Material Decomposition Technique Research Articles

Coronary artery calcium quantification technique using dual energy material decomposition: a simulation study

Coronary artery calcification is a significant predictor of cardiovascular disease, with current detection methods like Agatston scoring having limitations in sensitivity. This study aimed to evaluate the effectiveness of a novel CAC quantification method using dual-energy material decomposition, particularly its ability to detect low-density calcium and microcalcifications. A simulation study was conducted comparing the dual-energy material decomposition technique against the established Agatston scoring method and the newer volume fraction calcium mass technique. Detection accuracy and calcium mass measurement were the primary evaluation metrics. The dual-energy material decomposition technique demonstrated fewer false negatives than both Agatston scoring and volume fraction calcium mass, indicating higher sensitivity. In low-density phantom measurements, material decomposition resulted in only 7.41% false-negative (CAC = 0) measurements compared to 83.95% for Agatston scoring. For high-density phantoms, false negatives were removed (0.0%) compared to 20.99% in Agatston scoring. The dual-energy material decomposition technique presents a more sensitive and reliable method for CAC quantification.

Artificial intelligence-based image-domain material decomposition in single-energy computed tomography for head and neck cancer.

While dual-energy computed tomography (DECT) images provide clinically useful information than single-energy CT (SECT), SECT remains the most widely used CT system globally, and only a few institutions can use DECT. This study aimed to establish an artificial intelligence (AI)-based image-domain material decomposition technique using multiple keV-output learning of virtual monochromatic images (VMIs) to create DECT-equivalent images from SECT images. This study involved 82 patients with head and neck cancer. Of these, the AI model was built with data from the 67 patients with only DECT scans, while 15 patients with both SECT and DECT scans were used for SECT testing. Our AI model generated VMI50keV and VMI100keV from VMI70keV equivalent to 120-kVp SECT images. We introduced a loss function for material density images (MDIs) in addition to the loss for VMIs. For comparison, we trained the same model with the loss for VMIs only. DECT-equivalent images were generated from SECT images and compared with the true DECT images. The prediction time was 5.4s per patient. The proposed method with the MDI loss function quantitatively provided more accurate DECT-equivalent images than the model trained with the loss for VMIs only. Using real 120-kVp SECT images, the trained model produced precise DECT images of excellent quality. In this study, we developed an AI-based material decomposition approach for head and neck cancer patients by introducing the loss function for MDIs via multiple keV-output learning. Our results suggest the feasibility of AI-based image-domain material decomposition in a conventional SECT system without a DECT scanner.

Opportunistic screening for osteoporosis using enhanced images based on dual-energy computed tomography material decomposition: a comparison with quantitative computed tomography.

Many patients with malignant tumors require chemotherapy and radiation therapy, which can result in a decline in physical function and potentially influence bone mineral density (BMD). Furthermore, these treatments necessitate enhanced computed tomography (CT) scans for determining disease staging or treatment outcomes, and opportunistic screening with available imaging data is beneficial for patients at high risk for osteoporosis if existing imaging data can be used. The study aimed to investigate the feasibility of opportunistic screening for osteoporosis using enhanced CT based on a dual-energy CT (DECT) material decomposition technique. We prospectively enrolled 346 consecutive patients who underwent abdominal unenhanced and triphasic contrast-enhanced CT (arterial, portal venous, and delayed phases) between June 2021 and June 2022. The BMD, and the density of hydroxyapatite (HAP) on HAP-iodine images and calcium (Ca) on Ca-iodine images were measured on the L1-L3 vertebral bodies. The iodine intake was recorded. Pearson analysis was conducted to assess the correlation between iodine intake and the density values in three phases and the correlation between BMD and the densities of HAP and Ca. Furthermore, linear regression was employed for quantitative evaluation. Bland-Altman analysis was used to evaluate the agreement between calculated BMD derived from DECT (BMD-DECT) and reference BMD derived from quantitative CT (BMD-QCT). Receiver operating characteristic (ROC) analysis was applied to assess the diagnostic efficacy. The HAP and Ca density of the L1-L3 vertebral bodies did not differ significantly among the three phases of contrast-enhanced CT (F=0.001-0.049; P>0.05). Significant positive correlations were found between HAP, Ca densities, and BMD (HAP-BMD: r=0.9472, R2=0.8973; Ca-BMD: r=0.9470, R2=0.8968; all P<0.001). Bland-Altman plots showed high agreement between BMD-DECT and BMD-QCT. The area under the curve (AUC) using HAP and Ca measurements was 0.963 [95% confidence interval (CI): 0.937-0.980] and 0.964 (95% CI: 0.939-0.981), respectively, for diagnosing osteoporosis and was 0.951 (95% CI: 0.917-0.973) and 0.950 (95% CI: 0.916-0.973), respectively, for diagnosing osteopenia. The HAP and Ca density measurements generated through the material decomposition technique in DECT have good diagnostic performances in assessing BMD, which offers a new perspective for opportunistic screening of osteoporosis on contrast-enhanced CT.

