Dual-energy Computed Tomography (DECT) predicts the efficacy of contrast medium extravasation and secondary cerebral hemorrhage after stent thrombectomy in acute ischemic cerebral infarction.

To prospective research the efficacy of dual-energy computed tomography (DECT) in predicting contrast medium extravasation and secondary cerebral hemorrhage after stent thrombectomy in acute ischemic cerebral infarction. Ninety-two patients with acute ischemic stroke who underwent intra-arterial thrombolysis in our hospital from December 2019 to January 2022 have opted as the study subjects. DECT was performed immediately after stent thrombectomy. Images were generated through the image workstation and routine diagnosis was performed 24 hours after the operation. To analyze the diagnostic value of To analyze the diagnostic value of DECT, and to explore the diagnostic status of lesions with hemorrhagic transformation or increased hemorrhage and their correlation with iodine concentration. (1) 68 situations were confirmed, 56 positive and 12 negative with detection rates of 10.71% for hemorrhage, 75.00% for contrast agent extravasation, and 14.29% for extravasation combined with hemorrhage; (2) DECT diagnosed 8 cases of postoperative bleeding and 44 cases of extravasation of contrast media and 4 cases of extravasation of contrast media with hemorrhage ; The accuracy of DECT in diagnosing postoperative hemorrhage was 96.43%. The accuracy of diagnosis of extravasation was 96.43%. (3) The mean iodine concentration of lesions with increased hemorrhage or hemorrhagic transformation was higher compared to those without; (4) There was a correlation between hemorrhagic transformation or increased hemorrhage and iodine concentration. Dual-energy CT (DECT) can accurately distinguish the extravasation of contrast agent and secondary cerebral hemorrhage, and can predict the increased bleeding and bleeding transformation, with good diagnostic value and good predictive efficacy.

Concordance of Ultrasound and Dual-Energy CT in Diagnosing Gouty Arthritis in the Knee Joint: A Retrospective Observational Study.

RATIONALE AND OBJECTIVES: To assess the consistency between ultrasound and dual-energy computed tomography (DECT) for the diagnosis of gout in the knee joint. MATERIALS AND METHODS: The ultrasound and DECT images of 176 knee joints from 167 patients diagnosed with gout at the Gout Specialty Clinic of Qingdao University Affiliated Hospital from February 2022 to December 2023 were retrospectively analyzed. The knee joint was segmented into five anatomical regions: intra-articular, anterior, posterior, medial, and lateral. The location of monosodium urate (MSU) crystal deposition was recorded. Tophi were classified as hypoechogenic, isoechogenic, hyperechogenic, or strongly echogenic. The Kappa test was used to assess the consistency between the two examination methods in different regions of the knee joint. The McNemar chi-square test was utilized to conduct a differential analysis between the DECT and ultrasound results. The chi-square test was used to assess differences in the rate of tophi detection with different echogenicities by DECT. Pearson's correlation coefficient was used to assess the correlation between MSU crystal deposition volume and clinically relevant indicators. RESULTS: Double contour (61.4%) was the most common intra-articular ultrasound sign. In the extra-articular region, MSU crystals were commonly deposited in and around the popliteal groove region (ultrasound: 52.3%; DECT: 60.0%). Corresponding MSU deposits on DECT were found in 7 of 54 joints with aggregates detected on ultrasound, and in 15 of 108 joints with DC. Tophi with hyperechogenicity or strong echogenicity were more likely to be detected on DECT than those with hypoechoic or isoechoic features (84.3% and 90.9% vs. 55.1% and 27.8%, respectively). For the assessment of MSU deposits, ultrasound showed an overall higher positive rate than DECT (81.1% vs. 72.2%), with poor consistency between the two examinations (κ = 0.177). In distinct anatomical regions, ultrasound and DECT showed high consistency in the medial (κ = 0.651) and lateral (κ = 0.705) views, with no significant difference. The intra-articular (κ = 0.316) and anterior (κ = 0.346) regions exhibited only fair consistency, with statistically significant diagnostic differences. When exclusively assessing cases with tophi, ultrasound and DECT demonstrated similar consistency in the medial, lateral and anterior views (κ = 0.633, 0.712, and 0.400, respectively), with statistically significant differences. In the intra-articular region, the consistency was reduced (κ = 0.237), and the differences were statistically significant. CONCLUSION: Ultrasound and DECT are effective methods to detect MSU deposition in gout of the knee. However, the consistency between the two techniques varies in different anatomical locations. Clinical assessment should be tailored based on the specific anatomical position. DECT is advantageous for the evaluation of intra-articular MSU deposits, while ultrasound is more sensitive for the early detection of scattered MSU deposits.

