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Energy spectrum computed tomography can provide quantitative information of scanned objects and realize material decomposition. At present, the material decomposition method based on neural networks overcomes the limited decomposition effect of traditional iterative algorithms. However, the performance of traditional neural networks in feature detail recovery is still not satisfactory. To improve the material decomposition accuracy, a material decomposition method based on a Resnet and Squeeze excitation network (RS-Net) is proposed. The proposed method uses the structure of the U-Net network and Resnet-152 as the backbone network to extract multi-scale features. Parallel asymmetric convolution is used to complete the large kernel convolution, which reduces the number of parameters and computation of the network. The HD-SE attention mechanism is introduced in the decoder part to help the network recover the image features. Hybrid loss supervised network learning is used to improve the decomposition accuracy of the network. The feasibility of this method is verified on simulated rock and artificial sandstone datasets. The simulation and experimental results show that RS-Net combined with mixing loss can retain more internal details of the image, the decomposed image edge is clearer, and the image quality is higher.

The dual-energy spectral CT (DEsCT) employs material decomposition (MD) technology, opening up novel avenues for the opportunistic assessment of bone status. Radiomics, a powerful tool for elucidating the structural and textural characteristics of bone, aids in the detection of mineral loss. Therefore, this study aims to compare the efficacy of bone status assessment using both bone density measurements and radiomics models derived from MD images and to further explore the clinical value of radiomics models. Methods: Retrospective data were collected from 307 patients who underwent both quantitative computed tomography (QCT) and full-abdomen DEsCT scans at our institution. Based on QCT measurements, patients were divided into three categories: normal bone mineral density (BMD), osteopenia, and osteoporosis. Using the abdominal DEsCT data, six types of MD images were reconstructed, including HAP (Water), HAP (Fat), Ca (Water), Ca (Fat), Fat (Ca), and Fat (HAP). Patients were randomly divided into a training cohort (n = 214) and a validation cohort (n = 93) at a ratio of 7:3. Focusing on the L1 to L3 vertebrae, density values from the six MD images were measured. Six density value models and six radiomics models were constructed using a random forest (RF) classifier. The performance of these models in assessing bone status was evaluated using the receiver operating characteristic (ROC) curves, and the DeLong test was employed to compare performance differences between the models. Results: The macro-area under the curve (AUC) values for the density value models based on HAP (Water), HAP (Fat), Ca (Water), and Ca (Fat) MD images were 0.870, 0.870, 0.847, and 0.765, respectively, which outperformed those of Fat (Ca) (AUC = 0.623) and Fat (HAP) (AUC = 0.618) density value models. In the comparison of radiomics models, the trends of model performance were consistent with the density value models across the six MD images. However, the models based on HAP (Water), Ca (Water), HAP (Fat), Ca (Fat), Fat (Ca), and Fat (HAP) images exhibited superior performance than those of the density value models with the corresponding MD images, with values of 0.946, 0.941, 0.934, 0.926, 0.831, and 0.824, respectively. Conclusions: Bone status assessment can be accurately conducted using density values from HAP (Water), HAP (Fat), Ca (Water), and Ca (Fat) MD images. However, radiomics models derived from MD images surpass traditional density measurement methods in evaluating bone status, highlighting their superior diagnostic potential.

To evaluate the advantages of dual-energy computed tomography (DECT) virtual non-hydroxyapatite color mapping (VNHAP) in combination with standard bone CT (BCT) in the identification of subtle or occult traumatic fractures referred to emergency and acceptance departments (DEAs). Forty patients (22 men; mean age 83 ± 23.7 y) with suspected traumatic fractures referred to our emergency department and examined with a fast kilovoltage-switching single-source spectral CT scan between January and October 2023 were retrospectively reviewed. The BCT and VNHAP images were blindly evaluated by two radiologists with >10 years and