Multi-energy CT material decomposition using graph model improved CNN.

In spectral CT imaging, the coefficient image of the basis material obtained by the material decomposition technique can estimate the tissue composition, and its accuracy directly affects the disease diagnosis. Although the precision of material decomposition is increased by employing convolutional neural networks (CNN), extracting the non-local features from the CT image is restricted using the traditional CNN convolution operator. A graph model built by multi-scale non-local self-similar patterns is introduced into multi-material decomposition (MMD). We proposed a novel MMD method based on graph edge-conditioned convolution U-net (GECCU-net) to enhance material image quality. The GECCU-net focuses on developing a multi-scale encoder. At the network coding stage, three paths are applied to capture comprehensive image features. The local and non-local feature aggregation (LNFA) blocks are designed to integrate the local and non-local features from different paths. The graph edge-conditioned convolution based on non-Euclidean space excavates the non-local features. A hybrid loss function is defined to accommodate multi-scale input images and avoid over-smoothing of results. The proposed network is compared quantitatively with base CNN models on the simulated and real datasets. The material images generated by GECCU-net have less noise and artifacts while retaining more information on tissue. The Structural SIMilarity (SSIM) of the obtained abdomen and chest water maps reaches 0.9976 and 0.9990, respectively, and the RMSE reduces to 0.1218 and 0.4903g/cm3. The proposed method can improve MMD performance and has potential applications.

Implementation of dual-energy material decomposition technique in stationary CT baggage scanner withπ-angle sparsity for enhancing threat detection

Two-dimensional X-ray inspection systems are widely used in aviation security applications; however, they have inherent limitations in recognizing the three-dimensional (3D) shapes of hidden objects. Therefore, there is a growing demand for the implementation of advanced 3D X-ray inspection systems at airports for more accurate detection of threats in luggage and personal belongings. In this study, we designed a new stationary computed tomography (CT) baggage scanner with π-angle sparsity (i.e., 20 pairs of X-ray sources and line detectors were placed within a scan angle of 180°) and compressed sensing (CS)-based reconstruction, and implemented a dual-energy material decomposition (DEMD) technique in the proposed system to separate soft and dense materials of an examined object to enhance threat detection. To validate the efficacy of the proposed approach (CS/180°/P20), we conducted a feasibility study using numerical simulation before its practical implementation. Polychromatic projections were emulated at X-ray tube voltages of 60 and 140 kVp, and DEMD was applied to the projections prior to CT reconstruction. Conventional and dual-energy CT images were reconstructed using both standard filtered-backprojection (FBP) and state-of-the-art CS-based algorithms to compare the image quality. According to our simulation results, the CS-reconstructed images were almost unaffected by the clearly evident streak artifacts on the FBP-reconstructed images because of the use of 20 extreme sparse-view projections, and the image quality of the dual-energy CT images obtained using the proposed CT configuration was similar to that obtained using the conventional CT configuration with 720 dense projections, indicating the efficacy of the proposed approach. Consequently, high-quality dual-energy CT images of soft and dense materials were successfully obtained using the proposed stationary CT configuration.

Diagnostic Accuracy of Dual-Energy CT Material Decomposition Technique for Assessing Bone Status Compared with Quantitative Computed Tomography.