Validation of dual-energy CT-based composition analysis using fresh animal tissues and composition-optimized tissue equivalent samples.

Objective.Proton therapy allows for highly conformal dose deposition, but is sensitive to range uncertainties. Several approaches currently under development measure composition-dependent secondary radiation to monitor the delivered proton rangein-vivo. To fully utilize these methods, an estimate of the elemental composition of the patient's tissue is often needed.Approach.A published dual-energy computed tomography (DECT)-based composition-extraction algorithm was validated against reference compositions obtained with two independent methods. For this purpose, a set of phantoms containing either fresh porcine tissue or tissue-mimicking samples with known, realistic compositions were imaged with a CT scanner at two different energies. Then, the prompt gamma-ray (PG) signal during proton irradiation was measured with a PG detector prototype. The PG workflow used pre-calculated Monte Carlo simulations to obtain an optimized estimate of the sample's carbon and oxygen contents. The compositions were also assessed with chemical combustion analysis (CCA), and the stopping-power ratio (SPR) was measured with a multi-layer ionization chamber. The DECT images were used to calculate SPR-, density- and elemental composition maps, and to assign voxel-wise compositions from a selection of human tissues. For a more comprehensive set of reference compositions, the original selection was extended by 135 additional tissues, corresponding to spongiosa, high-density bones and low-density tissues.Results.The root-mean-square error for the soft tissue carbon and oxygen content was 8.5 wt% and 9.5 wt% relative to the CCA result and 2.1 wt% and 10.3 wt% relative to the PG result. The phosphorous and calcium content were predicted within 0.4 wt% and 1.1 wt% of the CCA results, respectively. The largest discrepancies were encountered in samples whose composition deviated the most from tabulated compositions or that were more inhomogeneous.Significance.Overall, DECT-based composition estimations of relevant elements were in equal or better agreement with the ground truth than the established SECT-approach and could contribute toin-vivodose verification measurements.

CT in musculoskeletal imaging: still helpful and for what?

Computed tomography (CT) is a common modality employed for musculoskeletal imaging. Conventional CT techniques are useful for the assessment of trauma in detection, characterization and surgical planning of complex fractures. CT arthrography can depict internal derangement lesions and impact medical decision making of orthopedic providers. In oncology, CT can have a role in the characterization of bone tumors and may elucidate soft tissue mineralization patterns. Several advances in CT technology have led to a variety of acquisition techniques with distinct clinical applications. These include four-dimensional CT, which allows examination of joints during motion; cone-beam CT, which allows examination during physiological weight-bearing conditions; dual-energy CT, which allows material decomposition useful in musculoskeletal deposition disorders (e.g., gout) and bone marrow edema detection; and photon-counting CT, which provides increased spatial resolution, decreased radiation, and material decomposition compared to standard multi-detector CT systems due to its ability to directly translate X-ray photon energies into electrical signals. Advanced acquisition techniques provide higher spatial resolution scans capable of enhanced bony microarchitecture and bone mineral density assessment. Together, these CT acquisition techniques will continue to play a substantial role in the practices of orthopedics, rheumatology, metabolic bone, oncology, and interventional radiology.

