Dual-energy CT Imaging Research Articles

Dual-energy CT in the differentiation of crystal depositions of the wrist: does it have added value?

To evaluate the ability of dual-energy computed tomography (DECT) to improve diagnostic discrimination between gout and other crystal arthropathies such as calcium pyrophosphate deposition disease (CPPD) of the wrist in a clinical patient population. This retrospective case-control study included 29 patients with either gout (case group; n = 9) or CPPD (control group; n = 20) who underwent DECT of the wrist for clinically suspected crystal arthropathy. Color-coded urate and enhanced calcium as well as virtual 120 kVe blended images were reconstructed from the DECT datasets. Two independent and blinded readers evaluated each reconstructed dataset for the presence of depositions in 17 predefined regions. Additionally, a global diagnosis was made first for 120 kVe images only, based solely on morphologic criteria, and subsequently for all reconstructed images. Sensitivity for the global diagnosis of gout was 1.0 (95% CI 0.63-1) for both DECT and 120 kVe images with specificities of 0.70 (95% CI 0.46-0.87) for DECT and 0.80 (95% CI 0.56-0.93) for 120 kVe images. Color-coded DECT images did not detect more depositions than monochrome standard CT images. Discrimination of crystal arthropathies of the wrist is limited using DECT and primarily relying on color-coded images. Evaluation of morphologic criteria on standard CT images is essential for accurate diagnosis.

Accurate Multi-Material Decomposition in Dual-Energy CT: A Phantom Study

DUAL-energy computed tomography (DECT) differentiates materials by exploiting the varying material linear attenuation coefficients (LACs) for different x-ray energy spectra. Multi-material decomposition (MMD) is a particularly attractive DECT clinical application to distinguish the complicated material components within the human body. One prior material assisted (PMA) image domain MMD method was implemented, but has suffered from inaccurate decomposition, magnified noise, and expensive computation. To suppress the noise, we implemented a statistical MMD (SMMD) algorithm, which applied the statistical weight to account for the noise variance in the DECT images. Its decomposition accuracy heavily relies on the initial value. In this paper, we propose a novel method to overcome these challenges. Based on the piecewise constant property of CT images with energy-dependent LAC, we assume that the pixels with high similarity have the same material composition. We cluster pixel patches into groups using the block-matching technique. The material composition in each group is preselected according to the shortest Euclidean distance in the energy map between the center of mass of the similar patch groups and the LAC of the object with known material composition pre-assigned by the clinician. MMD is performed on the central pixel of each patch using the preselected material composition. In a preliminary study, the proposed method is evaluated using the digital and water phantoms. The proposed method increases the volume fraction by 25.2% and decreases the standard deviation by 66.2% compared with the PMA method and increases the volume fraction by 19.6% compared with the SMMD method. The proposed method achieves an overall improvement of the normalized cross-correlation matrix diagonality by 34.8% and 69.4% compared with the PMA and SMMD methods. The phantom results indicate that the proposed method has the potential to be applied to clinical practice due to its increased decomposition accuracy, and suppressed noise and cross contamination.

Physics-informed Deep Learning for Dual-Energy Computed Tomography Image Processing

Dual-energy CT (DECT) was introduced to address the inability of conventional single-energy computed tomography (SECT) to distinguish materials with similar absorbances but different elemental compositions. However, material decomposition algorithms based purely on the physics of the underlying attenuation process have several limitations, leading to low signal-to-noise ratio (SNR) in the derived material-specific images. To overcome these, we trained a convolutional neural network (CNN) to develop a framework to reconstruct non-contrast SECT images from DECT scans. We show that the traditional physics-based decomposition algorithms do not bring to bear the full information content of the image data. A CNN that leverages the underlying physics of the DECT image generation process as well as the anatomic information gleaned via training with actual images can generate higher fidelity processed DECT images.

Radiomics Analysis of Iodine-Based Material Decomposition Images With Dual-Energy Computed Tomography Imaging for Preoperatively Predicting Microsatellite Instability Status in Colorectal Cancer.