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). 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. 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. 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.

Spectral CT: Current Liver Applications.

Using two different energy levels, dual-energy computed tomography (DECT) allows for material differentiation, improves image quality and iodine conspicuity, and allows researchers the opportunity to determine iodine contrast and radiation dose reduction. Several commercialized platforms with different acquisition techniques are constantly being improved. Furthermore, DECT clinical applications and advantages are continually being reported in a wide range of diseases. We aimed to review the current applications of and challenges in using DECT in the treatment of liver diseases. The greater contrast provided by low-energy reconstructed images and the capability of iodine quantification have been mostly valuable for lesion detection and characterization, accurate staging, treatment response assessment, and thrombi characterization. Material decomposition techniques allow for the non-invasive quantification of fat/iron deposition and fibrosis. Reduced image quality with larger body sizes, cross-vendor and scanner variability, and long reconstruction time are among the limitations of DECT. Promising techniques for improving image quality with lower radiation dose include the deep learning imaging reconstruction method and novel spectral photon-counting computed tomography.

Feasibility study of deep-learning-based bone suppression incorporated with single-energy material decomposition technique in chest X-rays.

To improve the detection of lung abnormalities in chest X-rays by accurately suppressing overlapping bone structures in the lung area. According to literature on missed lung cancer in chest X-rays, such structures are a significant cause of chest-related diagnostic errors. This study presents a deep-learning-based bone suppression method where a residual U-Net model is trained for chest X-rays using data set generated from the single-energy material decomposition (SEMD) technique on CT. Synthetic projection images and soft-tissue selective images were obtained from the CT data set via the SEMD, which were then used as the input and label data of the U-Net network. The trained network was tested on synthetic chest X-rays and two real chest radiographs. Bone-suppressed images of the real chest radiographs obtained by the proposed method were similar to the results from the American Association of Physicists in Medicine lung CT data; pulmonary nodules in the soft-tissue selective images appeared more clearly than in the synthetic projection images. The peak signal-to-noise ratio and structural similarity values measured between the output and the corresponding label images were approximately 17.85 and 0.90, respectively. The proposed method effectively yielded bone-suppressed chest X-ray images, indicating its clinical usefulness, and it can improve the detection of lung abnormalities in chest X-rays. The idea of using SEMD to obtain large amounts of paired images for deep-learning-based bone suppression algorithms is novel.

Quantitative Evaluation of Nonalcoholic Fatty Liver in Rat by Material Decomposition Techniques using Rapid-switching Dual Energy CT

To evaluate the material decomposition (MD) techniques in rapid kVp switching dual-energy CT (rsDECT) for quantifying liver fat content in rats with nonalcoholic fatty liver. Fifty male Sprague-Dawley (SD) rats were divided into study group (n=37) and control group (n=13) and underwent rsDECT examination at different intervals. All the data analysis was performed using AW4.7 workstation. The fat contents under the traditional fat(water), fat(blood), and fat(muscle) material decomposition (MD) images and the fat volume fraction (FVF) from the liver fat maps generated using multi-material decomposition (MMD) technique were measured. The pathological grades (grade 0, 1, 2 and 3) of fatty liver were determined after euthanasia. The measurement differences among different grades and the correlation of measurements with different grades was analyzed using ANOVA and Spearman correlation, respectively. A receiver operating characteristics (ROC) curve was used to analyze the diagnostic efficacies of fat contents and FVF. There were statistically significant differences in FVF and fat contents under fat(water), fat(blood), fat(muscle) based MD images among different grades. These values correlated well with the pathological grades (R-value: 0.90, 0.75, 0.79, 0.80, all p <0.001), with FVF having the highest correlation. The area-under-the-curve in ROC of using FVF was the highest, with the cut-off value of 0.92 for sensitivity of 89.2% and specificity of 100%. The rsDECT MD techniques could quantitatively evaluate the fat content of fatty liver in rat, with the FVF from MMD having the highest correlation with pathological grades.

Spectral CT imaging of human osteoarthritic cartilage via quantitative assessment of glycosaminoglycan content using multiple contrast agents.