Real-time diagnosis of intracerebral hemorrhage by generating dual-energy CT from single-energy CT.

Real-time diagnosis of intracerebral hemorrhage after thrombectomy is crucial for follow-up treatment. However, this is difficult to achieve with standard single-energy CT (SECT) due to similar CT values of blood and contrast agents under a single energy spectrum. In contrast, dual-energy CT (DECT) scanners employ two different energy spectra, which allows for real-time differentiation between hemorrhage and contrast extravasation based on energy-related attenuation characteristics. Unfortunately, DECT scanners are not as widely used as SECT scanners due to their high costs. To address this dilemma, in this paper, we generate pseudo DECT images from a SECT image for real-time diagnosis of hemorrhage. More specifically, we propose a SECT-to-DECT Transformer-based Generative Adversarial Network (SDTGAN), which is a 3D transformer-based multi-task learning framework equipped with a shared attention mechanism. In this way, SDTGAN can be guided to focus more on high-density areas (crucial for hemorrhage diagnosis) during the generation. Meanwhile, the introduced multi-task learning strategy and the shared attention mechanism also enable SDTGAN to model dependencies between interconnected generation tasks, improving generation performance while significantly reducing model parameters and computational complexity. In the experiments, we approximate real SECT images using mixed 120kV images from DECT data to address the issue of not being able to obtain the true paired DECT and SECT data. Extensive experiments demonstrate that SDTGAN can generate DECT images better than state-of-the-art methods. The code of our implementation is available at https://github.com/jiang-cw/SDTGAN.

Systematic Review on Learning-based Spectral CT.

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

Dual-energy CT applications in musculoskeletal disorders.

Dual-energy CT (DECT) is an exciting application in CT technology conferring many advantages over conventional single-energy CT at no additional with comparable radiation dose to the patient. Various emerging and increasingly established clinical DECT applications in musculoskeletal (MSK) imaging such as bone marrow oedema detection, metal artefact reduction, monosodium urate analysis, and collagen analysis for ligamentous, meniscal, and disc injuries are made possible through its advanced DECT post-processing capabilities. These provide superior information on tissue composition, artefact reduction and image optimization. Newer DECT applications to evaluate fat fraction for sarcopenia, Rho/Z application for soft tissue calcification differentiation, 3D rendering, and AI integration are being assessed for future use. In this article, we will discuss the established and developing applications of DECT in the setting of MSK radiology as well as the basic principles of DECT which facilitate them.

Systematic Review on Learning-based Spectral CT.

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

Virtual unenhanced images derived from dual-energy computed tomography for assessing bone mineral density and detecting osteoporosis.

Background: The early detection and treatment of osteoporosis can help prevent osteoporosis-related fractures, especially in patients who undergo enhanced computed tomography (CT) scans for disease diagnosis or evaluation of treatment outcomes. Although Hounsfield unit (HU) measurement of the vertebral body has been shown to have a strong positive correlation with bone mineral density (BMD), the contrast media will impact the CT value of the vertebral body and decrease the accuracy. This study is aimed to examine the distinctions in vertebral body CT attenuation measurement on true unenhanced (TUE) and virtual unenhanced (VUE) images generated from triphasic enhanced dual-energy CT (DECT) scans and to determine the feasibility of assessing BMD and detecting osteoporosis on VUE images as compared to quantitative CT (QCT). Methods: A total of 235 patients underwent abdominal CT examinations that included unenhanced (with 120 kVp and Smart mA) and triphasic enhanced DECT scans. The BMD and CT attenuation values of the L1-L2 vertebrae were measured on TUE and VUE images reconstructed from the triphasic enhanced CT. The differences and associations between TUE and VUE generated from triphasic enhanced CT were analyzed. The diagnostic performances of HU measurements obtained from TUE and VUE images were evaluated using receiver operating characteristic curve. Results: The BMD and HU measurements of the vertebrae showed good interobserver repeatability on both TUE and VUE images (all intercorrelation coefficients >0.92). The CT attenuation values of L1 and L2 and their average value showed no statistically significant difference among the triphasic VUE images (F=0.121, F=0.061, F=0.090; all P values >0.05) but were significantly lower than those obtained from the TUE images. HU measurements in both the TUE and triphasic VUE images, along with the reference BMD derived from QCT, demonstrated a strong positive correlation (rTUE =0.981, rVUEa =0.966, rVUEv =0.962, rVUEd =0.964; all P values <0.05), with excellent diagnostic performance for the diagnoses of osteoporosis and osteopenia (all areas under curve >0.95). The Bland-Altman scatter plot exhibited good agreement, as the deviations between the reference BMD and the calculated BMD were evenly distributed around 0. Conclusions: Although the attenuation values of the vertebrae on the VUE images were underestimated compared to those on the TUE images, the HU measurement on VUE image was effective in assessing BMD and detecting osteoporosis and osteopenia with good diagnostic performance.

