2-mm-thick skin layer, was used as a head phantom. The imaging target was a 5-mm-thick blood vessel centered in the head phantom. In K-edge imaging, two contrast agents, iodine and gadolinium, with the same concentration ([Formula: see text]) were studied. Three parameters that affect beam quality were evaluated: kVp settings (50 to 130 kVp), filter materials ([Formula: see text] to 83), and filter thicknesses [0 to 2 half-value layer (HVL)]. The image qualities resulting from the varying x-ray beams were compared in terms of two figures of merit (FOMs): squared signal-difference-to-noise ratio normalized by brain dose ([Formula: see text]) and that normalized by skin dose ([Formula: see text]). For nonenhanced imaging, the results show that the use of the 120-kVp spectrum filtered by 2 HVL copper ([Formula: see text]) provides the best performance in both FOMs. When iodine is used in K-edge imaging, the optimal filter is 2 HVL iodine ([Formula: see text]) and the optimal kVps are 60 kVp in terms of [Formula: see text] and 75 kVp in terms of [Formula: see text]. A tradeoff of 65 kVp was proposed to lower the potential risk of skin injuries if a relatively long exposure time is necessarily performed in the iodinated imaging. In the case of gadolinium imaging, both SD and BD can be minimized at 120 kVp filtered with 2 HVL thulium ([Formula: see text]). The results also indicate that with the same concentration and their respective optimal spectrum, the values of [Formula: see text] and [Formula: see text] in gadolinium imaging are, respectively, around 3 and 10 times larger than those in iodine imaging. However, since gadolinium is used in much lower concentrations than iodine in the clinic, iodine may be a preferable candidate for K-edge imaging.

Comparison of gastric vascular anatomy by monochromatic and polychromatic dual-energy spectral computed tomography imaging

OBJECTIVE

To evaluate the use of monochromatic and polychromatic dual-energy spectral computed tomography (CT) imaging for preoperative assessment of gastric vascular anatomy.

METHODS

Patients with suspected gastric cancer underwent spectral CT to generate conventional 140  kVp polychromatic and monochromatic images with energy levels ranging from 40 to 140  keV during the late arterial and portal venous phases. Optimal monochromatic images were selected according to the contrast-to-noise ratio (CNR) for the gastric artery. Image quality was subjectively assessed. Display rates of the arteries were recorded.

RESULTS

The study included 64 patients. Monochromatic images at 53 ± 3  keV provided the optimum CNR. At this energy level, subjective image scores were significantly higher for monochromatic images than polychromatic images. There were no significant differences in the display rates of arteries between polychromatic and optimal monochromatic images.

CONCLUSIONS

Monochromatic images obtained with spectral CT can improve the visualization of gastric arteries.

Quantitative iodine-based material decomposition images with spectral CT imaging for differentiating prostatic carcinoma from benign prostatic hyperplasia

RATIONALE AND OBJECTIVES

To investigate the value of iodine-based material decomposition images produced via spectral computed tomography (CT) in differentiating prostate cancer (PCa) from benign prostate hyperplasia (BPH).

MATERIALS AND METHODS

Fifty-six male patients underwent CT examination with spectral imaging during arterial phase (AP), venous phase (VP), and parenchymal phase (PP) of enhancement. Iodine concentrations of lesions were measured and normalized to that of the obturator internus muscle. Lesion CT values at 75 keV (corresponding to the energy of polychromatic images at 120 kVp) were measured and also normalized; their differences between AP and VP, VP and PP, and PP and AP were also obtained. The two-sample t-test was performed for comparisons. A receiver operating characteristic curve was generated to establish the threshold for normalized iodine concentration (NIC).

RESULTS

Fifty-two peripheral lesions were found, which were confirmed by biopsy as 28 cases of PCa and 24 BPHs. The NICs of prostate cancers significantly differed from those of the BPHs: 2.38 ± 1.72 compared with 1.21 ± 0.72 in AP, respectively, and 2.67 ± 0.61 compared with 2.27 ± 0.77 in VP. Receiver operating characteristic analysis indicated that an NIC of 1.24 in the AP provided a sensitivity of 88% and a specificity of 71% for differentiating PCa from BPH.

CONCLUSIONS

Spectral CT imaging enabled quantitative depiction of contrast medium uptake in prostatic lesions and improved sensitivity and specificity for differentiating PCa from BPH.