Detection of early osteoarthritis to stabilize or reverse the damage to articular cartilage would improve patient function, reduce disability, and limit the need for joint replacement. In this study, we investigated nondestructive photon-processing spectral computed tomography (CT) for the quantitative measurement of the glycosaminoglycan (GAG) content compared to destructive histological and biochemical assay techniques in normal and osteoarthritic tissues. Cartilage-bone cores from healthy bovine stifles were incubated in 50% ioxaglate (Hexabrix®) or 100% gadobenate dimeglumine (MultiHance®). A photon-processing spectral CT (MARS) scanner with a CdTe-Medipix3RX detector imaged samples. Calibration phantoms of ioxaglate and gadobenate dimeglumine were used to determine iodine and gadolinium concentrations from photon-processing spectral CT images to correlate with the GAG content measured using a dimethylmethylene blue assay. The zonal distribution of GAG was compared between photon-processing spectral CT images and histological sections. Furthermore, discrimination and quantification of GAG in osteoarthritic human tibial plateau tissue using the same contrast agents were demonstrated. Contrast agent concentrations were inversely related to the GAG content. The GAG concentration increased from 25 μg/ml (85 mg/ml iodine or 43 mg/ml gadolinium) in the superficial layer to 75 μg/ml (65 mg/ml iodine or 37 mg/ml gadolinium) in the deep layer of healthy bovine cartilage. Deep zone articular cartilage could be distinguished from subchondral bone by utilizing the material decomposition technique. Photon-processing spectral CT images correlated with histological sections in healthy and osteoarthritic tissues. Post-imaging material decomposition was able to quantify the GAG content and distribution throughout healthy and osteoarthritic cartilage using Hexabrix® and MultiHance® while differentiating the underlying subchondral bone.

Non-contrast dual-energy CT virtual ischemia maps accurately\xa0estimate\xa0ischemic core size\xa0in large-vessel occlusive\xa0stroke

Dual-energy CT (DECT) material decomposition techniques may better detect edema within cerebral infarcts than conventional non-contrast CT (NCCT). This study compared if Virtual Ischemia Maps (VIM) derived from non-contrast DECT of patients with acute ischemic stroke due to large-vessel occlusion (AIS-LVO) are superior to NCCT for ischemic core estimation, compared against reference-standard DWI-MRI. Only patients whose baseline ischemic core was most likely to remain stable on follow-up MRI were included, defined as those with excellent post-thrombectomy revascularization or no perfusion mismatch. Twenty-four consecutive AIS-LVO patients with baseline non-contrast DECT, CT perfusion (CTP), and DWI-MRI were analyzed. The primary outcome measure was agreement between volumetric manually segmented VIM, NCCT, and automatically segmented CTP estimates of the ischemic core relative to manually segmented DWI volumes. Volume agreement was assessed using Bland–Altman plots and comparison of CT to DWI volume ratios. DWI volumes were better approximated by VIM than NCCT (VIM/DWI ratio 0.68 ± 0.35 vs. NCCT/DWI ratio 0.34 ± 0.35; P < 0.001) or CTP (CTP/DWI ratio 0.45 ± 0.67; P < 0.001), and VIM best correlated with DWI (rVIM = 0.90; rNCCT = 0.75; rCTP = 0.77; P < 0.001). Bland–Altman analyses indicated significantly greater agreement between DWI and VIM than NCCT core volumes (mean bias 0.60 [95%AI 0.39–0.82] vs. 0.20 [95%AI 0.11–0.30]). We conclude that DECT VIM estimates the ischemic core in AIS-LVO patients more accurately than NCCT.

Computed tomography combined with a material decomposition technique using a compact deuterium-deuterium (D-D) fast neutron generator

Neutron cross-sections have an energy dependence which is uniquely varying according to isotopic composition. By measuring the local neutron beam attenuation of an object as a function of energy, this can be related to its elemental composition. Therefore material decomposition measurements using a deuterium-deuterium (D-D) compact fast neutron generator in an energy-selective fashion have been carried out. Previous work described determining setup-specific effective reference cross-section data for homogeneous samples. This work presents first tests of this non-destructive analysis principle in a 2D computed tomography (CT) setup using heterogeneous samples, based on the aforementioned reference cross-sections. Measurement data were collected at five angles around the neutron generator corresponding to five effective energies. At each angle a CT reconstruction was performed to obtain energy-specific CT images. These CT images were use to calculate an elemental decomposition of the samples of interest. Good agreement between theory and experiment was found for most of the samples, while the more challenging cases highlighted limitations of the technique. These first tomographic measurements are discussed along with an outlook for further developments of the technique.