Pediatric Applications of Dual-Energy Computed Tomography.

There is renewed interest in novel pediatric dual-energy computed tomography (DECT) applications that can image awake patients faster and at low radiation doses. DECT enables the simultaneous acquisition of 2 data sets at different energy levels, allowing for better material characterization and unique image reconstructions that enhance image analysis and provide quantitative and qualitative information about tissue composition. Pediatric DECT reduces radiation doses further while accelerating image acquisition and improving motion robustness. Current applications include the improved evaluation of congenital and acquired cardiovascular anomalies, lung perfusion and ventilation, renal stone composition, tumor extension and treatment response, and gastrointestinal diseases.

Evaluation of diagnostic accuracy of dual-energy computed tomography in patients with chronic thromboembolic pulmonary hypertension compared to V/Q-SPECT and pulmonary angiogram.

Purpose: The relevance of dual-energy computed tomography (DECT) for the detection of chronic thromboembolic pulmonary hypertension (CTEPH) still lies behind V/Q-SPECT in current clinical guidelines. Therefore, our study aimed to assess the diagnostic accuracy of DECT compared to V/Q-SPECT with invasive pulmonary angiogram (PA) serving as the reference standard. Methods: A total of 28 patients (mean age 62.1 years ± 10.6SD; 18 women) with clinically suspected CTEPH were retrospectively included. All patients received DECT with the calculation of iodine maps, V/Q-SPECT, and PA. Results of DECT and V/Q-SPECT were compared, and the percent of agreement, concordance (utilizing Cohen's kappa), and accuracy (kappa2) to PA were calculated. Furthermore, radiation doses were analyzed and compared. Results: In total, 18 patients were diagnosed with CTEPH (mean age 62.4 years ± 11.0SD; 10 women) and 10 patients had other diseases. Compared to PA, accuracy and concordance for DECT were superior to V/Q-SPECT in all patients (88.9% vs. 81.3%; k = 0.764 vs. k = 0.607) and in CTEPH patients (82.4% vs. 70.1%; k = 0.694 vs. k = 0.560). Furthermore, the mean radiation dose was significantly lower for DECT vs. V/Q-SPECT (p = 0.0081). Conclusion: In our patient cohort, DECT is at least equivalent to V/Q-SPECT in diagnosing CTEPH and has the added advantage of significantly lower radiation doses in combination with simultaneous assessment of lung and heart morphology. Hence, DECT should be the subject of ongoing research, and if our results are further confirmed, it should be implemented in future diagnostic PH algorithms at least on par with V/Q-SPECT.

Feasibility and accuracy of coronary artery calcium score on virtual non-contrast images derived from a dual-layer spectral detector CT: A retrospective multicenter study.

