Dual- and multi-energy CT: approach to functional imaging

Influence of object size on beam hardening in dual energy images: A study using different dual-energy CT systems

INTRODUCTION

Dual-energy CT (DECT) enables material decomposition and artifact reduction. However, beam hardening effects, which vary by DECT system and object size, can impact measurement accuracy. This study investigates the influence of beam hardening across various DECT systems and object sizes.

METHODS

A polyethylene Mercury phantom with five diameters (16, 21, 26, 31, and 36 cm) was scanned using three DECT systems: fast kilovolt-switching CT (FKSCT), dual-source CT (DSCT), and dual-layer CT (DLCT). Measurements included CT numbers and standard deviations (SD) of virtual monochromatic images (VMI) at 70 keV for iodine inserts, iodine concentrations, and artifact indices (AI) to assess beam hardening artifacts.

RESULTS

CT numbers and iodine concentrations decreased with increasing phantom size for FKSCT and DLCT, with DLCT showing a larger decrease. DSCT exhibited relatively stable CT numbers and iodine concentrations across all sizes. Noise levels (SD) increased significantly with phantom size for DSCT and DLCT, while FKSCT showed a smaller increase. Beam hardening artifacts, as assessed by AI, were the lowest for FKSCT, while DSCT and DLCT exhibited greater artifacts compared to FKSCT, particularly at larger phantom sizes.

CONCLUSION

The effect of beam hardening varies among DECT systems. FKSCT demonstrated the most stable performance across object sizes, while DSCT and DLCT were more sensitive to object size, affecting measurement accuracy and stability. These findings emphasize the importance of understanding system-specific characteristics to ensure optimal DECT use.

IMPLICATIONS FOR PRACTICE

In clinical practice, when using DECT to measure CT numbers and iodine concentration, it is important to understand that the size of the object may be affected by beam hardening, depending on the DECT system.

Advances in Novel Diagnostic Techniques for Alveolar Echinococcosis

Alveolar echinococcosis (AE), caused by the larval stage of the tapeworm Echinococcus multilocularis, is a serious parasitic disease that presents significant health risks and challenges for both patients and healthcare systems. Accurate and timely diagnosis is essential for effective management and improved patient outcomes. This review summarizes the latest diagnostic methods for AE, focusing on serological tests and imaging techniques such as ultrasonography (US), computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT). Each imaging modality has its strengths and limitations in detecting and characterizing AE lesions, such as their location, size, and invasiveness. US is often the first-line method due to its non-invasiveness and cost-effectiveness, but it may have limitations in assessing complex lesions. CT provides detailed anatomical information and is particularly useful for assessing bone involvement and calcification. MRI, with its excellent soft tissue contrast, is superior for delineating the extent of AE lesions and their relationship to adjacent structures. PET/CT combines functional and morphological imaging to provide insights into the metabolic activity of lesions, which is valuable for monitoring treatment response and detecting recurrence. Overall, this review emphasizes the importance of a multifaceted diagnostic approach that combines serological and imaging techniques for accurate and early AE diagnosis, which is crucial for effective management and improved patient outcomes.

Stacked Scintillators Based Multispectral X-Ray Imaging Featuring Quantum-Cutting Perovskite Scintillators With 570 nm Absorption-Emission Shift

Traditional energy-integration X-ray imaging systems rely on total X-ray intensity for image contrast, ignoring energy-specific information. Recently developed multilayer stacked scintillators have enabled multispectral, large-area flat-panel X-ray imaging (FPXI), enhancing material discrimination capabilities. However, increased layering can lead to mutual excitation, which may affect the accurate discrimination of X-ray energy. This issue is tackled by proposing a novel design strategy utilizing rare earth ions doped quantum-cutting scintillators as the top layer. These scintillators create new luminescence centers via energy transfer, resulting in a significantly larger absorption-emission shift, as well as the potential to double the photoluminescence quantum yield (PLQY) and enhance light output. To verify this concept, a three-layer stacked scintillator detector is developed using ytterbium ions (Yb3+)-doped CsPbCl3 perovskite nanocrystals (PeNCs) as the top layer, which offers a high PLQY of over 100% and a significant absorption-emission shift of 570 nm. This configuration, CsAgCl2 and Cs3Cu2I5 as the middle and bottom layers, respectively, ensures non-overlapping optical absorption and radioluminescence (RL) emission spectra. By calculating the optimal thickness for each layer to absorb specific X-ray energies, the detector demonstrates distinct absorption differences across various energy bands, enhancing the identification of materials with similar densities.

Virtual Monochromatic Imaging of Half-Iodine-Load, Contrast-Enhanced Computed Tomography with Deep Learning Image Reconstruction in Patients with Renal Insufficiency: A Clinical Pilot Study

BACKGROUND

We retrospectively examined image quality (IQ) of thin-slice virtual monochromatic imaging (VMI) of half-iodine-load, abdominopelvic, contrast-enhanced CT (CECT) by dual-energy CT (DECT) with deep learning image reconstruction (DLIR).

METHODS

In 28 oncology patients with moderate-to-severe renal impairment undergoing half-iodine-load (300 mgI/kg) CECT by DECT during the nephrographic phase, we reconstructed VMI at 40-70 keV with a slice thickness of 0.625 mm using filtered back-projection (FBP), hybrid iterative reconstruction (HIR), and DLIR; measured contrast-noise ratio (CNR) of the liver, spleen, aorta, portal vein, and prostate/uterus; and determined the optimal keV to achieve the maximal CNR. At the optimal keV, two independent radiologists compared each organ's CNR and subjective IQ scores among FBP, HIR, and DLIR to subjectively grade image noise, contrast, sharpness, delineation of small structures, and overall IQ.

RESULTS

CNR of each organ increased continuously from 70 to 40 keV using FBP, HIR, and DLIR. At 40 keV, CNR of the prostate/uterus was significantly higher with DLIR than with FBP; however, CNR was similar between FBP and HIR and between HIR and DLIR. The CNR of all other organs increased significantly from FBP to HIR to DLIR (P < 0.05). All IQ scores significantly improved from FBP to HIR to DLIR (P < 0.05) and were acceptable in all patients with DLIR only.

CONCLUSIONS

The combination of 40 keV and DLIR offers the maximal CNR and a subjectively acceptable IQ for thin-slice VMI of half-iodine-load CECT.

In vitro blood sample assessment: investigating correlation of laboratory hemoglobin and spectral properties of dual-energy CT measurements (ρ/Z)

OBJECTIVES

Our study comprised a single-center retrospective in vitro correlation between spectral properties, namely ρ/Z values, derived from scanning blood samples using dual-energy computed tomography (DECT) with the corresponding laboratory hemoglobin/hematocrit (Hb/Hct) levels and assessed the potential in anemia-detection.

METHODS

DECT of 813 patient blood samples from 465 women and 348 men was conducted using a standardized scan protocol. Electron density relative to water (ρ or rho), effective atomic number (Zeff), and CT attenuation (Hounsfield unit) were measured.

RESULTS

Positive correlation with the Hb/Hct was shown for ρ (r-values 0.37-0.49) and attenuation (r-values 0.59-0.83) while no correlation was observed for Zeff (r-values -0.04 to 0.08). Significant differences in attenuation and ρ values were detected for blood samples with and without anemia in both genders (p value < 0.001) with area under the curve ranging from 0.7 to 0.95. Depending on the respective CT parameters, various cutoff values for CT-based anemia detection could be determined.