Abstract Thermal ablation procedures, such as high intensity focused ultrasound and radiofrequency ablation, are often used to eliminate tumors by minimally invasively heating a focal region. For this task, real-time 3D temperature visualization is key to target the diseased tissues while minimizing damage to the surroundings. Current computed tomography (CT) thermometry is based on energy-integrated CT, tissue-specific experimental data, and linear relationships between attenuation and temperature. In this paper, we develop a novel approach using photon-counting CT for material decomposition and a neural network to predict temperature based on thermal characteristics of base materials and spectral tomographic measurements of a volume of interest. In our feasibility study, distilled water, 50 mmol/L CaCl2, and 600 mmol/L CaCl2 are chosen as the base materials. Their attenuations are measured in four discrete energy bins at various temperatures. The neural network trained on the experimental data achieves a mean absolute error of 3.97 °C and 1.80 °C on 300 mmol/L CaCl2 and a milk-based protein shake respectively. These experimental results indicate that our approach is promising for handling non-linear thermal properties for materials that are similar or dissimilar to our base materials.

The accurate screening of osteoporosis is important for identifying persons at risk. The diagnosis of bone conditions using dual X-ray absorptiometry is limited to extracting areal bone mineral density (BMD) and fails to provide any structural information. Computed tomography (CT) is excellent for morphological imaging but not ideal for material quantification. Advanced photon-counting detector CT (PCD-CT) possesses high spectral sensitivity and material decomposition capabilities to simultaneously determine qualitative and quantitative information. In this study, we explored the diagnostic utility of PCD-CT to provide high-resolution 3-D imaging of bone microarchitecture and composition for the sensitive diagnosis of bone in untreated and ovariectomized rats. PCD-CT accurately decomposed the calcium content within hydroxyapatite phantoms (r = 0.99). MicroCT analysis of tibial bone revealed significant differences in the morphological parameters between the untreated and ovariectomized samples. However, differences in the structural parameters of the mandible between the treatment groups were not observed. BMD determined with microCT and calcium concentration decomposed using PCD-CT differed significantly between the treatment groups in both the tibia and mandible. Quantitative analysis with PCD-CT is sensitive in determining the distribution of calcium and water components in bone and may have utility in the screening and diagnosis of bone conditions such as osteoporosis.

Spectral photon-counting CT offers novel potentialities to achieve quantitative decomposition of material components, in comparison with traditional energy-integrating CT or dual-energy CT. Nonetheless, achieving accurate material decomposition, especially for low-concentration materials, is still extremely challenging for current sCT, due to restricted energy resolution stemming from the trade-off between the number of energy bins and undesired factors such as quantum noise. We propose to improve material decomposition by introducing the notion of super-energy-resolution in sCT. The super-energy-resolution material decomposition consists in learning the relationship between simulation and physical phantoms in image domain. To this end, a coupled dictionary learning method is utilized to learn such relationship in a pixel-wise way. The results on both physical phantoms and in vivo data showed that for the same decomposition method using lasso regularization, the proposed super-energy-resolution method achieves much higher decomposition accuracy and detection ability in contrast to traditional image-domain decomposition method using L1-norm regularization.

Traditional inspection technology for cargo or container imaging in customs and harbours is MeV X-ray radiography. The biggest limitation for this technology is the structural overlapping problem, which is inherent to radiography technology. MeV dual energy CT has a major advantage over radiography in that it can provide cross-section image, which is free of the structural overlapping problem. Besides, the recorded dual-energy projection data provides the ability for material decomposition. Electron density image and effective atom number image can be further calculated from the material decomposition coefficient images. However, the quality of effective atom number image can be very poor. The behind reasons are multifaceted. In this paper we proposed an Attenuation Image Referenced (AIR) effective atom number image calculation method for MeV dual-energy container CT imaging by using an image-domain neural network. The network has three channels as input and outputs with the estimated effective atom number image. The input three channels include the low and high-energy attenuation images and the effective atom number image that was directly calculated by using the derived formula. The network utilizes the low and high-energy attenuation image as guidance or reference for the restoration of effective atom number image. The network was trained on synthetic data, which is based on the shape of XCAT model but filled with materials that often appear in security imaging. The trained network also performed well on experimental data, showing the robustness and good generalization ability of the network. The quantitative analysis on the simulation and experimental data that comes from actual MeV dual-energy CT system showed the effectiveness of the proposed Attenuation Image Referenced (AIR) deep learning method for effective atom number image calculation.