Rationale and objective: This retrospective study was to evaluate the feasibility and accuracy of coronary artery calcium score (CACS) from virtual non-contrast (VNC) images in comparison with that from true non-contrast (TNC) images. Materials and methods: A total of 540 patients with suspected of coronary artery disease (CAD) who underwent a dual-layer spectral detector CT (SDCT) in three hospitals were eligible for this study and 233 patients were retrospectively enrolled for further analysis. The CACS was calculated from both TNC and VNC images and compared. Linear regression analysis of the CACS was performed between TNC and VNC images. Results: The correlation of overall CACS from VNC and TNC images was very strong (r = 0.923, p < 0.001). The CACS from VNC images were lower than that from TNC images (221 versus. 69, p < 0.001). When the regression equation of the overall coronary artery was applied, the mean calibrated CACS-VNC was 221 which had a significant difference from the CACS-TNC (p = 0.017). When the regression equation of each coronary branch artery was applied, the mean calibrated CACS-VNC was 221, which had a significant difference from the CACS-TNC (p = 0.003). But the mean difference between the CACS-TNC and the calibrated CACS-VNC in either way was less than 1. The agreement on risk stratification with CACS-TNC and CCACS-VNC was almost perfect. Conclusion: This multicenter study with dual-layer spectral detector CT showed that it was feasible to calculate CACS from the VNC images derived from the spectral coronary artery CT angiography scan, and the results were in good accordance with the TNC images after correction. Therefore, the TNC scan could be omitted, reducing the radiation dose to patients and saving examination time while using dual-layer spectral detector CT.

Flat-panel dual-energy head computed tomography in the angiography suite after thrombectomy for acute stroke: A clinical feasibility study.

BACKGROUND: Management of large vessel occlusion (LVO) patients after thrombectomy is affected by the presence of intracranial hemorrhage (ICH) on post-procedure imaging. Differentiating contrast staining from hemorrhage on post-procedural imaging has been facilitated by dual-energy computed tomography (DECT), traditionally performed in dedicated computed tomography (CT) scanners with subsequent delays in treatment. We employed a novel method of DECT using the Siemens cone beam CT (DE-CBCT) in the angiography suite to evaluate for post-procedure ICH and contrast extravasation. METHODS: After endovascular treatment for LVO was performed and before the patient was removed from the operating table, DE-CBCT was performed using the Siemens Q-biplane system, with two separate 20-second CBCT scans at two energy levels: 70 keV (standard) and 125 keV with tin filtration (nonstandard). Post-procedurally, patients also underwent a standard DECT using Siemens SOMATOM Force CT scanner. Two independent reviewers blindly evaluated the DE-CBCT and DECT for hemorrhage and contrast extravasation. RESULTS: We successfully performed intra-procedural DE-CBCT in 10 subjects with no technical failure. The images were high-quality and subjectively useful to differentiate contrast from hemorrhage. The one hemorrhage seen on standard DECT was very small and clinically silent. The interrater reliability was 100% for both contrast and hemorrhage detection. CONCLUSION: We demonstrate that intra-procedural DE-CBCT after thrombectomy is feasible and provides clinically meaningful images. There was close agreement between findings on DE-CBCT and standard DECT. Our findings suggest that DE-CBCT could be used in the future to improve stroke thrombectomy patient workflow and to more efficiently guide the postoperative management of these patients.

What's new in the hot gallbladder: the evolving radiologic diagnosis and management of acute cholecystitis.

Acute cholecystitis (AC) is a common condition and its incidence is rising. New technologies have advanced the imaging diagnosis of AC, providing more structural and functional information as well as allowing the radiologist to distinguish AC from mimics and identify complications from both the disease and its management. Dual energy CT aids in detecting gallstones and gallbladder wall enhancement, which helps to diagnose AC and identify its complications. Similarly, contrast-enhanced and non-contrast perfusion ultrasound techniques improve detection of abnormal gallbladder wall enhancement. Advances in MR imaging including hepatobiliary contrast agents aid in characterizing post-cholecystectomy complications such as bile leaks. Newer interventional techniques have also expanded the suite of options for minimally invasive management. Lumen apposing metal stents provide more options for conservative treatment in non-surgical candidates and are compared to a standard percutaneous cholecystostomy. Radiologists should be familiar with these advanced imaging methods and intervention techniques and the value they can bring to the diagnosis and management of AC.