Quantitative Knee Arthrography in a Large Animal Model of Osteoarthritis Using Photon-Counting Detector CT.

The aim of this study was to grade cartilage damage in a swine model of osteoarthritis using a whole-body photon-counting detector (PCD) CT. A multienergy phantom containing gadolinium (Gd) (2, 4, 8, and 16 mg/mL) and hydroxyapatite (200 and 400 mg/cc) was scanned using a PCD-CT system (48 × 0.25 mm collimation, 80 kV, 800 mAs, D50 reconstruction kernel) to serve as calibration for material decomposition and to assess quantification accuracy. Osteoarthritis was induced in Yucatan miniswine (n = 8) using 1.2 mg monoiodoacetate (MIA) injected into a randomized knee, whereas the contralateral control knee received saline. Twenty-one days later, a contrast bolus (gadoterate meglumine, 4 mL/knee) was intra-articularly administered into both knees. The knees were simultaneously scanned on the PCD-CT system (48 × 0.25 mm collimation, 80 kV, 800 mAs). Multienergy images were reconstructed with a sharp "V71" kernel and a quantitative "D50" kernel. Image denoising was applied to the V71 images before grading cartilage damage, and an iterative material decomposition technique was applied to D50 images to generate the Gd maps. Two radiologists blinded to the knee injection status graded the cartilage integrity based on a modified International Cartilage Repair Society scoring system. Histology was performed on excised cartilage using methylene blue/basic fuchsin. Statistical analysis of grade distribution was performed using an exact test of omnibus symmetry with P < 0.05 considered significant. Material decomposed images from the multienergy phantom scan showed delineation and quantification of Gd and hydroxyapatite with a root-mean-squared error of 0.3 mg/mL and 18.4 mg/cc, respectively. In the animal cohort, the radiologists reported chondromalacia in the MIA knees with International Cartilage Repair Society scores ranging from grade 1 (cartilage heterogeneity, n = 4 knees) to grade 3 (up to 100% cartilage loss, n = 4 knees). Grade 1 was characterized by cartilage heterogeneity and increased joint space in the patellofemoral compartment, whereas grade 3 was characterized by cartilage erosion and bone-on-bone articulation in the patellofemoral compartment. All control knees were scored as grade 0 (normal cartilage). Significant difference (P = 0.004) was observed in the grade distribution between the MIA and control knees. Gross examination of the excised knees showed cartilage lesions in the grade 3 MIA knees. The Gd maps from material decomposition showed lower contrast levels in the joint space of the MIA knee compared with the contralateral control knee due to joint effusion. Histology revealed chondrocyte loss in the MIA knee cartilage confirming the chondrotoxic effects of MIA on cartilage matrix. We demonstrated a high-resolution and quantitative PCD-CT arthrography technique for grading cartilage damage in a large animal model of osteoarthritis. Photon-counting detector CT offers simultaneous high-resolution and multienergy imaging capabilities that allowed morphological assessment of cartilage loss and quantification of contrast levels in the joint as a marker of joint disease. Cartilage damage in the MIA knees was graded using PCD-CT images, and the image-based findings were further confirmed using histology and gross examination of the excised knees.

