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Abstract To assess the image quality parameters of dual-energy computed tomography angiography (DECTA) 40-, and 60 keV virtual monoenergetic images (VMIs) combined with deep learning-based image reconstruction model (DLM) and iterative reconstructions (IR). CT scans of 28 post EVAR patients were enrolled. The 60 s delayed phase of DECTA was evaluated. Objective [noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR)] and subjective (overall image quality and endoleak conspicuity – 3 blinded readers assessment) image quality analyses were performed. The following reconstructions were evaluated: VMI 40, 60 keV VMI; IR VMI 40, 60 keV; DLM VMI 40, 60 keV. The noise level of the DLM VMI images was approximately 50% lower than that of VMI reconstruction. The highest CNR and SNR values were measured in VMI DLM images. The mean CNR in endoleak in 40 keV was accounted for as 1.83 ± 1.2; 2.07 ± 2.02; 3.6 ± 3.26 in VMI, VMI IR, and VMI DLM, respectively. The DLM algorithm significantly reduced noise and increased lesion conspicuity, resulting in higher objective and subjective image quality compared to other reconstruction techniques. The application of DLM algorithms to low-energy VMIs significantly enhances the diagnostic value of DECTA in evaluating endoleaks. DLM reconstructions surpass traditional VMIs and IR in terms of image quality.

Purpose: The aim of this study was to evaluate the diagnostic accuracy of an artificial intelligence (AI) tool in detecting endoleaks in patients undergoing endovascular aneurysm repair (EVAR) using dual-energy computed tomography angiography (CTA). Material and methods: The study involved 95 patients who underwent EVAR and subsequent CTA follow-up. Dual-energy scans were performed, and images were reconstructed as linearly blended (LB) and 40 keV virtual mono-energetic (VMI) images. The AI tool PRAEVAorta®2 was used to assess arterial phase images for endoleaks. Two experienced readers independently evaluated the same images, and their consensus served as the reference standard. Key metrics, including accuracy, precision, recall, F1 score, and area under the receiver operating characteristic (ROC) curve (AUC), were calculated. Results: The final analysis included 94 patients. The AI tool demonstrated an accuracy of 78.7%, precision of 67.6%, recall of 10 71.9%, F1 score of 69.7%, and an AUC of 0.77 using LB images. However, the tool failed to process 40 keV VMI images correctly, limiting further analysis of these datasets. Conclusions: The AI tool showed moderate diagnostic accuracy in detecting endoleaks using LB images but failed to achieve the reliability needed for clinical use due to the significant number of misdiagnoses. [ABSTRACT FROM AUTHOR]

Gastrointestinal bleeding is a potentially life-threatening abdominal emergency that remains a common cause of hospitalisation. Although 80–85% of cases of gastrointestinal bleeding resolve spontaneously, it can result in massive haemorrhage and death. The presentation of gastrointestinal bleeding can range from asymptomatic or mildly ill patients requiring only conservative treatments to severely ill patients requiring immediate intervention. Identifying the source of the bleeding can be difficult due to the wide range of potential causes, the length of the gastrointestinal tract and the intermittent nature of the bleeding. The diagnostic and therapeutic approach is fully dependent on the nature of the bleeding and the patient’s haemodynamic status. Radiologists should be aware of the appropriate uses of computed tomography angiography and other imaging modalities in patients with acute gastrointestinal bleeding, as well as the semiotics of bleeding and diagnostic pitfalls in order to appropriately diagnose and manage these patients. The learning objective of this review is to illustrate the computed tomography angiography technique, including the potential role of dual-energy computed tomography angiography, also highlighting the tips and tricks to identify the most common and uncommon features of acute gastrointestinal bleeding and its obscure form.