Purpose: The aim of this study was to investigate the value of radiomics analysis of iodine-based material decomposition (MD) images with dual-energy computed tomography (DECT) imaging for preoperatively predicting microsatellite instability (MSI) status in colorectal cancer (CRC).Methods: This study included 102 CRC patients proved by postoperative pathology, and their MSI status was confirmed by immunohistochemistry staining. All patients underwent preoperative DECT imaging scanned on either a Revolution CT or Discovery CT 750HD scanner, and the iodine-based MD images in the venous phase were reconstructed. The clinical, CT-reported, and radiomics features were obtained and analyzed. Data from the Revolution CT scanner were used to establish a radiomics model to predict MSI status (70% samples were randomly selected as the training set, and the remaining samples were used to validate); and data from the Discovery CT 750HD scanner were used to test the radiomics model. The stable radiomics features with both inter-user and intra-user stability were selected for the next analysis. The feature dimension reduction was performed by using Student's t-test or Mann–Whitney U-test, Spearman's rank correlation test, min–max standardization, one-hot encoding, and least absolute shrinkage and selection operator selection method. The multiparameter logistic regression model was established to predict MSI status. The model performances were evaluated: The discrimination performance was accessed by receiver operating characteristic (ROC) curve analysis; the calibration performance was tested by calibration curve accompanied by Hosmer–Lemeshow test; the clinical utility was assessed by decision curve analysis.Results: Nine top-ranked features were finally selected to construct the radiomics model. In the training set, the area under the ROC curve (AUC) was 0.961 (accuracy: 0.875; sensitivity: 1.000; specificity: 0.812); in the validation set, the AUC was 0.918 (accuracy: 0.875; sensitivity: 0.875; specificity: 0.857). In the testing set, the diagnostic performance was slightly lower with AUC of 0.875 (accuracy: 0.788; sensitivity: 0.909; specificity: 0.727). A nomogram based on clinical factors and radiomics score was generated via the proposed logistic regression model. Good calibration and clinical utility were observed using the calibration and decision curve analyses, respectively.Conclusion: Radiomics analysis of iodine-based MD images with DECT imaging holds great potential to predict MSI status in CRC patients.

Detection of different degree traumatic vertebral bone marrow oedema by virtual non-calcium technique of dual-source dual-energy CT

To evaluate the diagnostic performance of dual-energy computed tomography (DECT) using the virtual non-calcium (VNCa) technique for the detection of different degrees of vertebral bone marrow oedema. The present prospective study was approved by the institutional ethics committee. Informed consent was obtained from all patients. Twenty patients underwent both DECT and MRI. Vertebral bodies on VNCa images were graded visually by two blinded, independent radiologists using a three-point classification system (0=normal bone marrow, 2=distinct abnormal bone marrow attenuation). DECT values obtained from VNCa images were calculated by a third radiologist. Vertebral bodies on MRI images were graded by a forth radiologist using the same classification as DECT; magnetic resonance imaging (MRI) served as the reference standard. The agreement between VNCa and MRI images for the grading of bone marrow oedema was substantial (κ=0.694). If MRI at grade 2 was regarded as positive for bone marrow oedema, VNCa images achieved an overall sensitivity of 85%, specificity of 97.5%, positive predictive value of 81%, negative predictive value of 98.1%. Receiver operating characteristic analysis based on CT values for the diagnosis of distinct bone marrow oedema revealed an area under the curve of 0.950. A cut-off value of -11.80 HU provided a sensitivity of 95% and specificity of 86.3% for the diagnosis of distinct bone marrow oedema at DECT. DECT images of VNCa showed excellent diagnostic performance for the detection of distinct vertebral bone marrow oedema.