Purpose: The purpose of this study was to evaluate the diagnostic accuracy when using various base material pairs (BMPs) in dual-energy computed tomography (DECT), and to establish corresponding diagnostic standards for assessing bone status through comparison with quantitative computed tomography (QCT). Methods: This prospective study enrolled a total of 469 patients who underwent both non-enhanced chest CT scans under conventional kVp and abdominal DECT. The bone densities of hydroxyapatite (water), hydroxyapatite (fat), hydroxyapatite (blood), calcium (water), and calcium (fat) (DHAP (water), DHAP (fat), DHAP (blood), DCa (water), and DCa (fat)) in the trabecular bone of vertebral bodies (T11–L1) were measured, along with bone mineral density (BMD) via QCT. Intraclass correlation coefficient (ICC) analysis was used to assess the agreement of the measurements. Spearman’s correlation test was performed to analyze the relationship between the DECT- and QCT-derived BMD. Receiver operator characteristic (ROC) curves were generated to determine the optimal diagnostic thresholds of various BMPs for diagnosing osteopenia and osteoporosis. Results: A total of 1371 vertebral bodies were measured, and QCT identified 393 with osteoporosis and 442 with osteopenia. Strong correlations were observed between DHAP (water), DHAP (fat), DHAP (blood), DCa (water), and DCa (fat) and the QCT-derived BMD. DHAP (water) showed the best predictive capability for osteopenia and osteoporosis. The area under the ROC curve, sensitivity, and specificity for identifying osteopenia were 0.956, 86.88%, and 88.91% with DHAP (water) ≤ 107.4 mg/cm3, respectively. The corresponding values for identifying osteoporosis were 0.999, 99.24%, and 99.53% with DHAP (water) ≤ 89.62 mg/cm3, respectively. Conclusions: Bone density measurement using various BMPs in DECT enables the quantification of vertebral BMD and the diagnosis of osteoporosis, with DHAP (water) having the highest diagnostic accuracy.

Dual energy CT (DECT) refers to the acquisition of CT images at two energy spectra and can provide information about tissue composition beyond that obtainable by conventional CT. The attenuation of a photon beam varies depends on the atomic number and density of the attenuating material and the energy of the incoming photon beam. This differential attenuation of the beam at varying energy levels forms the basis of DECT imaging and enables separation of materials with different atomic numbers but similar CT attenuation. DECT can be used to detect and quantify materials like iodine, calcium, or uric acid. Several post-processing techniques are available to generate virtual non-contrast images, iodine maps, virtual mono-chromatic images, Mixed or weighted images and material specific images. Although initially the concept of dual energy CT was introduced in 1970, it is only over the past two decades that it has been extensively used in clinical practice owing to advances in CT hardware and post-processing capabilities. There are numerous applications of DECT in Emergency radiology including stroke imaging to differentiate intracranial hemorrhage and contrast staining, diagnosis of pulmonary embolism, characterization of incidentally detected renal and adrenal lesions, to reduce beam and metal hardening artifacts, in identification of uric acid renal stones and in the diagnosis of gout. This review article aims to provide the emergency radiologist with an overview of the physics and basic principles of dual energy CT. In addition, we discuss the types of DECT acquisition and post processing techniques including newer advances such as photon-counting CT followed by a brief discussion on the applications of DECT in Emergency radiology.