CONCLUSION

In summary, our study investigated the correlation between DECT measurements and Hb/Hct levels, emphasizing novel aspects of ρ and Zeff values. Assuming that quantitative changes in the number of hemoglobin proteins might alter the mean Zeff values, the results of our study show that there is no measurable correlation on the atomic level using DECT. We established a positive in vitro correlation between Hb/Hct values and ρ. Nevertheless, attenuation emerged as the most strongly correlated parameter with identifiable cutoff values, highlighting its preference for CT-based anemia detection.

CLINICAL RELEVANCE STATEMENT

By scanning multiple blood samples with dual-energy CT scans and comparing the measurements with standard laboratory blood tests, we were able to underscore the potential of CT-based anemia detection and its advantages in clinical practice.

KEY POINTS

Prior in vivo studies have found a correlation between aortic blood pool and measured hemoglobin and hematocrit. Hemoglobin and hematocrit correlated with electron density relative to water and attenuation but not Zeff. Dual-energy CT has the potential for additional clinical benefits, such as CT-based anemia detection.

Dual-energy computed tomography: pediatric considerations

Multidetector computed tomography (CT) has revolutionized medicine and is now a fundamental aspect of modern radiology. Hardware and software advancements have significantly improved CT accessibility, image quality, and acquisition times. While considerable attention has been directed towards the potential risks of ionizing radiation from CT scans in children, recent concerns regarding the possible short- and long-term risks related to magnetic resonance imaging (MRI) conducted under general anesthesia have generated fresh interest in novel pediatric CT applications and techniques that allow imaging of awake patients at low radiation doses. Among these novel techniques, dual-energy CT (DECT) stands out for its ability to provide enhanced diagnostic information, reduce radiation doses further, and facilitate faster scans, making it a highly promising tool in pediatric radiology. This manuscript explores the current role of DECT in pediatric imaging, emphasizing its technical foundations, hardware configurations, and various reconstruction techniques. We discuss advanced post-processing techniques, such as material decomposition algorithms and virtual monoenergetic imaging, highlighting their clinical advantages in improving diagnostic accuracy and patient outcomes. Furthermore, the paper reviews the clinical applications of DECT in evaluating pulmonary perfusion, cardiovascular assessments, and oncologic imaging in pediatric patients.

Correlation between CT spectral quantitative parameters and expression levels of HIF-1α and ALX1 in non-small cell lung cancer

To detect the expression levels of hypoxia inducible factor-1alpha (HIF-1α) and aristaless-like homeobox 1 (ALX1) in non-small cell lung cancer and analyze the relationship between CT spectral quantitative parameters and immunohistochemical markers, in order to evaluate the biological characteristics of lung cancer by spectral CT. Spectral CT data and paraffin masses of 50 adult patients with lung cancer were collected. CT quantitative parameters including the slope of spectral curve, effective atomic number and iodine concentration in enhanced phases were acquired. Expression levels of HIF-1α and ALX1 were detected by immunohistochemical tests, and compared between different pathological types and differentiation grades of tumor cells. CT quantitative parameters at different expression levels of HIF-1α and ALX1 were compared, respectively. The relationship between CT quantitative parameters and expression levels of HIF-1α and ALX1 were analyzed. There was no significant difference of expression levels of HIF-1α and ALX1 between adenocarcinoma and squamous cell carcinoma. Expression levels of HIF-1α among different differentiation grades of tumor cells had significant difference (χ2 = 27.100, P < .001), while without significant difference in ALX1 expression. CT spectral parameters had significant difference among expression levels of HIF-1α and ALX1 (P < .01). There was a positive correlation between each CT spectral parameter and the expression level of immunohistochemical markers. CT spectral quantitative parameters are significantly different among expression levels of immunohistochemical markers. The positive correlation between CT quantitative parameter and expression level of immunohistochemical markers suggests CT spectral imaging could predict biological characteristics of tumors.

Feasibility study of photon-counting CT for material identification based on YSO/SiPM detector: A proof of concept

BACKGROUND

Current photon-counting computed tomography (CT) systems utilize semiconductor detectors, such as cadmium telluride (CdTe), cadmium zinc telluride (CZT), and silicon (Si), which convert x-ray photons directly into charge pulses. An alternative approach is indirect detection, which involves Yttrium Orthosilicate (YSO) scintillators coupled with silicon photomultipliers (SiPMs). This presents an attractive and cost-effective option due to its low cost, high detection efficiency, low dark count rate, and high sensor gain.

OBJECTIVE

This study aims to establish a comprehensive quantitative imaging framework for three-energy-bin proof-of-concept photon-counting CT based on YSO/SiPM detectors developed in our group using multi-voltage threshold (MVT) digitizers and assess the feasibility of this spectral CT for material identification.

METHODS

We developed a proof-of-concept YSO/SiPM-based benchtop spectral CT system and established a pipeline for three-energy-bin photon-counting CT projection-domain processing. The empirical A-table method was employed for basis material decomposition, and the quantitative imaging performance of the spectral CT system was assessed. This evaluation included the synthesis errors of virtual monoenergetic images, electron density images, effective atomic number images, and linear attenuation coefficient curves. The validity of employing A-table methods for material identification in three-energy-bin spectral CT was confirmed through both simulations and experimental studies.

RESULTS

In both noise-free and noisy simulations, the thickness estimation experiments and quantitative imaging results demonstrated high accuracy. In the thickness estimation experiment using the practical spectral CT system, the mean absolute error for the estimated thickness of the decomposed Al basis material was 0.014 ± 0.010 mm, with a mean relative error of 0.66% ± 0.42%. Similarly, for the decomposed polymethyl methacrylate (PMMA) basis material, the mean absolute error in thickness estimation was 0.064 ± 0.058 mm, with a mean relative error of 0.70% ± 0.38%. Additionally, employing the equivalent thickness of the basis material allowed for accurate synthesis of 70 keV virtual monoenergetic images (relative error 1.85% ± 1.26%), electron density (relative error 1.81% ± 0.97%), and effective atomic number (relative error 2.64% ± 1.26%) of the tested materials. In addition, the average synthesis error of the linear attenuation coefficient curves in the energy range from 40 to 150 keV was 1.89% ± 1.07%.

CONCLUSIONS

Both simulation and experimental results demonstrate the accurate generation of 70 keV virtual monoenergetic images, electron density, and effective atomic number images using the A-table method. Quantitative imaging results indicate that the YSO/SiPM-based photon-counting detector is capable of accurately reconstructing virtual monoenergetic images, electron density images, effective atomic number images, and linear attenuation coefficient curves, thereby achieving precise material identification.

Assessment of solitary pulmonary nodules using dual-layer spectral detector computed tomography

We aim to quantitatively investigate the difference between benign and malignant solid pulmonary nodules that appeared on dual-energy spectral computed tomography, and assess the diagnostic accuracy of several parameters derived from computed tomography in differentiating malignant from benign pulmonary nodules. Between September 2021 and December 2022, spectral images of 71 patients (male:female = 44:27, mean age = 71.0 years) confirmed by pathology were retrospectively analyzed in the venous phase. Patients were classified into the malignant group and the benign group. The iodine concentration values of the nodules, normalized iodine concentration of the nodules to the neighboring vessels, virtual monochromatic images of 40 and 80 keV, and slope of the spectral curve were calculated and compared between the benign and malignant groups. Receiver operating characteristic curves and the area under the curve were performed to assess the diagnostic performance of the above parameters. Both virtual monochromatic images and iodine concentration maps prove to be highly useful in differentiating benign and malignant pulmonary nodules. The malignant pulmonary nodules have higher iodine density and slope of the spectral curve than the benign lesions. The combined model of iodine density and curve slope with an optimal cutoff of 0.39 (area under the curve = 0.82) yielded a sensitivity of 95% and a specificity of 63%. Contrast-enhanced dual-energy spectral computed tomography allows promising capability of distinguishing malignant from benign lesions, potential for avoiding unnecessary invasive procedure or surgery.