Dynamic single-slice CT estimates whole-lung dual-energy CT variables in pigs with and without experimental lung injury

BackgroundDynamic single-slice CT (dCT) is increasingly used to examine the intra-tidal, physiological variation in aeration and lung density in experimental lung injury. The ability of dCT to predict whole-lung values is unclear, especially for dual-energy CT (DECT) variables. Additionally, the effect of inspiration-related lung movement on CT variables has not yet been quantified.MethodsEight domestic pigs were studied under general anaesthesia, including four following saline-lavage surfactant depletion (lung injury model). DECT, dCT and whole-lung images were collected at 12 ventilatory settings. Whole-lung single energy scans images were collected during expiratory and inspiratory apnoeas at positive end-expiratory pressures from 0 to 20 cmH2O. Means and distributions of CT variables were calculated for both dCT and whole-lung images. The cranio-caudal displacement of the anatomical slice was measured from whole-lung images.ResultsMean CT density and volume fractions of soft tissue, gas, iodinated blood, atelectasis, poor aeration, normal aeration and overdistension correlated between dCT and the whole lung (r2 0.75–0.94) with agreement between CT density distributions (r 0.89–0.97). Inspiration increased the matching between dCT and whole-lung values and was associated with a movement of 32% (SD 15%) of the imaged slice out of the scanner field-of-view. This effect introduced an artefactual increase in dCT mean CT density during inspiration, opposite to that caused by the underlying physiology.ConclusionsOverall, dCT closely approximates whole-lung aeration and density. This approximation is improved by inspiration where a decrease in CT density and atelectasis can be interpreted as physiological rather than artefactual.

Dual-Energy Computed Tomography in Thoracic Imaging-Current Practices and Utility: Survey of the Society of Thoracic Radiology.

The purpose of this study was to determine the current practice patterns of utilization of dual-energy computed tomography (DECT) in thoracic imaging. In this IRB-approved study, the URL link for an anonymous 26-question survey was sent by email to all the members of the Society of Thoracic Radiology (STR). Survey questions focused on the practice type, case volume, DECT scanner availability, common indications, image types, and perceived utility of DECT. Study data were collected and managed using SurveyMonkey tools and analyzed with χ tests. The survey response rate was 11% (104/962). DECT was available in 75% of respondents' institutions, with 90% of these having 1 to 5 DECT scanners (P<0.001). Seventy percent performed 1 to 500 DECT chest CTs per month (P<0.001). Dual-source was the most common DECT scanner (81%) (P=0.239). DECT was opted depending on the clinical indication in 89% (P=0.433). The technologist reconstructs the additional DECT images in 75% of instances (P<0.001). Acute pulmonary embolism (PE) was the most common indication of DECT (53%) (P=0.006), while chronic PE was considered the most valuable use of DECT (33%) (P<0.001). Iodine map was the most commonly used DECT image (53%) (P<0.001) followed by low-energy virtual monoenergetic image (VMI) (29%). For VMI, 50 keV was the most commonly used energy level for improving vascular contrast (43%) (P=0.048), whereas 120 keV was the most commonly used energy in VMI for decreasing artifacts (25%) (P=0.027). There is wide variability on the utilization of DECT in thoracic imaging. PE is perceived to be the most valuable utility of DECT, and iodine map is considered the most valuable DECT image.

Dual energy CT and research of the bone marrow edema: Comparison with MRI imaging.

Aims:To evaluate the diagnostic accuracy of the Dual Energy Computed Tomography (DECT) in the research of the bone marrow edema.Methods and Material:The MRI images of 100 patients with episodes of articular and or bone pains with or without traumatic lesion were prospectively evaluated during a period between March 2018 and February 2019. In the presence of bone marrow edema, a DECT was performed. The measurement of the density of the bone marrow edema in the DECT was compared with healthy bone in the same patient by two operators.Result:The DECT and MRI images of 15 patients with bone marrow edema were compared. The mean of pathologic bone marrow edema was 1008.20 (Standard Deviation (SD) 23.00), for healthy bone marrow 947.53 (SD 16.42), and t = 11.75, with a statistical significance P < 0.05 (Statistical significance 95%). The agreement between the measurements of the two radiologists has a statistical significance (P < 0.05).Conclusion:The DECT presents an excellent diagnostic accuracy to detect the bone marrow edema, comparable to MRI. The utility of these recent possibilities is maximum where access to MRI is still very difficult.