Detection of left atrial appendage thrombi by third-generation dual-source dual-energy CT: Iodine concentration versus conventional enhancement measurements

BackgroundDual-energy computed tomography (DECT) can differentiate iodine from other materials through the material decomposition technique. The purpose of this study was to compare the diagnostic performance of DECT-derived iodine concentration (mg/ml) with conventional enhancement measurements (HU), in detecting left atrial appendage (LAA) thrombi and differentiating thrombi from circulatory stasis in atrial fibrillation (AF) patients referred for catheter ablation. MethodsConsecutive patients were prospectively recruited and scanned using a third-generation dual-source CT system in dual-energy mode. Regions of interest were placed inside the filling defect in the LAA and ascending aorta (AA) of the same sections, to determine iodine concentration and the LAA/AA HU ratio. The diagnostic performance of iodine concentration and LAA/AA HU ratios were compared using transesophageal echocardiography (TEE) as the reference standard. ResultsAmong 302 patients, 10 thrombi and 27 cases with spontaneous echo contrast (SEC) were detected by TEE. Diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of iodine concentration were superior to those of LAA/AA HU ratios (iodine concentration: 99.7%, 100%, 99.7%, 90.9%, and 100% vs. LAA/AA HU ratios: 96.0%, 100%, 95.9%, 45.5%, and 100%) in detecting LAA thrombi. The area under the receiver operating characteristic curve of iodine concentration (0.996; 0.898–1.000) was significantly larger than that of the LAA/AA HU ratio (0.881; 0.733–0.964) in differentiating thrombi from circulatory stasis (p < 0.05). ConclusionsDECT-derived iodine concentration was associated with improved diagnostic accuracy compared with conventional enhancement measurements in detecting LAA thrombi and differentiating thrombi from circulatory stasis in AF patients.

The influence of liver fat deposition on the quantification of the liver-iron fraction using fast-kilovolt-peak switching dual-energy CT imaging and material decomposition technique: an in vitro experimental study.

To assess the feasibility of dual-energy spectral computed tomography (DECT) for quantifying the liver iron content (LIC) with material decomposition (MD) technique in vitro. Liver-iron mixture samples (model A) and liver-iron-fat mixture samples (model B) were prepare and scanned by a single source DECT using GSI mode with successive tube currents of 200, 320, and 485 mA. A standard algorithm of 1.25 mm was used to reconstruct iron (fat) MD images and iron (water) MD images. The iron concentrations of all samples were measured and analyzed by Spearman's rank correlation and linear regression analysis. Significant positive linear correlations were found between virtual iron content (VIC) and LIC in the absence of fat (model A) and in the presence of fat (model B) in the range of LIC 0 to 25 mg/mL. The lines of best fit to model A had slopes around 1.1 and an intercept around (-1.5) mg/mL for iron (water) MD images, and had slopes around 1.1 and an intercept around (-10) mg/mL for iron (fat) MD images. The lines of best fit to the model B had slopes around 1.5 and an intercept around (-15) mg/mL. At the same value of LIC (LIC >0), the VIC values of model A were always higher than those of model B. At the high value of LIC (12.5 mg/mL), the VIC values of model B were similar, but they differed greatly from those of model A. The fast-kilovolt-peak switching dual-energy CT imaging and MD techniques allow for quantification of iron content. Fat and the post-reconstruction algorithm of iron (fat) MD images, were confounding factors, and led to the underestimation and overestimation of LIC, respectively.

The utility of dual-energy CT collagen material decomposition technique for the visualization of tendon grafts after knee ligament reconstruction

