AIM: To evaluate the computed tomography (CT) attenuation values, background noise, arterial depiction, and image quality in whole-body dual-energy CT angiography (DECTA) at 40 keV with a reduced iodine dose using deep-learning image reconstruction (...
OBJECTIVE: Many robotic mitral surgeons utilize right thoracotomy with transthoracic clamping of the aorta, while a smaller number employ a port-only endoscopic approach with endoaortic balloon occlusion of the aorta. We present our technique for a p...
Journal of magnetic resonance imaging : JMRI
38216546
BACKGROUND: Quantification of aortic morphology plays an important role in the evaluation and follow-up assessment of patients with aortic diseases, but often requires labor-intensive and operator-dependent measurements. Automatic solutions would hel...
AIM: To evaluate arterial enhancement, its depiction, and image quality in low-tube potential whole-body computed tomography (CT) angiography (CTA) with extremely low iodine dose and compare the results with those obtained by hybrid-iterative reconst...
Cardiovascular engineering and technology
38388764
PURPOSE: Aorta segmentation is extremely useful in clinical practice, allowing the diagnosis of numerous pathologies, such as dissections, aneurysms and occlusive disease. In such cases, image segmentation is prerequisite for applying diagnostic algo...
European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery
38837348
OBJECTIVES: To assess the accuracy of a deep learning-based algorithm for fully automated detection of thoracic aortic calcifications in chest computed tomography (CT) with a focus on the aortic clamping zone.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
39579454
Advancements in medical imaging and endovascular grafting have facilitated minimally invasive treatments for aortic diseases. Accurate 3D segmentation of the aorta and its branches is crucial for interventions, as inaccurate segmentation can lead to ...
BACKGROUND: To evaluate the effectiveness of machine learning (ML) techniques in predicting negative remodeling in uncomplicated Stanford type B intramural hematoma (IMHB) during the acute phase.