Myocardial extracellular volume fraction to differentiate healthy from cardiomyopathic myocardium using dual-source dual-energy CT

ObjectiveTo evaluate the feasibility of dual-energy CT (DECT)-based iodine quantification to estimate myocardial extracellular volume (ECV) fraction in patients with and without cardiomyopathy (CM), as well as to assess its ability to distinguish healthy myocardial tissue from cardiomyopathic, with the goal of defining a threshold ECV value for disease detection. MethodsTen subjects free of heart disease and 60 patients with CM (mean age 66.4 ± 9.4; 59 males and 11 females; 40 ischemic and 20 non-ischemic CM) underwent late iodine enhanced DECT imaging. Myocardial iodine maps were obtained using 3-material decomposition. ECV of the left ventricle was estimated from hematocrit levels and the iodine maps using the AHA 16-segment model. Receiver operating characteristic curve analysis was performed, with corresponding area under the curve, along with Youden's index assessment, to establish a threshold for CM detection. ResultsThe median ECV for healthy myocardium, non-ischemic CM, and ischemic CM were 25.4% (22.9–27.3), 38.3% (33.7–43.0), and 36.9% (32.4–41.1), respectively. Healthy myocardium showed significantly lower ECV values compared to ischemic and non-ischemic CM (p < 0.001). From Youden's index analysis, an ECV>29.5% would indicate the presence of CM in the myocardium (sensitivity = 90.3; specificity = 90.3); the AUC for this criterion was 0.950 (p < 0.001). ConclusionThe findings of this study resulted in a statistically significant distinction between healthy myocardium and CM ECVs. This led to the establishment of a promising threshold ECV value that could facilitate the differentiation between healthy and diseased myocardium, and highlights the potential of this DECT methodology to detect cardiomyopathic tissue.

CT coreflood study of foam flow for enhanced oil recovery: The effect of oil type and saturation

We present a CT coreflood study of foam, both pre-generated and generated in-situ, displacing oil, as a function of oil type and saturation. Foam generation and propagation are reflected through sectional pressure measurements. Dual-energy CT imaging monitors in-time phase saturations. With an oil less harmful to foam (hexadecane), injection with and without pre-generation of foam exhibits similarities: propagation of a foam bank through a core and later refinement of foam texture. In contrast, with an oil destabilizing to foam (with 20 wt% oleic acid in the hexadecane), pre-generation of foam behaves very differently from co-injection, suggesting very-different effects on foam generation and propagation. Without pre-generation, strong-foam generation is very difficult even at residual oil saturation (about 0.1); generation finally starts from the outlet (likely a result of the capillary-end effect). This strong-foam state propagates upstream very slowly. Pre-generated foam shows two stages of propagation, both from the inlet to outlet. First, weak foam displaces most of the oil, followed by a propagation of stronger foam at lower oil saturation. This dependence on injection method with harmful oil is not represented in currently applied foam models, which need further improvements for reliable prediction of foam for enhanced oil recovery.

Top-Ten Tips for Dual-Energy CT in MSK Radiology.

Dual-energy computed tomography (DECT) has the potential to detect musculoskeletal pathology with greater sensitivity than conventional CT alone at no additional radiation dose to the patient. It therefore has the potential to reduce the need for further diagnostic imaging or procedures (e.g., joint aspirations in the case of gout or magnetic resonance imaging to confirm undisplaced fractures).DECT is a well-established technique for the detection of gout arthropathy. Multiple newer applications have shown clinical potential including bone marrow edema detection and metal artifact reduction. Collagen analysis, bone marrow lesion detection, and iodine mapping in CT arthrography are areas of possible future application and development.This article outlines 10 tips on the use of DECT imaging of the musculoskeletal system, explaining the technique and indications with practical suggestions to help guide the radiologist.