ObjectiveThe purpose of this article is to assess whether dual-energy CT (DECT) collagen material decomposition technique could reliably depict graft integrity in patients with knee ligament reconstruction. MethodsSeventy patients (mean age, 29.8 years; age range, 15–57 years; 61 men, 9 women) who underwent knee DECT, from June 2016 to January 2018, after knee ligament reconstruction were included in our study. A total of 92 intact tendon grafts (autograft, n = 37; allograft, n = 50), confirmed by MRI and clinical assessment or second-look arthroscopy of the operated knee, were evaluated. The type and number of reconstructed ligaments were as follows: anterior cruciate ligament (ACL) (n = 30), posterior cruciate ligament (PCL) (n = 20), medial collateral ligament (MCL) (n = 12), lateral collateral ligament (LCL) (n = 10), posterolateral ligamentous complex (PLC) (n = 7), anterolateral ligament (ALL) (n = 6), and medial patellofemoral ligament (MPFL) (n = 7). All DECT tendon-specific color mapping images were analyzed by two radiologists independently. Each reconstructed ligament was divided into proximal, middle, and distal portion and rated separately using a three-point scale (0 = absent 'dual-energy color staining'; 1 = partial 'dual-energy color staining'; 2 = full 'dual-energy color staining'). ResultsThe mean of total visualization scores of reconstructed ligaments were 5 or more out of 6 points (PCL: 5.0 ± 0.8; MCL: 5.4 ± 0.7; LCL: 5.5 ± 0.5; PLC: 5.4 ± 1.0; ALL: 5.3 ± 0.6; MPFL: 5.8 ± 0.5), except for ACL (4.3 ± 1.7). No significant difference was observed in the mean of total visualization scores between the autografts and allografts (p > 0.05). The frequency of the score 0 was greater than 10% for the ACL group (15.7%), while less than 5% or 0% for the other groups. Overall, substantial to almost perfect interobserver agreement (range 0.71–0.93) was found for all types of ligaments. ConclusionDECT collagen material decomposition technique could be a valuable tool to qualitatively display tendon grafts in the patients with knee ligament reconstruction, but more caution would be needed to assess ACL graft.

The influence of liver fat deposition on liver iron overload quantitative assessment by fast-kilovolt-peak switching dual-energy CT imaging and material decomposition technique: a vitro experiment study

Objective To investigate the influence of liver fat deposition on the quantification of the liver iron overload using fast-kilovolt-peak switching dual-energy CT imaging and material decomposition technique. Methods A total of 20 healthy SD rats were taken to make 18 PVC tube of homogenate of fresh liver tissue. The dextran with concentration of 50, 40, 30, 20, 10 and 0 mg/ml were mixed with rat liver homogenate and triglyceride with three different concentrations (add fat with volume percentage of 60%, 30%, 10% and to simulate severe, moderate and mild fatty liver respectively). All samples were placed in standard phantom according to the order of iron concentration from high to low and scanned by GE Revolution CT 256 slices scanner in GSI mode with rapid tube voltage switching between 80 and 140 kVp and with tube current 200 mA,320 mA, 485 mA respectively. The images of iron (fat)-based substance pair were reconstructed and the virtual iron concentration (VIC) value were recorded. The correlation between VIC and the actual liver concentration (LIC) of the three sets of tube currents (200, 320, 485 mA) was analyzed by Spearman correlation analysis and linear regression. Results LIC and VIC were highly positively correlated with the liver iron deposition model under different tube currents (r value was 0.900 to 1.000, P<0.05). The presence of fat will result in the decrease of VIC value. In a certain X-ray energy range and certain liver iron concentration, the higher the fat content, the more liver iron concentration underestimation were happened in VIC. At 200, 320 and 485 mA, the crossing points of linear equations for 30% and 10% fatty liver iron deposition models were located at VIC=12.682, 12.470 and 13.447 mg/cm3, respectively. Conclusions The fast-kilovolt-peak switching dual-energy CT imaging and material decomposition techniques can be used for quantitative evaluation of liver iron with hepatic steatosis. The presence of fat will lead to a decrease in VIC measurement. Key words: Tomography, X-ray computed; Substances separation; Liver; Iron; Fat

Extension of the Fermi–Eyges most-likely path in heterogeneous medium with prior knowledge information