Systematic Review on Learning-based Spectral CT

Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.

Simultaneous iodine and barium imaging with photon-counting CT

Objective.The objective of this study is to explore the capabilities of photon-counting computed tomography (PCCT) in simultaneously imaging and differentiating materials with close atomic numbers, specifically barium (Z= 56) and iodine (Z= 53), which is challenging for conventional computed tomography (CT).Approach.Experiments were conducted using a bench-top PCCT system equipped with a cadmium zinc telluride detector. Various phantom setups and contrast agent concentrations (1%-5%) were employed, along with a biological sample. Energy thresholds were tuned to the K-edge absorption energies of barium (37.4 keV) and iodine (33.2 keV) to capture multi-energy CT images. K-edge decomposition was performed using K-edge subtraction and principal component analysis (PCA) techniques to differentiate and quantify the contrast agents.Main results.The PCCT system successfully differentiated and accurately quantified barium and iodine in both phantom combinations and a biological sample, achieving high correlations (R2≈1) between true and reconstructed concentrations. PCA outperformed K-edge subtraction, particularly in the presence of calcium, by providing superior differentiation between barium and iodine.Significance.This study demonstrates the potential of PCCT for reliable, detailed imaging in both clinical and research settings, particularly for contrast agents with similar atomic numbers. The results suggest that PCCT could offer significant improvements in imaging quality over conventional CT, especially in applications requiring precise material differentiation.

Photon-counting computed tomography in radiology

Photon-counting detector computed tomography (PCD-CT) devices have recently been introduced into practice, despite photon-counting detector technology having been studied for many years. PCD-CT devices are expected to provide advantages in dose reduction, tissue specificity, artifact-free imaging, and multi-contrast demonstration capacity. Noise reduction and increased spatial resolution are expected using PCD-CT, even under challenging scanning conditions. Some experimental or preliminary studies support this hypothesis. This pictorial review illustrates the features of PCD-CT systems, particularly in the interventional field. PCD-CT offers superior image quality and better lesion discrimination than conventional CT techniques for various conditions. PCD-CT shows significant improvements in many aspects of vascular imaging. It is still in its early stages, and several challenges have been identified. Also, PCD-CT devices have some important caveats. The average cost of these devices is 3 to 4 times higher than conventional CT units. This additional cost must be justified by improved clinical benefits or reduced clinical harms. Further investigations will be needed to resolve these issues.

A model-based direct inversion network (MDIN) for dual spectral computed tomography

Objective. Dual spectral computed tomography (DSCT) is a very challenging problem in the field of imaging. Due to the nonlinearity of its mathematical model, the images reconstructed by the conventional CT usually suffer from the beam hardening artifacts. Additionally, several existing DSCT methods rely heavily on the information of the spectra, which is often not readily available in applications. To address this problem, in this study, we aim to develop a novel approach to improve the DSCT reconstruction performance.Approach. A model-based direct inversion network (MDIN) is proposed for DSCT, which can directly predict the basis material images from the collected polychromatic projections. The all operations are performed in the network, requiring neither the conventional algorithms nor the information of the spectra. It can be viewed as an approximation to the inverse procedure of DSCT imaging model. The MDIN is composed of projection pre-decomposition module (PD-module), domain transformation layer (DT-layer), and image post-decomposition module (ID-module). The PD-module first performs the pre-decomposition on the polychromatic projections that consists of a series of stacked one-dimensional convolution layers. The DT-layer is designed to obtain the preliminary decomposed results, which has the characteristics of sparsely connected and learnable parameters. And the ID-module uses a deep neural network to further decompose the reconstructed results of the DT-layer so as to achieve higher-quality basis material images.Main results. Numerical experiments demonstrate that the proposed MDIN has significant advantages in substance decomposition, artifact reduction and noise suppression compared to other methods in the DSCT reconstruction.Significance. The proposed method has a flexible applicability, which can be extended to other CT problems, such as multi-spectral CT and low dose CT.

Systematic Review on Learning-based Spectral CT

Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.

Feasibility study of YSO/SiPM based detectors for virtual monochromatic image synthesis

BACKGROUND

The development of photon-counting CT systems has focused on semiconductor detectors like cadmium zinc telluride (CZT) and cadmium telluride (CdTe). However, these detectors face high costs and charge-sharing issues, distorting the energy spectrum. Indirect detection using Yttrium Orthosilicate (YSO) scintillators with silicon photomultiplier (SiPM) offers a cost-effective alternative with high detection efficiency, low dark count rate, and high sensor gain.

OBJECTIVE

This work aims to demonstrate the feasibility of the YSO/SiPM detector (DexScanner L103) based on the Multi-Voltage Threshold (MVT) sampling method as a photon-counting CT detector by evaluating the synthesis error of virtual monochromatic images.

METHODS

In this study, we developed a proof-of-concept benchtop photon-counting CT system, and employed a direct method for empirical virtual monochromatic image synthesis (EVMIS) by polynomial fitting under the principle of least square deviation without X-ray spectral information. The accuracy of the empirical energy calibration techniques was evaluated by comparing the reconstructed and actual attenuation coefficients of calibration and test materials using mean relative error (MRE) and mean square error (MSE).

RESULTS

In dual-material imaging experiments, the overall average synthesis error for three monoenergetic images of distinct materials is 2.53% ±2.43%. Similarly, in K-edge imaging experiments encompassing four materials, the overall average synthesis error for three monoenergetic images is 4.04% ±2.63%. In rat biological soft-tissue imaging experiments, we further predicted the densities of various rat tissues as follows: bone density is 1.41±0.07 g/cm3, adipose tissue density is 0.91±0.06 g/cm3, heart tissue density is 1.09±0.04 g/cm3, and lung tissue density is 0.32±0.07 g/cm3. Those results showed that the reconstructed virtual monochromatic images had good conformance for each material.

CONCLUSION

This study indicates the SiPM-based photon-counting detector could be used for monochromatic image synthesis and is a promising method for developing spectral computed tomography systems.

A Locally Weighted Linear Regression Look-Up Table-Based Iterative Reconstruction Method for Dual Spectral CT

OBJECTIVE

Compared with traditional computed tomography (CT), dual spectral CT (DSCT) exhibits superior material distinguishability and thus has broad prospects in industrial and medical fields. In iterative DSCT algorithms, accurately modeling forward-projection functions is crucial, but it is very difficult to analytically provide accurate functions.

METHODS

In this article, we propose a locally weighted linear regression look-up table-based (LWLR-LUT) iterative reconstruction method for DSCT. First, the proposed method uses LWLR to establish LUTs for the forward-projection functions through calibration phantoms, achieving good local information calibration. Second, the reconstructed images can be iteratively obtained through the established LUTs. The proposed method not only does not require knowledge of the X-ray spectra and the attenuation coefficients, but also implicitly accounts for some scattered radiation while fitting locally the forward-projection functions in the calibration space.

RESULTS

Both numerical simulations and real data experiments demonstrate that the proposed method can achieve highly accurate polychromatic forward-projection functions and greatly improve the quality of the images reconstructed from scattering-free and scattering projections.

CONCLUSION

The proposed method is simple and practical, and achieves good material decomposition effects for objects with different complex structures through simple calibration phantoms.