Detection of bone marrow oedema in knee joints using a dual-energy CT virtual non-calcium technique

To evaluate bone marrow oedema in knee joints quantitatively and qualitatively using a dual-energy computed tomography (CT) virtual non-calcium (VNCa) technique. Thirty-five patients with knee joint injuries underwent both dual-energy CT and magnetic resonance imaging (MRI) between March 2018 and November 2018. The presence of bone marrow oedema was assessed by two independent radiologists with the use of colour-coded dual-energy CT VNCa images and measured attenuation on them. The biggest area of bone marrow oedema on axial images was measured by another radiologist using the dual-energy CT and MRI images, respectively. Attenuation values were subjected to receiver operating characteristic (ROC) curve analysis and oedema area sizes were subjected to paired t-test analysis. In qualitative analysis, colour-coded VNCa images had an overall sensitivity of 88.4%, specificity of 98%, positive predictive value of 92.7%, negative predictive value of 96.8%, and accuracy of 95.9%. Attenuation values obtained from colour-coded VNCa images were significantly different in knee joint regions with and without oedema (p<0.001). ROC curve analysis revealed an area under the curve (AUC) of 0.910. A cut-off value of -67 HU provided a sensitivity of 81.4%, specificity of 99.3%, accuracy of 90.4%, positive predictive value of 99.1%, and negative predictive value of 84.2% for the differentiation of oedematous knee joint regions. Significant differences in the size of oedema area were not found between them. Dual-energy CT VNCa can be used to evaluate bone marrow oedema effectively.

"Dark-blood" dual-energy computed tomography angiography for thoracic aortic wall imaging.

To assess the capability of a newly developed material decomposition method from contrast-enhanced dual-energy CT images, aiming to better visualize the aortic wall and aortic intramural hematoma (IMH), compared with true non-contrast (TNC) CT. Twenty-two patients (11 women; mean age, 61 ± 20years) with acute chest pain underwent 25 dual-layer non-contrast and contrast-enhanced CT. CT-angiography images were retrospectively processed using two-material decomposition analysis, where we defined the first material as the content of a region of interest placed in the ascending aorta for each patient, and the second material as water. Two independent radiologists assessed the images from the second material termed "dark-blood" images and the TNC images regarding contrast-to-noise ratio (CNR) between the wall and the lumen, diagnostic quality regarding the presence of aortic wall thickening, and the inner/outer vessel wall conspicuity. Diagnostic quality scores in normal aortic segments were 0.9 ± 0.3 and 2.7 ± 0.6 (p < 0.001) and wall conspicuity scores were 0.7 ± 0.5 and 1.8 ± 0.3 (p < 0.001) on TNC and dark-blood images, respectively. In aortic segments with IMH, diagnostic quality scores were 1.7 ± 0.5 and 2.4 ± 0.6 (p < 0.001) and wall conspicuity scores were 0.7 ± 0.7 and 1.8 ± 0.3 (p < 0.001) on TNC and dark-blood images, respectively. In normal aortic segments, CNRs were 0.3 ± 0.2 and 2.8 ± 0.9 on TNC and dark-blood images, respectively (p < 0.001). In aortic segments with IMH, CNRs were 0.3 ± 0.2 and 4.0 ± 1.0 on TNC and dark-blood images, respectively (p < 0.001). Compared with true non-contrast CT, dark-blood material decomposition maps enhance quantitative and qualitative image quality for the assessment of normal aortic wall and IMH. • Current dual-energy CT-angiography provides virtual non-contrast and bright-blood images. • Dark-blood images represent a new way to assess the vascular wall structure with dual-energy CT and can improve the lumen-to-wall contrast compared with true non-contrast CT. • This dual-energy CT material decomposition method is likely to improve contrast resolution in other applications as well, taking advantage of the high spatial resolution of CT.

Papillary vs clear cell renal cell carcinoma. Differentiation and grading by iodine concentration using DECT-correlation with microvascular density.