As a new generation computed tomography (CT) technology, spectral CT has great potential in many aspects, especially in the identification and decomposition of materials. To achieve higher accuracy of materials decomposition, we propose a multi-constraint based nonlocal total variation (NLTV) method, named as MCNLTV. Because image-domain based material decomposition belongs to the two-step material decomposition method, the Filter Back-Projection (FBP) algorithm or SART algorithm is used to reconstruct spectral CT images in the first step. Then the material attenuation coefficient matrix is obtained from the reconstruction results. In the second step, MCNLTV regularization is utilized to obtain the material decomposition image. Both simulation experiments and real data experiments are carried out. Experiment results show that the proposed method can obtain higher accuracy of material decomposition than traditional total variation based material decomposition (TVMD), ROF-LLT regularization and direct inverse transformation (DI) for spectral CT.

Material decomposition in computed tomography is a method for differentiation and quantification of materials in a sample and it utilizes the energy dependence of the linear attenuation coefficient. In this study, a post-image reconstruction material decomposition method is constructed for a low-energy micro-CT setup using a photon counting x-ray detector. The low photon energy range (4–11 keV) allows for K-edge contrast separation of naturally occurring materials in organic tissue without the need of additional contrast agents. The decomposition method was verified using a phantom and its capability to decompose biomedical samples was evaluated with paraffin embedded human atherosclerotic plaques. Commonly, the necessary dual energy data for material decomposition is obtained by manipulating the emitted x-ray spectrum from the source. With the photon counting detector, this data was obtained by acquiring two energy window images on each side of the K-edge of one material in the sample. The samples were decomposed into three materials based on attenuation values in manually selected regions. The method shows a successful decomposition of the verification phantom and a distinct distribution of iron, calcium and paraffin in the atherosclerotic plaque samples. Though the decompositions are affected by beam hardening and ring artifacts, the method shows potential for spectral evaluation of biomedical samples.

In this paper, we propose a novel static CT system: triple planes CT (TPCT) system. Three source-detector planes in different horizontal directions are placed in the system. Line-array carbon nanotube sources with different voltages and sandwich detectors are used. Compared to conventional cone-beam CT and common inverse-geometry CT, the TPCT enables fast scanning and six-energy imaging. 1-D U-Net is applied to correct the severe scatter caused by the special geometry. The limited-view problem is solved by the hybrid reconstruction algorithm. A Monte-Carlo simulation is performed on a thorax phantom. Both the reconstruction results and decomposition results have good image quality and show the feasibility of our proposed TPCT imaging system.

One of the advantages of spectral computed tomography (CT) is it can achieve accurate material components using the material decomposition methods. The image-based material decomposition is a common method to obtain specific material components, and it can be divided into two steps: image reconstruction and post material decomposition. To obtain accurate material maps, the image reconstruction method mainly focuses on improving image quality by incorporating regularization priors. Very recently, the regularization priors are introduced into the post material decomposition procedure in the iterative image-based methods. Since the regularization priors can be incorporated into image reconstruction and post image-domain material decomposition, the performance of regularization by combining these two cases is still an open problem. To realize this goal, the material accuracy from those steps are first analyzed and compared. Then, to further improve the accuracy of decomposition materials, a two-step regularization based method is developed by incorporating priors into image reconstruction and post material decomposition. Both numerical simulation and preclinical mouse experiments are performed to demonstrate the advantages of the two-step regularization based method in improving material accuracy.

Contrast-enhanced Computed Tomography (CT) imaging is very helpful for the detection of tumor metastasis and cancer cells. It is one of the most effective means of clinical imaging examination. The objective of this paper is to realize the quantitative reconstruction of two contrast agents at the same time and obtain the non-contrast-enhanced images in four energy bins as an additional product. This paper presents an iterative material decomposition method based on volume conservation and K-edge characteristics. Compared with other K-edge based material decomposition algorithms, the proposed method can simultaneously quantify the two contrast agents and make better use of the energy information provided by the photon-counting detector. The proposed algorithm was compared with the image-domain K-edge subtraction, Angular Rejection and filtered back projection (FBP) reconstruction. Numerical simulation and Monte Carlo simulation were used to verify the effectiveness of the proposed algorithm. We found that the proposed algorithm has higher efficiency and is meaningful for the quantification of contrast agent concentration. The proposed algorithm can achieve the quantitative separation of dual K-edge contrast agent by a single scan, which is significant for reducing the radiation dose to patients and improving the efficiency of material decomposition.