Photon-Counting Versus Energy-Integrating Detector CT Angiography of the Lower Extremity in a Human Cadaveric Model With Continuous Extracorporeal Perfusion

OBJECTIVES

Detailed visualization of the arterial runoff is mandatory for the assessment of peripheral arterial occlusive disease. This study aims to compare the performance of a first-generation photon-counting detector computed tomography (PCD-CT) to a third-generation energy-integrating detector CT (EID-CT).

MATERIALS AND METHODS

Computed tomography angiographies of 8 upper leg arterial runoffs were performed on human cadaveric models with continuous extracorporeal perfusion. For both PCD-CT and EID-CT, radiation dose-equivalent 120 kVp acquisition protocols (low-/medium-/high-dose: CTDI Vol = 3/5/10 mGy) were used. All scans were performed with standard collimation (PCD-CT: 144 × 0.4 mm; EID-CT: 96 × 0.6 mm), a pitch factor of 0.4, and a gantry rotation time of 1.0 second. Reformatting of data included the use of comparable vascular kernels (Bv 48/49), a slice thickness and increment of 1.0 mm, and a field of view of 150 × 150 mm. Eight radiologists evaluated image quality independently using a browser-based pairwise forced-choice comparison setup. Kendall concordance coefficient ( W ) was calculated to estimate interrater agreement. Signal-to-noise ratio and contrast-to-noise ratio (CNR) were compared based on 1-way analyses of variance and linear regression analysis.

RESULTS

Low-dose PCD-CT achieved superior signal-to-noise ratio/CNR values compared with high-dose EID-CT ( P < 0.001). Linear regression analysis suggested that an EID-CT scan with a CTDI Vol of at least 15.5 mGy was required to match the CNR value of low-dose PCD-CT. Intraluminal contrast attenuation was higher in PCD-CT than EID-CT, irrespective of dose level (415.0 ± 31.9 HU vs 329.2 ± 29.4 HU; P < 0.001). Subjective image quality of low-dose PCD-CT was considered superior to high-dose EID-CT ( P < 0.001). Interrater agreement was high ( W = 0.989).

CONCLUSIONS

Using cadaveric models with continuous extracorporeal perfusion allows for intraindividual image quality comparisons between PCD-CT and EID-CT on variable dose levels. With superior luminal contrast attenuation and denoising in angiographies of the peripheral arterial runoff, PCD-CT displayed potential for radiation saving of up to 83% compared with EID-CT.

Spectral computed tomography-guided radiotherapy of osteosarcoma utilizing BiOI nanosheets

As an aggressive malignant bone tumor, osteosarcoma (OS) is usually found in children and adolescents. Computed tomography (CT) is an important tool for the clinical evaluation of osteosarcoma, but limits to low diagnostic specificity due to single parameters of traditional CT and modest signal-to-noise ratio of clinical iodinated contrast agents. As one kind of spectral CT, dual-energy CT (DECT), with the advantage of a provision of multi-parameter information, makes it possible to acquire the best signal-to-noise ratio image, accurate detection, as well as imaging-guided therapy of bone tumors. Hereby, we synthesized BiOI nanosheets (BiOI NSs) as a DECT contrast agent with superior imaging capability compared to iodine agents for clinical detection of OS. Meanwhile, the synthesized BiOI NSs with great biocompatibility is able to achieve effective radiotherapy (RT) by enhancing X-ray dose deposition at the tumor site, leading to DNA damage, which in turn inhibits tumor growth. This study offers a promising new avenue for DECT imaging-guided treatment of OS. STATEMENT OF SIGNIFICANCE: Osteosarcoma (OS) is a common primary malignant bone tumor. Traditional surgical procedures and conventional CT scans are often used for the treatment and monitoring of OS, but the effects are generally unsatisfactory. In this work, BiOI nanosheets (NSs) was reported for dual-energy CT (DECT) imaging-guided OS radiotherapy. The powerful and constant X-ray absorption of BiOI NSs at any energy guarantees excellent enhanced DECT imaging performance, allowing detailed visualization of OS through images with a better signal-to-noise ratio and guiding radiotherapy process. The deposition of X-rays could be greatly enhanced by Bi atoms to induce serious DNA damage in radiotherapy. Taken together, the BiOI NSs for DECT-guided radiotherapy will greatly improve the current treatment status of OS.

Preliminary experience of CT imaging of the ischaemic brain penumbra through spectral processing of multiphasic CTA datasets

To assess ischaemic penumbra through the post-processing of the spectral multiphasic CT Angiography (mCTA) data in acute ischaemic stroke (AIS) patients. Thirty one consecutive patients strongly suspected of severe Middle Cerebral Artery AIS presenting less than 6 h after onset of symptoms or with unknown time of onset of symptoms underwent a standardized CT protocol in spectral mode including Non Contrast CT, mCTA, and Perfusion CT (CTP) on a dual-layer MDCT system. Areas disclosing delayed enhancement on iodine density (ID) maps were highlighted by subtraction of the serial mCTA datasets. Two neuroradiologists independently rated the correspondence between delayed enhancing areas at mCTA and the penumbral/infarcted areas delineated by two validated CTP applications using a 5-levels scoring scale. Interobserver agreement between observers was evaluated by kappa statistics. Dose delivery was recorded for each acquisition. Averaged correspondence score between penumbra delineation using subtracted mCTA-derived ID maps and CTP ones was 2.76 for one application and 2.9 for the other with best interobserver agreement kappa value at 0.59. All 6 stroke mimics out of the 31 patients' cohort were correctly identified. Average dose delivery was 7.55 mSv for the whole procedure of which CTP accounted for 39.7%. Post-processing of spectral mCTA data could allow clinically relevant assessment of the presence or absence of ischaemic penumbra in AIS-suspected patients if results of this proof-of-concept study should be confirmed in larger patients'series.

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.

Dual Energy CT in Oncology: Benefits for Both Patients and Radiologists From an Emerging Quantitative and Functional Diagnostic Technique

Dual-energy CT (DECT) imaging makes it possible to identify the characteristics of materials that cannot be recognized with conventional single-energy CT (SECT). In the postprocessing study phase, virtual monochromatic images and virtual-non-contrast (VNC) images, also permits reduction of dose exposure by eliminating the precontrast acquisition scan. Moreover, in virtual monochromatic images, the iodine contrast increases when the energy level decreases resulting in better visualization of hypervascular lesions and in a better tissue contrast between hypovascular lesions and the surrounding parenchyma; thus, allowing for reduction of required iodinate contrast material, especially important in patients with renal impairment. All these advantages are particularly important in oncology, providing the possibility of overcoming many SECT imaging limits and making CT examinations safer and more feasible in critical patients. This review explores the basis of DECT imaging and its utility in routine oncologic clinical practice, with particular attention to the benefits of this technique for both the patients and the radiologists.

Virtual monoenergetic imaging predicting Ki-67 expression in lung cancer

This study aimed to optimize slope and energy levels for evaluating Ki-67 expression in lung cancer using virtual monoenergetic imaging and compare the predictive efficiency of different energy spectrum slopes (λHU) for Ki-67. Forty-three patients with primary lung cancer confirmed via pathological examination were enrolled in this study. They underwent baseline arterial-phase (AP) and venous-phase (VP) energy spectrum computed tomography (CT) scanning before surgery. The CT values were 40-190 keV, with 40-140 keV indicating pulmonary lesions at AP and VP, and P < 0.05 indicating a statistically significant difference. An immunohistochemical examination was conducted, and receiver operating characteristic curves were used to analyze the prediction performance of λHU for Ki-67 expression. SPSS Statistics 22.0 (IBM Corp., NY, USA) was used for statistical analysis, and χ², t, and Mann-Whitney U tests were used for quantitative and qualitative analyses of data. Significant differences were observed at the corresponding CT values of 40 keV (as 40-keV is considered the best for single-energy image for evaluating Ki-67 expression) and 50 keV in AP and at 40, 60, and 70 keV in VP between high- and low-Ki-67 expression groups (P < 0.05). In addition, the λHU values of three-segment energy spectrum curve in both AP and VP were quite different between two groups (P < 0.05). However, the VP data had greater predictive values for Ki-67. The areas under the curve were 0.859, 0.856, and 0.859, respectively. The 40-keV single-energy sequence was the best single-energy sequence to evaluate the expression of Ki-67 in lung cancer and to obtain λHU values using the energy spectrum curve in the VP. The CT values had better diagnostic efficiency.