Various imaging methods have been evaluated regarding non-invasive differentiation of renal cell carcinoma (RCC) subtypes. Dual-energy computed tomography (DECT) allows iodine concentration (IC) analysis as a correlate of tissue perfusion. Microvascular density (MVD) in histopathology specimens is evaluated to determine intratumoral vascularization. The objective of this study was to assess the potential of IC and MVD regarding the differentiation between papillary and clear cell RCC and between well- and dedifferentiated tumors. Further, we aimed to investigate a possible correlation between these parameters. DECT imaging series of 53 patients with clear cell RCC (ccRCC) and 15 with papillary RCC (pRCC) were analyzed regarding IC. Histology samples were stained using CD31/CD34 monoclonal antibodies; MVD was evaluated digitally. Statistical analysis included performance of Mann-Whitney U test, ROC analysis, and Spearman rank correlation. Analysis of IC demonstrated significant differences between ccRCC and pRCC (p < 0.001). A cutoff value of ≤ 3.1mg/ml at IC analysis allowed identification of pRCC with an accuracy of 86.8%. Within the ccRCC subgroup, G1/G2 tumors could significantly be differentiated from G3/G4 carcinomas (p = 0.045). A significant positive correlation between IC and MVD could be determined for the entire RCC cohort and the ccRCC subgroup. Limitations include the small percentage of pRCCs. IC analysis is a useful method to differentiate pRCC from ccRCC. The significant positive correlation between IC and MVD indicates valid representation of tumor perfusion by DECT. • Analysis of iodine concentration using DECT imaging could reliably distinguish papillary from clear cell subtypes of renal cell cancer (RCC). • A cutoff value of 3.1mg/ml allowed a distinction between papillary and clear cell RCCs with an accuracy of 86.8%. • The positive correlation with microvascular density in tumor specimens indicates correct display of perfusion by iodine concentration analysis.

EIT monitors valid and robust regional ventilation distribution in pathologic ventilation states in porcine study using differential DualEnergy-CT (\u0394DECT)

It is crucial to precisely monitor ventilation and correctly diagnose ventilation-related pathological states for averting lung collapse and lung failure in Intensive Care Unit (ICU) patients. Although Electrical Impedance Tomography (EIT) may deliver this information continuously and non-invasively at bedside, to date there are no studies that systematically compare EIT and Dual Energy CT (DECT) during inspiration and expiration (ΔDECT) regarding varying physiological and ICU-typical pathological conditions such as atelectasis. This study aims to prove the accuracy of EIT through quantitative identification and monitoring of pathological ventilation conditions on a four-quadrant basis using ΔDECT. In a cohort of 13 pigs, this study investigated systematic changes in tidal volume (TV) and positive end-expiratory pressure (PEEP) under physiological ventilation conditions. Pathological ventilation conditions were established experimentally by single-lung ventilation and pulmonary saline lavage. Spirometric data were compared to voxel-based entire lung ΔDECT, and EIT intensities were compared to ΔDECT of a 12-cm slab of the lung around the EIT belt, the so called ΔDECTBelt. To validate ΔDECT data with spirometry, a Pearson’s correlation coefficient of 0.92 was found for 234 ventilation conditions. Comparing EIT intensity with ΔDECT(Belt), the correlation r = 0.84 was found. Normalized cross-correlation function (NCCF) between scaled global impedance (EIT) waveforms and global volume ventilator curves was r = 0.99 ± 0.003. The EIT technique correctly identified the ventilated lung in all cases of single-lung ventilation. In the four-quadrant based evaluation, which assesses the difference between end-expiratory lung volume (ΔEELV) and the corresponding parameter in EIT, i.e. the end-expiratory lung impedance (ΔEELI), the Pearson’s correlation coefficient of 0.94 was found. The respective Pearson’s correlation coefficients implies good to excellent concurrence between global and regional EIT ventilation data validated by ventilator spirometry and DECT imaging. By providing real-time images of the lung, EIT is a promising, EIT is a promising, clinically robust tool for bedside assessment of regional ventilation distribution and changes of end-expiratory lung volume.