Dual-energy mega-electron-volt (MeV) X-ray container radiography has become a well-established technique in customs security application, because of its material discrimination capability. The main difficulty of X-ray radiography is dealing with the materials overlapping problem. When two or more materials exist along the X-ray beam path, its material discrimination performance will be obviously affected. Computed tomography (CT) collects many X-ray measurements taken from different angles surrounding an object to produce cross-sectional (tomographic) images of the scanned object. Therefore, CT can provide real 3-D images inside the object. However, due to the bulky container volume and complex types of cargos, it is very hard to develop such a huge CT system for container inspection. To the best of our knowledge, there has no such commercial X-ray CT system for container inspection yet. This paper presents the design of a dual MeV energy X-ray CT system for cargo container inspection which uses an accelerator with fast 6/9 MeV switching spectra, an arc detector array and rotating mechanism. A dual MeV energy X-ray CT image reconstruction and material decomposition algorithm are developed. An experimental system was built with the same accelerator and detector array as used in the designed container CT system. Experimental results that prove the validity and effectiveness of the algorithm and CT system are presented.

The present study evaluates the diagnostic reliability of virtual non-contrast (VNC) images acquired with the first photon counting CT scanner that is approved for clinical use by comparing quantitative image properties of VNC and true non-contrast (TNC) images. Seventy-two patients were retrospectively enrolled in this study. VNC images reconstructed from the arterial (VNCa) and the portalvenous (VNCv) phase were compared to TNC images. In addition, consistency between VNCa and VNCv images was evaluated. Regions of interest (ROI) were drawn in the following areas: liver, spleen, kidney, aorta, muscle, fat and bone. Comparison of VNCa and VNCv images revealed a mean offset of less than 4 HU in all tissues. The greatest difference between TNC and VNC images was found in spongious bone (VNCv 86.13 HU ± 28.44, p < 0.001). Excluding measurements in spongious bone, differences between TNC and VNCv of 10 HU or less were found in 40% (VNCa 36%) and differences of 15 HU or less were found in 72% (VNCa 68%) of all measurements. The underlying algorithm for the subtraction of iodine works in principle but requires adjustments. Until then, special caution should be exercised when using VNC images in routine clinical practice.

Material decomposition methods are used to identify and quantify multiple tissue components in spectral CT but there is no published method to quantify the misidentification of materials. This paper describes a new method for assessing misidentification and mis-quantification in spectral CT. We scanned a phantom containing gadolinium (1, 2, 4, 8 mg/mL), hydroxyapatite (54.3, 211.7, 808.5 mg/mL), water and vegetable oil using a MARS spectral scanner equipped with a poly-energetic X-ray source operated at 118 kVp and a CdTe Medipix3RX camera. Two imaging protocols were used; both with and without 0.375 mm external brass filter. A proprietary material decomposition method identified voxels as gadolinium, hydroxyapatite, lipid or water. Sensitivity and specificity information was used to evaluate material misidentification. Biological samples were also scanned. There were marked differences in identification and quantification between the two protocols even though spectral and linear correlation of gadolinium and hydroxyapatite in the reconstructed images was high and no qualitative segmentation differences in the material decomposed images were observed. At 8 mg/mL, gadolinium was correctly identified for both protocols, but concentration was underestimated by over half for the unfiltered protocol. At 1 mg/mL, gadolinium was misidentified in 38% of voxels for the filtered protocol and 58% of voxels for the unfiltered protocol. Hydroxyapatite was correctly identified at the two higher concentrations for both protocols, but mis-quantified for the unfiltered protocol. Gadolinium concentration as measured in the biological specimen showed a two-fold difference between protocols. In future, this methodology could be used to compare and optimize scanning protocols, image reconstruction methods, and methods for material differentiation in spectral CT.