Multi-Energy and Fast-Convergence Iterative Reconstruction Algorithm for Organic Material Identification Using X-ray Computed Tomography

In order to significantly reduce the computing time while, at the same time, keeping the accuracy and precision when determining the local values of the density and effective atomic number necessary for identifying various organic material, including explosives and narcotics, a specialized multi-stage procedure based on a multi-energy computed tomography investigation within the 20-160 keV domain was elaborated. It consisted of a compensation for beam hardening and other non-linear effects that affect the energy dependency of the linear attenuation coefficient (LAC) in the chosen energy domain, followed by a 3D fast reconstruction algorithm capable of reconstructing the local LAC values for 64 energy values from 19.8 to 158.4 keV, and, finally, the creation of a set of algorithms permitting the simultaneous determination of the density and effective atomic number of the investigated materials. This enabled determining both the density and effective atomic number of complex objects in approximately 24 s, with an accuracy and precision of less than 3%, which is a significantly better performance with respect to the reported literature values.

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.

Spectral CT imaging: Technical principles of dual-energy CT and multi-energy photon-counting CT

Spectral computed tomography (CT) imaging encompasses a unique generation of CT systems based on a simple principle that makes use of the energy-dependent information present in CT images. Over the past two decades this principle has been expanded with the introduction of dual-energy CT systems. The first generation of spectral CT systems, represented either by dual-source or dual-layer technology, opened up a new imaging approach in the radiology community with their ability to overcome the limitations of tissue characterization encountered with conventional CT. Its expansion worldwide can also be considered as an important leverage for the recent groundbreaking technology based on a new chain of detection available on photon counting CT systems, which holds great promise for extending CT towards multi-energy CT imaging. The purpose of this article was to detail the basic principles and techniques of spectral CT with a particular emphasis on the newest technical developments of dual-energy and multi-energy CT systems.

Spectral computed tomography with inorganic nanomaterials: State-of-the-art

Recently, spectral computed tomography (CT) technology has received great interest in the field of radiology. Spectral CT imaging utilizes the distinct, energy-dependent X-ray absorption properties of substances in order to provide additional imaging information. Dual-energy CT and multi-energy CT (Spectral CT) are capable of constructing monochromatic energy images, material separation images, energy spectrum curves, constructing effective atomic number maps, and more. However, poor contrast, due to neighboring X-ray attenuation of organs and tissues, is still a challenge to spectral CT. Hence, contrast agents (CAs) are applied for better differentiation of a given region of interest (ROI). Currently, many different kinds of inorganic nanoparticulate CAs for spectral CT have been developed due to the limitations of clinical iodine (I)-based contrast media, leading to the conclusion that inorganic nanomedicine applied to spectral CT will be a powerful collaboration both in basic research and in clinics. In this review, the underlying principles and types of spectral CT techniques are discussed, and some evolving clinical diagnosis applications of spectral CT techniques are introduced. In particular, recent developments in inorganic CAs used for spectral CT are summarized. Finally, the challenges and future developments of inorganic nanomedicine in spectral CT are briefly discussed.

Basis and current state of computed tomography perfusion imaging: a review

Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.

Quantitative dual-energy CT techniques in the abdomen

Advances in dual-energy CT (DECT) technology and spectral techniques are catalyzing the widespread implementation of this technology across multiple radiology subspecialties. The inclusion of energy- and material-specific datasets has ushered overall improvements in CT image contrast and noise as well as artifacts reduction, leading to considerable progress in radiologists' ability to detect and characterize pathologies in the abdomen. The scope of this article is to provide an overview of various quantitative clinical DECT applications in the abdomen and pelvis. Several of the reviewed applications have not reached mainstream clinical use and are considered investigational. Nonetheless awareness of such applications is critical to having a fully comprehensive knowledge base to DECT and fostering future clinical implementation.

Dual energy CT applications in oncological imaging

Cancer is the second leading cause of death in the United States, killing more than 600.000 people each year.¹ Despite several screening programs available, cancer diagnosis is often made incidentally during imaging studies performed for other reasons. Once the diagnosis is made, treatment assessment and surveillance of these patients heavily rely on radiological tools. Computed tomography (CT) in particular is one of the most commonly ordered modalities due to wide availability even in the most remote locations, and fast results. However, conventional CT often cannot definitively characterize a neoplastic lesion unless it was tailored toward answering a specific question. Furthermore, characterizing small lesions can be difficult with CT. An innovative technique called dual-energy CT (DECT) offers solutions to some of the challenges of conventional CT in oncological imaging.

Data Analysis and Filter Optimization for Pulse-Amplitude Measurement: A Case Study on High-Resolution X-ray Spectroscopy

In this study, we present a procedure to optimize a set of finite impulse response filter (FIR) coefficients for digital pulse-amplitude measurement. Such an optimized filter is designed using an adapted digital penalized least mean square (DPLMS) method. The effectiveness of the procedure is demonstrated using a dataset from a case study on high-resolution X-ray spectroscopy based on single-photon detection and energy measurements. The energy resolutions of the Kα and Kβ lines of the Manganese energy spectrum have been improved by approximately 20%, compared to the reference values obtained by fitting individual photon pulses with the corresponding mathematical model.

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.

Materials Separation via the Matrix Method Employing Energy-Discriminating X-ray Detection

The majority of lab-based X-ray sources are polychromatic and are not easily tunable, which can make the 3D quantitative analysis of multi-component samples challenging. The lack of effective materials separation when using conventional X-ray tube sources has motivated the development of a number of potential solutions including the application of dual-energy X-ray computed tomography (CT) as well as the use of X-ray filters. Here, we demonstrate the simultaneous decomposition of two low-density materials via inversion of the linear attenuation matrices using data from the energy-discriminating PiXirad detector. A key application for this method is soft-tissue differentiation which is widely used in biological and medical imaging. We assess the effectiveness of this approach using both simulation and experiment noting that none of the materials investigated here incorporate any contrast enhancing agents. By exploiting the energy discriminating properties of the detector, narrow energy bands are created resulting in multiple quasi-monochromatic images being formed using a broadband polychromatic source. Optimization of the key parameters for materials separation is first demonstrated in simulation followed by experimental validation using a phantom test sample in 2D and a small-animal model in 3D.

Metal Artifact Reduction in Spectral X-ray CT Using Spectral Deep Learning

Spectral X-ray computed tomography (SCT) is an emerging method for non-destructive imaging of the inner structure of materials. Compared with the conventional X-ray CT, this technique provides spectral photon energy resolution in a finite number of energy channels, adding a new dimension to the reconstructed volumes and images. While this mitigates energy-dependent distortions such as beam hardening, metal artifacts due to photon starvation effects are still present, especially for low-energy channels where the attenuation coefficients are higher. We present a correction method for metal artifact reduction in SCT that is based on spectral deep learning. The correction efficiently reduces streaking artifacts in all the energy channels measured. We show that the additional information in the energy domain provides relevance for restoring the quality of low-energy reconstruction affected by metal artifacts. The correction method is parameter free and only takes around 15 ms per energy channel, satisfying near-real time requirement of industrial scanners.