Effect of abdominal adipose content on spine phantom bone mineral density measured by rapid kilovoltage-switching dual-energy CT and quantitative CT.

Background: The purpose of this study was to investigate the effect of abdominal adiposity, as measured by abdominal total adipose tissue (TAT), on the accuracy of rapid kilovoltage-switching dual-energy computed tomography (DECT) and quantitative computed tomography (QCT) measurements of bone mineral density (BMD) in a spine phantom model. Methods: Fresh porcine fat was wrapped around the European Spine Phantom (ESP) and divided into four groups according to the TAT cross-sectional areas, S=0, 100, 200, and 350 cm2, to simulate different TAT contents. The hydroxyapatite (HAP) (water) values of each vertebra were measured by DECT, and the BMD values by QCT. A one-sample t-test was used to analyze the differences between the measurements and the true values of the ESP. One-way analysis of variance (ANOVA) was applied to compare the differences between measurements under different TAT conditions, and the root-mean-square errors (RMSEs) of the BMD measurements were calculated and compared. Moreover, Pearson's correlation analysis was performed for the RMSE and TAT. Linear regression analysis was conducted on the measurements, the true values, and the TAT to obtain the correction equations for the BMD and to compare the RMSE before and after correction. Results: At higher TAT content, the measurements of both scanning methods were more affected, and the measurements of the TAT =350 cm2 group were significantly different from the remaining groups (P<0.05). There was a positive correlation between the RMSE and TAT (r>0, P<0.05), with the RMSE of the L1 vertebrae the largest under the same TAT content. The corrected equations for BMD were derived, and the RMSE of BMD was significantly reduced after correction. Conclusions: The measurements of ESP BMD for both rapid kilovoltage-switching DECT and QCT changed with TAT content. Along with the increase of TAT, the RMSE of measurements increased and the accuracy decreased; moreover, the lower the value of BMD, the more significant the RMSE. The linear regression analysis allowed the corrected BMD measurements to be very close to true values.

Detection of left atrial appendage thrombus by dual-energy computed tomography-derived imaging biomarkers in patients with atrial fibrillation.

Aims: This study aimed to assess the diagnostic performances of dual-energy computed tomography (CT)-derived iodine concentration and effective atomic number (Z eff ) in early-phase cardiac CT in detecting left atrial appendage (LAA) thrombus and differentiating thrombus from spontaneous echo contrast (SEC) in patients with atrial fibrillation using transesophageal echocardiography (TEE) as the reference standard. Methods and results: A total of 389 patients with atrial fibrillation were prospectively recruited. All patients underwent a single-phase cardiac dual-energy CT scan using a third-generation dual-source CT. The iodine concentration, Z eff , and conventional Hounsfield units (HU) in the LAA were measured and normalized to the ascending aorta (AA) of the same slice to calculate the LAA/AA ratio. Of the 389 patients, TEE showed thrombus in 15 (3.9%), SEC in 33 (8.5%), and no abnormality in 341 (87.7%) patients. Using TEE findings as the reference standard, the respective sensitivity, specificity, positive predictive value, and negative predictive value of the LAA/AA HU ratio for detecting LAA thrombus were 100.0, 96.8, 55.6, and 100.0%; those of the LAA/AA iodine concentration ratio were 100.0, 99.2, 83.3, and 100.0%; and those of the LAA/AA Z eff ratio were 100.0, 98.9, 79.0, and 100.0%. The areas under the receiver operator characteristic curve (AUC) of the LAA/AA iodine concentration ratio (0.978; 95% CI 0.945-1.000) and Z eff ratio (0.962; 95% CI 0.913-1.000) were significantly larger than that of the LAA/AA HU ratio (0.828; 95% CI 0.714-0.942) in differentiating the thrombus from the SEC (both P < 0.05). Although the AUC of the LAA/AA iodine concentration ratio was larger than that of the LAA/AA Z eff ratio, no significant difference was found between them (P = 0.259). Conclusion: The dual-energy CT-derived iodine concentration and the Z eff showed better diagnostic performance than the conventional HU in early-phase cardiac CT in detecting LAA thrombus and differentiating the thrombus from the circulatory stasis. However, these results need to be validated in large-cohort studies with late-phase images.