Multi-material decomposition of spectral CT images via Fully Convolutional DenseNets.

Spectral computed tomography (CT) has the capability to resolve the energy levels of incident photons, which has the potential to distinguish different material compositions. Although material decomposition methods based on x-ray attenuation characteristics have good performance in dual-energy CT imaging, there are some limitations in terms of image contrast and noise levels. This study focused on multi-material decomposition of spectral CT images based on a deep learning approach. To classify and quantify different materials, we proposed a multi-material decomposition method via the improved Fully Convolutional DenseNets (FC-DenseNets). A mouse specimen was first scanned by spectral CT system based on a photon-counting detector with different energy ranges. We then constructed a training set from the reconstructed CT images for deep learning to decompose different materials. Experimental results demonstrated that the proposed multi-material decomposition method could more effectively identify bone, lung and soft tissue than the basis material decomposition based on post-reconstruction space in high noise levels. The new proposed approach yielded good performance on spectral CT material decomposition, which could establish guidelines for multi-material decomposition approaches based on the deep learning algorithm.

Sensitization of Vascular Endothelial Cells to Ionizing Radiation Promotes the Development of Delayed Intestinal Injury in Mice.

Exposure of the gastrointestinal (GI) tract to ionizing radiation can cause acute and delayed injury. However, critical cellular targets that regulate the development of radiation-induced GI injury remain incompletely understood. Here, we investigated the role of vascular endothelial cells in controlling acute and delayed GI injury after total-abdominal irradiation (TAI). To address this, we used genetically engineered mice in which endothelial cells are sensitized to radiation due to the deletion of the tumor suppressor p53. Remarkably, we found that VE-cadherin-Cre; p53FL/FL mice, in which both alleles of p53 are deleted in endothelial cells, were not sensitized to the acute GI radiation syndrome, but these mice were highly susceptible to delayed radiation enteropathy. Histological examination indicated that VE-cadherin-Cre; p53FL/FL mice that developed delayed radiation enteropathy had severe vascular injury in the small intestine, which was manifested by hemorrhage, loss of microvessels and tissue hypoxia. In addition, using dual-energy CT imaging, we showed that VE-cadherin-Cre; p53FL/FL mice had a significant increase in vascular permeability of the small intestine in vivo 28 days after TAI. Together, these findings demonstrate that while sensitization of endothelial cells to radiation does not exacerbate the acute GI radiation syndrome, it is sufficient to promote the development of late radiation enteropathy.

Influence of intravenous contrast agent on dose calculation in proton therapy using dual energy CT

The purpose of this study is to evaluate the effect of an intravenous (IV) contrast agent on proton therapy dose calculation using dual-energy computed tomography (DECT). Two DECT methods are considered. The first one, , attempts to accurately predict the proton stopping powers relative to water (SPR) of contrast enhanced (CE) DECT images, while the second generates a virtual non-contrast (VNC) volume that can be processed as a native non-contrast (NC) one. Both methods are compared against single-energy computed tomography (SECT). The accuracy of SPR predicted for different concentrations of IV contrast diluted in water is first evaluated using simulated data. Results then are validated in an experimental set-up comparing SPR predictions for both NC and CE images to measurements made with a multi-layer ionisation chamber (MLIC). Finally, the impact of IV contrast on dose calculation using both SECT and DECT is evaluated for one liver and one head and neck patient. Using simulated data, DECT is shown to be less sensitive to the presence of IV contrast than SECT, although the performance of the method is sensitive to the level of beam hardening considered. For different concentrations of IV contrast diluted in water, experimental MLIC measurement of SPR agrees with DECT predictions within 3% while SECT introduce errors above 20%. This error in the SPR value results in a range error of up to 3.2 mm (2.6%) for proton beams calculated on SECT CE patient images. The error is reduced below 1 mm using DECT with the and VNC methods. Globally, it is observed that the influence of IV contrast on proton therapy dose calculation is mitigated using DECT over SECT. In patient anatomies, the VNC approach provides the best agreement with the reference dose distribution.