Dual-Layer Spectral CTA for TAVI Planning Using a Split-Phase Protocol and Low-keV Virtual Monoenergetic Images: Improved Image Quality in Comparison with Single-Phase Conventional CTA

PURPOSE

 Adaptation of computed tomography protocols for transcatheter aortic valve implantation (TAVI) planning is required when a first-generation dual-layer spectral CT scanner (DLCT) is used. The purpose of this study was to evaluate the objective image quality of aortic CT angiography (CTA) for TAVI planning using a split-phase technique with reconstruction of 40 keV virtual monoenergetic images (40 keV-VMI) obtained with a DLCT scanner. CT angiography obtained with a single-phase protocol of a conventional single-detector CT (SLCT) was used for comparison.

MATERIALS AND METHODS

 75 CTA scans from DLCT were retrospectively compared to 75 CTA scans from SLCT. For DLCT, spiral CTA without ECG-synchronization was performed immediately after a retrospectively ECG-gated acquisition covering the heart and aortic arch. For SLCT, spiral CTA with retrospective ECG-gating was performed to capture the heart and the access route simultaneously in one scan. Objective image quality was compared at different levels of the arterial access route.

RESULTS

 40 keV virtual monoenergetic images of DLCT showed a significantly higher mean vessel attenuation, SNR, and CNR at all levels of the arterial access route. With 40 keV-VMI of DLCT, the overall mean aortic attenuation of all six measured regions was 589.6 ± 243 HU compared to 492.7 ± 209 HU of SLCT (p < 0.01). A similar trend could be observed for SNR (23.6 ± 18 vs. 18.6 ± 9; p < 0.01) and CNR (21.1 ± 18 vs. 16.4 ± 8; p < 0.01). No deterioration was observed for vascular noise (27.8 ± 9 HU vs. 28.1 ± 8 HU; p = 0.599).

CONCLUSION

 Using a DLCT scanner with a split-phase protocol and 40 keV-VMI for TAVI planning, higher objective image quality can be obtained compared to a single-phase protocol of a conventional CT scanner.

KEY POINTS

  · Adaption of TAVI planning CT protocols may be required when using a first-generation dual-layer CT scanner.. · Reconstruction of virtual monoenergetic images at 40 keV improves image quality.. · With a split-phase protocol, the radiation dose is lower compared to a single-phase ECG-gated CT acquisition..

CITATION FORMAT

· Mangold D, Salatzki J, Riffel J et al. Dual-Layer Spectral CTA for TAVI Planning Using a Split-Phase Protocol and Low-keV Virtual Monoenergetic Images: Improved Image Quality in Comparison with Single-Phase Conventional CTA. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1717-2542.

Iterative dynamic dual-energy CT algorithm in reducing statistical noise in multi-energy CT imaging

Multi-energy spectral CT has a broader range of applications with the recent development of photon-counting detectors. However, the photons counted in each energy bin decrease when the number of energy bins increases, which causes a higher statistical noise level of the CT image. In this work, we propose a novel iterative dynamic dual-energy CT algorithm to reduce the statistical noise. In the proposed algorithm, the multi-energy projections are estimated from the dynamic dual-energy CT data during the iterative process. The proposed algorithm is verified on sufficient numerical simulations and a laboratory two-energy-threshold PCD system. By applying the same reconstruction algorithm, the dynamic dual-energy CT's final reconstruction results have a much lower statistical noise level than the conventional multi-energy CT. Moreover, based on the analysis of the simulation results, we explain why the dynamic dual-energy CT has a lower statistical noise level than the conventional multi-energy CT. The reason is that: the statistical noise level of multi-energy projection estimated with the proposed algorithm is much lower than that of the conventional multi-energy CT, which leads to less statistical noise of the dynamic dual-energy CT imaging.

Spectral Properties of Abdominal Tissues on Dual-energy Computed Tomography and the Effects of Contrast Agent

BACKGROUND/AIM

Multiparametric dual energy comptuted tomography (CT) imaging allows for multidimensional tissue characterization beyond the measurement of Hounsfield units. The purpose of this study was to evaluate multiple imaging parameters for different abdominal organs in dual energy CT (DECT) and analyze the effects of the contrast agent on these different parameters and provide normal values for characterization of parenchymatous organs.

PATIENTS AND METHODS

This retrospective analysis included a total of 484 standardized DECT scans of the abdomen. Hounsfield Units (HU), rho (electron density relative to water), Z_eff (effective atomic number) and FF (fat fraction) were evaluated for liver, spleen, kidney, muscle, fat-tissue. Independent generalized estimation equation models were fitted.

RESULTS

In DECT imaging there is only little difference in mean HU_mixed for parenchymatous abdominal organs. Analysis including Z_eff, rho and FF allows for better discrimination while a large overlap remains for liver, spleen and muscle. Including multidimensional analysis and the effects of contrast medium further enhances tissue characterization. Small differences remain for liver and spleen.

CONCLUSION

Organ characterization using multiparametric dual energy CT analysis is possible. An increased number of parameters obtained from DECT improves organ characterization. To our knowledge this is the first attempt to provide normal values for characterization of parenchymatous organs.

An iterative reconstruction method based on monochromatic images for dual energy CT

PURPOSE

Dual-energy computed tomography (DECT) scans objects using two different X-ray spectra to acquire more information, which is also called dual spectral CT (DSCT) in some articles. Compared to traditional CT, DECT exhibits superior material distinguishability. Therefore, DECT can be widely used in the medical and industrial domains. However, owing to the nonlinearity and ill condition of DECT, studies are underway on DECT reconstruction to obtain high quality images and achieve fast convergence speed. Therefore, in this study, we propose an iterative reconstruction method based on monochromatic images (IRM-MI) to rapidly obtain high-quality images in DECT reconstruction.

METHODS

An IRM-MI is proposed for DECT. The proposed method converts DECT reconstruction problem from the basis material images decomposition to monochromatic images decomposition to significantly improve the convergence speed of DECT reconstruction by changing the coefficient matrix of the original equations to increase the angle of the high- and low-energy projection curves or reduce the condition number of the coefficient matrix. The monochromatic images were then decomposed into basis material images. Furthermore, we conducted numerical experiments to evaluate the performance of the proposed method.

RESULTS

The decomposition results of the simulated data and real data experiments confirmed the effectiveness of the proposed method. Compared to the extended algebraic reconstruction technique (E-ART) method, the proposed method exhibited a significant increase in the convergence speed by increasing the angle of polychromatic projection curves or decreasing the condition number of the coefficient matrix, when choosing the appropriate monochromatic images. Therefore, the proposed method is also advantageous in acquiring high quality and rapidly converged images.

CONCLUSIONS

We developed an iterative reconstruction method based on monochromatic images for the material decomposition for DECT. The numerical experiments using the proposed method validated its capability of decomposing the basis material images. Furthermore, the proposed method achieved faster convergence speed compared to the E-ART method.

Learning-Based Stopping Power Mapping on Dual-Energy CT for Proton Radiation Therapy

Purpose

Dual-energy computed tomography (DECT) has been used to derive relative stopping power (RSP) maps by obtaining the energy dependence of photon interactions. The DECT-derived RSP maps could potentially be compromised by image noise levels and the severity of artifacts when using physics-based mapping techniques. This work presents a noise-robust learning-based method to predict RSP maps from DECT for proton radiation therapy.