Persistent respiratory failure after SARS-CoV-2 infection: The role of dual energy computed tomography. A case report.

Background: COVID-19 disease is often complicated by respiratory failure, developing through multiple pathophysiological mechanisms, with pulmonary embolism (PE) and microvascular thrombosis as key and frequent components. Newer imaging modalities such as dual-energy computed tomography (DECT) can represent a turning point in the diagnosis and follow-up of suspected PE during COVID-19. Case presentation: A 78-year-old female presented to our internal medicine 3 weeks after initial hospitalization for COVID-19 disease, for recrudescent respiratory failure needing oxygen therapy. A computed tomography (CT) lungs scan showed a typical SARSCoV-2 pneumonia. Over the following 15 days, respiratory function gradually improved. Unexpectedly, after 21 days from symptom onset, the patient started complaining of breath shortening with remarkable desaturation requiring high-flow oxygen ventilation. CT pulmonary angiography and transthoracic echocardiography were negative for signs of PE. Thereby, Dual-energy CT angiography of the lungs (DECT) was performed and detected diffuse peripheral microembolism. After 2 weeks, a second DECT was performed, showing a good response to the anticoagulation regimen, with reduced extent of microembolism and some of the remaining emboli partially recanalized. Discussion: DECT is an emerging diagnostic technique providing both functional and anatomical information. DECT has been reported to produce a much sharper delineation of perfusion defects than pulmonary scintigraphy, using a significantly lower equivalent dose of mSv. We highlight that DECT is particularly useful in SARS-Cov-2 infection, in order to determine the predominant underlying pathophysiology, particularly when respiratory failure prolongs despite improved lung parenchymal radiological findings.

Multiphase Dual-Energy Spectral CT-Based Deep Learning Method for the Noninvasive Prediction of Head and Neck Lymph Nodes Metastasis in Patients With Papillary Thyroid Cancer.

Purpose: To develop deep learning (DL) models based on multiphase dual-energy spectral CT for predicting lymph nodes metastasis preoperatively and noninvasively in papillary thyroid cancer patients. Methods: A total of 293 lymph nodes from 78 papillary thyroid cancer patients who underwent dual-energy spectral CT before lymphadenectomy were enrolled in this retrospective study. The lymph nodes were randomly divided into a development set and an independent testing set following a 4:1 ratio. Four single-modality DL models based on CT-A model, CT-V model, Iodine-A model and Iodine-V model and a multichannel DL model incorporating all modalities (Combined model) were proposed for the prediction of lymph nodes metastasis. A CT-feature model was also built on the selected CT image features. The model performance was evaluated with respect to discrimination, calibration and clinical usefulness. In addition, the diagnostic performance of the Combined model was also compared with four radiologists in the independent test set. Results: The AUCs of the CT-A, CT-V, Iodine-A, Iodine-V and CT-feature models were 0.865, 0.849, 0.791, 0.785 and 0.746 in the development set and 0.830, 0.822, 0.744, 0.739 and 0.732 in the testing set. The Combined model had outperformed the other models and achieved the best performance with AUCs yielding 0.890 in the development set and 0.865 in the independent testing set. The Combined model showed good calibration, and the decision curve analysis demonstrated that the net benefit of the Combined model was higher than that of the other models across the majority of threshold probabilities. The Combined model also showed noninferior diagnostic capability compared with the senior radiologists and significantly outperformed the junior radiologists, and the interobserver agreement of junior radiologists was also improved after artificial intelligence assistance. Conclusion: The Combined model integrating both CT images and iodine maps of the arterial and venous phases showed good performance in predicting lymph nodes metastasis in papillary thyroid cancer patients, which could facilitate clinical decision-making.