Gastrointestinal bleeding is a potentially life-threatening abdominal emergency that remains a common cause of hospitalisation. Although 80–85% of cases of gastrointestinal bleeding resolve spontaneously, it can result in massive haemorrhage and death. The presentation of gastrointestinal bleeding can range from asymptomatic or mildly ill patients requiring only conservative treatments to severely ill patients requiring immediate intervention. Identifying the source of the bleeding can be difficult due to the wide range of potential causes, the length of the gastrointestinal tract and the intermittent nature of the bleeding. The diagnostic and therapeutic approach is fully dependent on the nature of the bleeding and the patient's haemodynamic status. Radiologists should be aware of the appropriate uses of computed tomography angiography and other imaging modalities in patients with acute gastrointestinal bleeding, as well as the semiotics of bleeding and diagnostic pitfalls in order to appropriately diagnose and manage these patients. The learning objective of this review is to illustrate the computed tomography angiography technique, including the potential role of dual-energy computed tomography angiography, also highlighting the tips and tricks to identify the most common and uncommon features of acute gastrointestinal bleeding and its obscure form. [ABSTRACT FROM AUTHOR]

Background and Objectives: This study aimed to evaluate the utility and accuracy of dual-energy automatic plaque removal (DE-APR) in patients with symptomatic peripheral arterial disease (PAD) using digital subtraction angiography (DSA) as the reference standard. Materials and Methods: We retrospectively analyzed 100 patients with PAD who underwent DE computed tomography angiography (DE-CTA) and DSA of the lower extremities. DE-CTA was used to generate APR subtracted images. In the three main arterial segments (aorto-iliac segment, femoro-popliteal segment, and below-the-knee segment), the presence or absence of hemodynamically significant stenosis (>50%) and calcification was assessed using the images. CTA data were analyzed using different imaging approaches (DE-standard reconstruction image (DE-SR), DE-APR maximum intensity projection image (APR), and DE-SR with APR). Results: For all segments evaluated, the sensitivity, specificity, and accuracy for detecting significant stenosis were 98.16%, 81.01%, and 89.58%, respectively, with DE-SR; 97.79%, 83.33%, and 90.56%, respectively, with APR; and 98.16%, 92.25%, and 95.20%, respectively, with DE-SR with APR. DE-SR with APR had greater accuracy than DE-SR or APR alone (p < 0.001 and p < 0.001, respectively). When analyzed based on vascular wall calcification, the accuracy of DE-SR with APR remained greater than 90% regardless of calcification severity, whereas DE-SR showed a considerable reduction in accuracy in moderate to severe calcification. In the case of APR, the degree of vascular wall calcification did not significantly influence the accuracy in the aorto-iliac and femoro-popliteal segments. DE-SR with APR achieved significantly higher diagnostic accuracy for all lower extremity segments in evaluating hemodynamically significant stenosis in patients with symptomatic PAD and transcended the impact of vascular wall calcification compared with DE-SR. Conclusions: APR demonstrated favorable diagnostic performance in the aorto-iliac and femoro-popliteal segments, exhibiting good agreement with DSA even in cases of moderate to severe vascular wall calcification. [ABSTRACT FROM AUTHOR]