Materials and Methods

The proposed method uses a residual attention cycle-consistent generative adversarial network to bring DECT-to-RSP mapping close to a 1-to-1 mapping by introducing an inverse RSP-to-DECT mapping. To evaluate the proposed method, we retrospectively investigated 20 head-and-neck cancer patients with DECT scans in proton radiation therapy simulation. Ground truth RSP values were assigned by calculation based on chemical compositions and acted as learning targets in the training process for DECT datasets; they were evaluated against results from the proposed method using a leave-one-out cross-validation strategy.

Results

The predicted RSP maps showed an average normalized mean square error of 2.83% across the whole body volume and an average mean error less than 3% in all volumes of interest. With additional simulated noise added in DECT datasets, the proposed method still maintained a comparable performance, while the physics-based stoichiometric method suffered degraded inaccuracy from increased noise level. The average differences from ground truth in dose volume histogram metrics for clinical target volumes were less than 0.2 Gy for D_95% and D_max with no statistical significance. Maximum difference in dose volume histogram metrics of organs at risk was around 1 Gy on average.

Conclusion

These results strongly indicate the high accuracy of RSP maps predicted by our machine-learning–based method and show its potential feasibility for proton treatment planning and dose calculation.

Dual-energy CT enterography in evaluation of Crohn's disease: the role of virtual monochromatic images

PURPOSE

To assess the use of virtual monochromatic images (VMI) for discrimination of affected and non-affected bowel walls in patients with Crohn's disease (CD) as well as to compare mural enhancement between patients with and without CD.

MATERIALS AND METHODS

This retrospective study included 61 patients (47 with CD, 14 without CD). Attenuation value (AV), signal-to noise ratio (SNR), and contrast-to-noise ratio (CNR) were obtained at VMI energy levels from 40 to 110 keV in 10 keV increment. Analyses were performed among affected and non-affected bowel walls in CD patients, as well as from bowel walls in patients without CD. Image quality and mural enhancement were evaluated at VMI energy levels at 40, 70, and 110 keV.

RESULTS

At all energy levels of VMI, each quantitative data for AV, SNR, and CNR showed statistically significant difference between diseased and non-diseased bowel walls in CD patients. In the quantitative assessment of patients with and without CD, the optimal AV and SNR were obtained at 40 keV, and the optimal CNR was obtained at 70 keV. For the qualitative assessment, the best image quality and mural enhancement were obtained at 70 keV and 40 keV, respectively.

CONCLUSION

VMI are helpful for the differentiation of affected bowel walls in CD patients, providing high diagnostic accuracy.

Dual-energy CT in diffuse liver disease: is there a role?

Dual-energy CT (DECT) can be defined as the use of two different energy levels to identify and quantify material composition. Since its inception, DECT has benefited from remarkable improvements in hardware and clinical applications. DECT enables accurate identification and quantification of multiple materials, including fat, iron, and iodine. As a consequence, multiple studies have investigated the potential role of DECT in the assessment of diffuse liver diseases. While this role is evolving, this article aims to review the most relevant literature on use of DECT for assessment of diffuse liver diseases. Moreover, the basic concepts on DECT techniques, types of image reconstruction, and DECT-dedicated software will be described, focusing on the areas that are most relevant for the evaluation of diffuse liver diseases. Also, we will review the evidence of added value of DECT in detection and assessment of hepatocellular carcinoma which is a known risk in patients with diffuse liver disease.

Effects of Detector Sampling on Noise Reduction in Clinical Photon-Counting Whole-Body Computed Tomography

OBJECTIVES

Reconstructing images from measurements with small pixels below the system's resolution limit theoretically results in image noise reduction compared with measurements with larger pixels. We evaluate and quantify this effect using data acquired with the small pixels of a photon-counting (PC) computed tomography scanner that can be operated in different detector pixel binning modes and with a conventional energy-integrating (EI) detector.

MATERIALS AND METHODS

An anthropomorphic abdominal phantom that can be extended to 3 sizes by adding fat extension rings, equipped with iodine inserts as well as human cadavers, was measured at tube voltages ranging from 80 to 140 kV. The images were acquired with the EI detector (0.6 mm pixel size at isocenter) and the PC detector operating in Macro mode (0.5 mm pixel size at iso) and ultrahigh-resolution (UHR) mode (0.25 mm pixel size at iso). Both detectors are components of the same dual-source prototype computed tomography system. During reconstruction, the modulation transfer functions were matched to the one of the EI detector. The dose-normalized contrast-to-noise ratio (CNRD) values are evaluated as a figure of merit.

RESULTS

Images acquired in UHR mode achieve on average approximately 6% higher CNRD compared with Macro mode at the same spatial resolution for a quantitative D40f kernel. Using a sharper B70f kernel, the improvement increases to 21% on average. In addition, the better performance of PC detectors compared with EI detectors with regard to iodine imaging has been evaluated by comparing CNRD values for Macro and EI. Combining both of these effects, a CNRD improvement of up to 34%, corresponding to a potential dose reduction of up to 43%, can be achieved for D40f.

CONCLUSIONS

Reconstruction of UHR data with a modulation transfer function below the system's resolution limit reduces image noise for all patient sizes and tube voltages compared with standard acquisitions. Thus, a relevant dose reduction may be clinically possible while maintaining image quality.

Quo Vadis, Molecular Imaging?

The important insights yielded by molecular imaging (MI) into relevant biologic signatures at an organ-specific and systemic level are not achievable with conventional imaging methods and thus provide an essential link between preclinical and clinical research. New diagnostic probes and imaging methods revealing comprehensive functional and molecular information are being provided by MI research, several of which have found their way into clinical application. However, there are also reservations about the impact of MI and its added value over conventional, often less expensive, diagnostic imaging methods. This perspective discusses seminal research directions for the MI field that have the potential to result in added value to the patient. Emphasis is placed on MI without probes, MI based on radiotracers and small molecules, MI nano- and microsystems, and MI in context with comprehensive diagnostics. Furthermore, besides technical innovations and probes, emerging clinical indications are highlighted.

A comprehensive review of dual-energy and multi-spectral computed tomography

This review will provide a brief introduction to the development of the first Computed Tomography (CT) scan, from the beginnings of x-ray imaging to the first functional CT system introduced by Godfrey Houndsfield. The principles behind photon interactions and the methods by which they can be leveraged to generate dual-energy or multi-spectral CT images are discussed. The clinical applications of these methodologies are investigated, showing the immense potential for dual-energy or multi-spectral CT to change the fields of in-vivo and non-destructive imaging for quantitative analysis of tissues and materials. Lastly the current trends of research for dual-energy and multi-spectral CT are covered, showing that the majority of instrument development is focused on photon counting detectors for mutli-spectral CT and that clinical research is dominated by validation studies for the implementation of dual-energy and multi-spectral CT.

Focal organizing pneumonia in patients: differentiation from solitary bronchioloalveolar carcinoma using dual-energy spectral computed tomography

OBJECTIVE

To explore the utility of dual-energy spectral computed tomography (CT) in the differential diagnosis of focal organizing pneumonia (FOP) and solitary bronchioloalveolar carcinoma (S-BAC).