Acute Pulmonary Embolism and Chronic Thromboembolic Pulmonary Hypertension: Clinical and Serial CT Pulmonary Angiographic Features.

In acute pulmonary embolism (PE), circulatory failure and systemic hypotension are important clinically for predicting poor prognosis. While pulmonary artery (PA) clot loads can be an indicator of the severity of current episode of PE or treatment effectiveness, they may not be used directly as an indicator of right ventricular (RV) failure or patient death. In other words, pulmonary vascular resistance or patient prognosis may not be determined only with mechanical obstruction of PAs and their branches by intravascular clot loads on computed tomography pulmonary angiography (CTPA), but determined also with vasoactive amines, reflex PA vasoconstriction, and systemic arterial hypoxemia occurring during acute PE. Large RV diameter with RV/left ventricle (LV) ratio > 1.0 and/or the presence of occlusive clot and pulmonary infarction on initial CTPA, and clinically determined high baseline PA pressure and RV dysfunction are independent predictors of oncoming chronic thromboembolic pulmonary hypertension (CTEPH). In this pictorial review, authors aimed to demonstrate clinical and serial CTPA features in patients with acute massive and submassive PE and to disclose acute CTPA and clinical features that are related to the prediction of oncoming CTEPH.

Machine learning based oxygen and carbon concentration derivation using dual-energy CT for PET-based dose verification in proton therapy.

PURPOSE: Online dose verification based on proton-induced positron emitters requires high accuracy in the assignment of elemental composition (e.g., C and O). We developed a machine learning framework for deriving oxygen and carbon concentration based on dual-energy CT (DECT). METHODS: Digital phantoms at the head site were constructed based on single-energy CT (SECT) and stoichiometric calibration. DECT images (80 and 140 kVp) were synthesized using two methods: (1) theoretical CT numbers with Gaussian noise (method 1) and (2) forward/backward image reconstruction with poly-energetic energy spectrum and Poisson noise modeled (method 2). Two architectures of convolutional neural networks, UNet and ResNet, were investigated to map from DECT images to C/O weights. Four cases (UNet-1: Method 1+UNet, ResNet-1: Method 1+ResNet, UNet-2: Method 2+UNet, and ResNet-2: Method 2 +ResNet) were tested for different tissue types and different noise levels. Monte-Carlo simulation was employed to identify the impact of fluctuation in oxygen and carbon concentration on activity/dose distribution. RESULTS: When no noise is present, all four cases are able to obtain <2% mean absolute errors and <4% root mean square error (RMSE). For the worst image quality (e.g., lowest image SNR), the RMSE for O among all tissue types is 3.02% (UNet-1), 4.46% (ResNet-1), 4.38% (UNet-2), and 6.31% (ResNet-2), respectively. For UNet-1 and ResNet-1, the model performed slightly better in terms of RMSE for skeletal tissue than soft tissues. Such a trend is not observed for UNet-2 and ResNet-2. With regard to the comparison between UNet and ResNet, different accuracy and noise immunity are observed. The activity profiles exhibit 3%-5% difference in terms of mean relative error between the ground truth and machine learning outcome. CONCLUSION: We explored the feasibility of a machine learning framework to derive elemental concentration of oxygen and carbon based on DECT images. Two machine learning models, UNet and ResNet, are able to utilize spatial correlation and obtain accurate carbon and oxygen concentration. This study lays a foundation for us to apply the proposed approach to clinical DECT images.