Diagnostic Accuracy of Attenuation Difference and Iodine Concentration Thresholds at Rapid-Kilovoltage-Switching Dual-Energy CT for Detection of Enhancement in Renal Masses.

OBJECTIVE. The objective of our study was to evaluate iodine concentration and attenuation change in Hounsfield unit (ΔHU) thresholds to diagnose enhancement in renal masses at rapid-kilovoltage-switching dual-energy CT (DECT). MATERIALS AND METHODS. We evaluated 30 consecutive histologically confirmed solid renal masses (including nine papillary renal cell carcinomas [RCCs]) and 27 benign cysts (17 simple and 10 hemorrhagic or proteinaceous cysts) with DECT December 2016 and May 2018. A blinded radiologist measured iodine concentration (in milligrams per milliliter) and ΔHU (attenuation on enhanced CT - attenuation on unenhanced CT) using 70-keV corticomedullary (CM) phase virtual monochromatic and 120-kVp nephrographic (NG) phase images. The accuracies of previously described enhancement thresholds were compared by ROC curve analysis. RESULTS. An iodine concentration of ≥ 2.0 mg/mL and an iodine concentration of ≥ 1.2 mg/mL achieved sensitivity, specificity, and the area under the ROC curve (AUC) of 73.3%, 100.0%, and 0.87 and 86.7%, 100.0%, and 0.93, respectively. On 70-keV CM phase images, ΔHU ≥ 20 HU and ΔHU ≥ 15 HU yielded sensitivity, specificity, and AUC of 80.0%, 100.0%, and 0.90 and 90.0%, 100.0%, and 0.95, respectively. The numbers of incorrectly classified papillary RCCs were as follows: iodine concentration of ≥ 2.0 mg/mL, 77.8% (7/9; range, 0.7-1.6 mg/mL); iodine concentration of ≥ 1.2 mg/mL, 44.4% (4/9; range, 0.7-0.9 mg/mL); ΔHU ≥ 20 HU on 70-keV CM phase images, 66.7% (6/9; range, 4-17 HU); and ΔHU ≥ 15 HU on 70-keV DECT images, 33.3% (3/9; 4-12 HU). No cyst pseudoenhancement occurred on DECT. For 120-kVp NG phase DECT, ΔHU ≥ 20 HU and ΔHU ≥ 15 HU yielded sensitivity, specificity, and AUC of 93.3%, 96.3%, and 0.95 and 100.0%, 88.9%, and 0.94, respectively. With ΔHU ≥ 20 HU, 22.2% (2/9) (range, 15-18 HU) of papillary RCCs were misclassified and there was one pseudoenhancing cyst. With ΔHU ≥ 15 HU, no papillary RCCs were misclassified but 11.1% (3/27) of cysts showed pseudoenhancement. Only an iodine concentration of ≥ 2.0 mg/mL showed significantly lower accuracy than other measures (p = 0.031-0.045). CONCLUSION. DECT applied in the CM phase performed best using an iodine concentration of ≥ 1.2 mg/mL or a 70-keV ΔHU ≥ 15 HU; these parameters improved sensitivity for the detection of enhancement in renal masses without instances of cyst pseudoenhancement.

The Experimental Study on Geometric Calibration and Material Discrimination for In Vivo Dual-Energy CT Imaging.

Photon-counting detector (PCD) can identify absorption features in the multiple ranges of photon energies, which has a great potential in material discrimination. In this paper, we focused on in vivo dual-energy CT imaging to characterize different biomedical compositions. The precision of material decomposition in post-reconstruction space depends on the quality of reconstructed CT images; we used the locally linear embedding (LLE) based online geometric calibration method and GPU-based reconstruction toolbox to reconstruct high-quality CT images. Then, we performed the real experiment and studied materials decomposition with basis material model to discriminate soft tissue and cortical bone of small animal. Finally, the experimental results demonstrated that the proposed method could reconstruct small animal CT images with more slim structures and details, and improve the precision of materials decomposition in dual-energy CT imaging.