MATERIALS AND METHODS

The institutional review board approved this study and waived the requirement for informed consent. It is a retrospective study. A total of 105 patients (62 with FOP and 43 with S-BAC) enrolled and all patients have contrast enhanced spectral CT including the arterial phase (AP) and venous phase (VP). During AP and VP, CT_{40 keV}, CT_{70 keV}, and CT_{100 keV} values, iodine concentration (IC), water concentration (WC), and effective atomic number (Zeff) were measured on monochromatic and iodine-based material decomposition images, and the slope of the spectral curve (λ_Hu) was calculated. The two-sample t-test was used to compare quantitative parameters, and receiver operating characteristic (ROC) curves were generated to calculate diagnostic efficacies.

RESULTS

For AP, CT_{40 keV} and CT_{70 keV} values, IC, WC, Zeff, λ_{70 keV}, and λ_{100 keV} measurements, there were significantly higher in patients with S-BAC than in those with FOP (P < 0.05). However, these quantitative parameters of VP were significantly lower in patients with S-BAC than in those with FOP (P < 0.05). ROC curve analysis revealed that the combination of all quantitative parameters in AP and VP provided the best diagnostic performance in distinguishing S-BAC from FOP (area under the ROC curve, 93.1%; sensitivity, 95.3%; specificity, 77.4%).

CONCLUSIONS

Dual-energy spectral CT has the potential to identify S-BAC and FOP.

Photon-counting computed tomography of lanthanide contrast agents with a high-flux 330-μm-pitch cadmium zinc telluride detector in a table-top system

Purpose: We present photon-counting computed tomography (PCCT) imaging of contrast agent triplets similar in atomic number ( Z ) achieved with a high-flux cadmium zinc telluride (CZT) detector. Approach: The table-top PCCT imaging system included a 330 - μ m -pitch CZT detector of size 8    mm × 24    mm 2 capable of using six energy bins. Four 3D-printed 3-cm-diameter phantoms each contained seven 6-mm-diameter vials with water and low and high concentration solutions of various contrast agents. Lanthanum ( Z = 57 ), gadolinium (Gd) ( Z = 64 ), and lutetium ( Z = 71 ) were imaged together and so were iodine ( Z = 53 ), Gd, and holmium ( Z = 67 ). Each phantom was imaged with 1-mm aluminum-filtered 120-kVp cone beam x rays to produce six energy-binned computed tomography (CT) images. Results: K -edge images were reconstructed using a weighted sum of six CT images, which distinguished each contrast agent with a root-mean-square error (RMSE) of < 0.29 % and 0.51% for the 0.5% and 5% concentrations, respectively. Minimal cross-contamination in each K -edge image was seen, with RMSE values < 0.27 % in vials with no contrast. Conclusion: This is the first preliminary demonstration of simultaneously imaging three similar Z contrast agents with a difference in Z as low as 3.

Imaging the In Vivo Degradation of Tissue Engineering Implants by Use of Supramolecular Radiopaque Biomaterials

For in situ tissue engineering (TE) applications it is important that implant degradation proceeds in concord with neo-tissue formation to avoid graft failure. It will therefore be valuable to have an imaging contrast agent (CA) available that can report on the degrading implant. For this purpose, a biodegradable radiopaque biomaterial is presented, modularly composed of a bisurea chain-extended polycaprolactone (PCL2000-U4U) elastomer and a novel iodinated bisurea-modified CA additive (I-U4U). Supramolecular hydrogen bonding interactions between the components ensure their intimate mixing. Porous implant TE-grafts are prepared by simply electrospinning a solution containing PCL2000-U4U and I-U4U. Rats receive an aortic interposition graft, either composed of only PCL2000-U4U (control) or of PCL2000-U4U and I-U4U (test). The grafts are explanted for analysis at three time points over a 1-month period. Computed tomography imaging of the test group implants prior to explantation shows a decrease in iodide volume and density over time. Explant analysis also indicates scaffold degradation. (Immuno)histochemistry shows comparable cellular contents and a similar neo-tissue formation process for test and control group, demonstrating that the CA does not have apparent adverse effects. A supramolecular approach to create solid radiopaque biomaterials can therefore be used to noninvasively monitor the biodegradation of synthetic implants.

Determination of optimal virtual monochromatic energy level for target delineation of brain metastases in radiosurgery using dual-energy CT

OBJECTIVE

Determination of the optimal energy level of virtual monochromatic image (VMI) for brain metastases in contrast-enhanced dual-energy CT (DECT) for radiosurgery and assessment of the subjective and objective image quality of VMI at the optimal energy level.

METHODS

20 patients (total of 42 metastases) underwent contrast-enhanced DECT. Spectral image analysis of VMIs at energy levels ranging from 40 to 140 keV in 1 keV increments was performed to determine the optimal VMI (VMI_opt) as the one corresponding to the highest contrast-to-noise ratio (CNR) between brain parenchyma and the metastases. The objective and subjective values of VMI_opt were compared to those of the VMI with 120 kVp equivalent, defined as reference VMI (VMI_ref, 77 keV). The objective measurement parameters included mean HU value and SD of tumor and brain parenchyma, absolute lesion contrast (LC), and CNR. The subjective measurements included five-point scale assessment of "overall image quality" and "tumor delineation" by three radiation oncologists.

RESULTS

The VMI at 63 keV was defined as VMI_opt. The LC and CNR of VMI_opt were significantly (p < 0.01) higher than those of VMI_ref (LC: 37.4 HU vs 24.7 HU; CNR: 1.1 vs 0.8, respectively). Subjective analysis rated VMI_opt significantly (p < 0.01) superior to VMI_ref with respect to the overall image quality (3.2 vs 2.9, respectively) and tumor delineation (3.5 vs 2.9, respectively).

CONCLUSION

The VMI at 63 keV derived from contrast-enhanced DECT yielded the highest CNR and improved the objective and subjective image quality for radiosurgery, compared to VMI_ref.

ADVANCES IN KNOWLEDGE

This paper investigated for the first time the optimal energy level of VMI in DECT for brain metastases. The findings will lead to improvement in tumor visibility with optimal VMI and consequently supplement accuracy delineation of brain metastases.

The role of low keV virtual monochromatic imaging in increasing the conspicuity of primary breast cancer in dual-energy spectral thoracic CT examination for staging purposes

Background

The additive value of dual-energy spectral computerized tomography (DESCT) in breast cancer imaging is still unknown.

Purpose

To investigate the role of DESCT in improving the conspicuity of primary breast cancer.

Material and Methods

Twenty-nine patients who were histopathologically diagnosed with breast cancer and underwent DESCT for staging of lung metastasis were evaluated retrospectively. The visual conspicuity of breast cancer was scored by two readers separately in reconstructed virtual monochromatic images obtained at 40, 60, 80, and 100 keV. A circular region of interest slightly smaller than the maximum contrasted portion of the primary breast cancer was manually placed. Iodine enhancement (HU) and iodine content (mg/mL) values of tumor, normal breast tissue and pectoral muscle, and contrast-to-noise values of images at four different energy levels were calculated.

Results

The lesion conspicuity score peaked at 40-keV series for both readers and was significantly higher than those at other energy levels (all P < 0.001). Lesion iodine enhancement was highest at 40-keV virtual monochromatic image reconstructions ( P < 0.001). The iodine content was significantly higher in tumor than normal breast tissue, and pectoral muscle ( P < 0.001). The highest contrast-to-noise value was obtained at 60 keV (4.0 ± 2.5), followed by 40 keV (3.9 ± 2.2), without a significant difference ( P = 0.33).

Conclusion

The conspicuity of primary breast cancer was significantly higher in low keV virtual monochromatic images obtained by DESCT. This gives us hope that DESCT may play an effective role in detecting incidental breast lesions. It also raises the question of whether quantitative values obtained by DESCT can be used for characterization of primary breast lesion.