Particle imaging suffers from poor spatial resolution due to the multiple Coulomb scattering deflections undergone by the particles throughout their path. To account for these deflections, a most-likely path (MLP) formalism was developed based on a Bayesian adaption of the Fermi–Eyges theory. Previous work calculated the MLP formalism in a homogeneous water medium as an initial estimate. However, this potentially reduces the accuracy of the MLP estimate as well as the achievable resolution of the subsequent tomographic reconstruction. This work investigates the potential gain of introducing prior-knowledge on the medium composition and density to improve the MLP accuracy. To do so, a Monte Carlo (MC) Geant4 algorithm was used to simulate protons () crossing three different anthropomorphic phantoms representing the lung, abdomen, and head. The prior-knowledge information is gathered from (1) the MC simulation for ground-truth (MLP-GT), or from (2) a recent DECT material decomposition technique (MLP-DECT). The reconstructed path accuracy using prior-knowledge methods is compared with (3) the path reconstructed in homogeneous water (MLP-Water) and (4) a path reconstruction method where the proton path is projected onto a Hull at the boundary of the phantom with a subsequent MLP-Water calculation (MLP-Hull). For each path reconstruction method, the maximal root-mean-square error (RMSmax) is compared between the reconstructed and the MC path. In every phantom, the RMSmax is decreased between the MLP-Water and the three other path algorithms that take into account heterogeneities ( for the lung, for the abdomen and for the head), with no significant differences between each (MLP-DECT, MLP-GT and MLP-Hull). In conclusion, the introduction of prior-knowledge in the MLP formalism decreases the RMS uncertainty to the MC path, but no further than the use of a simpler Hull contour algorithm. The use of this Hull algorithm is suggested for future particle imaging applications.

Imaging the Spine with Dual-Energy CT

This review article aims to summarize the available literature regarding the utility of Dual-Energy CT (DECT) in spine imaging. DECT using virtual non-calcium images, virtual monoenergetic images, and material decomposition techniques have shown promise in bone marrow edema visualization associated with vertebral fractures, metallic artifact reduction related to spinal hardware, axial monosodium urate deposit detection, bone marrow composition evaluation, and bone marrow lesion identification. In our experience, DECT may also be an assuring technique in evaluation of disc degeneration, and disc herniation. DECT imaging of the spine has numerous clinical applications, potential advantages over single-energy CT and other imaging modalities, and future promising avenues.

An Image-Domain Contrast Material Extraction Method for Dual-Energy Computed Tomography.

Conventional material decomposition techniques for dual-energy computed tomography (CT) assume mass or volume conservation, where the CT number of each voxel is fully assigned to predefined materials. We present an image-domain contrast material extraction process (CMEP) method that preferentially extracts contrast-producing materials while leaving the remaining image intact. Image processing freeware (Fiji) is used to perform consecutive arithmetic operations on a dual-energy ratio map to generate masks, which are then applied to the original images to generate material-specific images. First, a low-energy image is divided by a high-energy image to generate a ratio map. The ratio map is then split into material-specific masks. Ratio intervals known to correspond to particular materials (eg, iodine, calcium) are assigned a multiplier of 1, whereas ratio values in between these intervals are assigned linear gradients from 0 to 1. The masks are then multiplied by an original CT image to produce material-specific images. The method was tested quantitatively at dual-source CT and rapid kVp-switching CT (RSCT) with phantoms using pure and mixed formulations of tungsten, calcium, and iodine. Errors were evaluated by comparing the known material concentrations with those derived from the CMEP material-specific images. Further qualitative evaluation was performed in vivo at RSCT with a rabbit model using identical CMEP parameters to the phantom. Orally administered tungsten, vascularly administered iodine, and skeletal calcium were used as the 3 contrast materials. All 5 material combinations-tungsten, iodine, and calcium, and mixtures of tungsten-calcium and iodine-calcium-showed distinct dual-energy ratios, largely independent of material concentration at both dual-source CT and RSCT. The CMEP was successful in both phantoms and in vivo. For pure contrast materials in the phantom, the maximum error between the known and CMEP-derived material concentrations was 0.9 mg/mL, 24.9 mg/mL, and 0.4 mg/mL for iodine, calcium, and tungsten respectively. Mixtures of iodine and calcium showed the highest discrepancies, which reflected the sensitivity of iodine to the image-type chosen for the extraction of the final material-specific image. The rabbit model was able to clearly show the 3 extracted material phases, vascular iodine, oral tungsten, and skeletal calcium. Some skeletal calcium was misassigned to the extracted iodine image; however, this did not impede the depiction of the vasculature. The CMEP is a straightforward, image-domain approach to extract material signal at dual-energy CT. It has particular value for separation of experimental high-Z contrast elements from conventional iodine contrast or calcium, even when the exact attenuation coefficient profiles of desired contrast materials may be unknown. The CMEP is readily implemented in the image-domain within freeware, and can be adapted for use with images from multiple vendors.