Background and Objectives: This study aimed to evaluate the utility and accuracy of dual-energy automatic plaque removal (DE-APR) in patients with symptomatic peripheral arterial disease (PAD) using digital subtraction angiography (DSA) as the reference standard. Materials and Methods: We retrospectively analyzed 100 patients with PAD who underwent DE computed tomography angiography (DE-CTA) and DSA of the lower extremities. DE-CTA was used to generate APR subtracted images. In the three main arterial segments (aorto-iliac segment, femoro-popliteal segment, and below-the-knee segment), the presence or absence of hemodynamically significant stenosis (>50%) and calcification was assessed using the images. CTA data were analyzed using different imaging approaches (DE-standard reconstruction image (DE-SR), DE-APR maximum intensity projection image (APR), and DE-SR with APR). Results: For all segments evaluated, the sensitivity, specificity, and accuracy for detecting significant stenosis were 98.16%, 81.01%, and 89.58%, respectively, with DE-SR; 97.79%, 83.33%, and 90.56%, respectively, with APR; and 98.16%, 92.25%, and 95.20%, respectively, with DE-SR with APR. DE-SR with APR had greater accuracy than DE-SR or APR alone (p < 0.001 and p < 0.001, respectively). When analyzed based on vascular wall calcification, the accuracy of DE-SR with APR remained greater than 90% regardless of calcification severity, whereas DE-SR showed a considerable reduction in accuracy in moderate to severe calcification. In the case of APR, the degree of vascular wall calcification did not significantly influence the accuracy in the aorto-iliac and femoro-popliteal segments. DE-SR with APR achieved significantly higher diagnostic accuracy for all lower extremity segments in evaluating hemodynamically significant stenosis in patients with symptomatic PAD and transcended the impact of vascular wall calcification compared with DE-SR. Conclusions: APR demonstrated favorable diagnostic performance in the aorto-iliac and femoro-popliteal segments, exhibiting good agreement with DSA even in cases of moderate to severe vascular wall calcification.

This study aimed to develop and validate a machine learning model incorporating both dual-energy computed tomography (DECT) angiography quantitative parameters and clinically relevant risk factors for the identification of symptomatic carotid plaques to prevent acute cerebrovascular events. The data of 180 patients with carotid atherosclerosis plaques were analysed from January 2017 to December 2021; 110 patients (64.03±9.58 years old, 20 women, 90 men) were allocated to the symptomatic group, and 70 patients (64.70±9.89 years old, 50 women, 20 men) were allocated to the asymptomatic group. Overall, five machine learning models using the XGBoost algorithm, based on different CT and clinical features, were developed in the training cohort. The performances of all five models were assessed in the testing cohort using receiver operating characteristic curves, accuracy, recall rate, and F1 score. The shapley additive explanation (SHAP) value ranking showed fat fraction (FF) as the highest among all CT and clinical features and normalised iodine density (NID) as the 10th. The model based on the top 10 features from the SHAP measurement showed optimal performance (area under the curve [AUC].885, accuracy.833, recall rate.933, F1 score.861), compared with the other four models based on conventional CT features (AUC.588, accuracy.593, recall rate.767, F1 score.676), DECT features (AUC.685, accuracy.648, recall rate.667, F1 score.678), conventional CT and DECT features (AUC.819, accuracy.740, recall rate.867, F1 score.788), and all CT and clinical features (AUC.878, accuracy.833, recall rate.867, F1 score.852). FF and NID can serve as useful imaging markers of symptomatic carotid plaques. This tree-based machine learning model incorporating both DECT and clinical features could potentially comprise a non-invasive method for identification of symptomatic carotid plaques to guide clinical treatment strategies. [ABSTRACT FROM AUTHOR]

Gastrointestinal bleeding is a potentially life-threatening abdominal emergency that remains a common cause of hospitalisation. Although 80-85% of cases of gastrointestinal bleeding resolve spontaneously, it can result in massive haemorrhage and death. The presentation of gastrointestinal bleeding can range from asymptomatic or mildly ill patients requiring only conservative treatments to severely ill patients requiring immediate intervention. Identifying the source of the bleeding can be difficult due to the wide range of potential causes, the length of the gastrointestinal tract and the intermittent nature of the bleeding. The diagnostic and therapeutic approach is fully dependent on the nature of the bleeding and the patient's haemodynamic status. Radiologists should be aware of the appropriate uses of computed tomography angiography and other imaging modalities in patients with acute gastrointestinal bleeding, as well as the semiotics of bleeding and diagnostic pitfalls in order to appropriately diagnose and manage these patients. The learning objective of this review is to illustrate the computed tomography angiography technique, including the potential role of dual-energy computed tomography angiography, also highlighting the tips and tricks to identify the most common and uncommon features of acute gastrointestinal bleeding and its obscure form.