Publications by authors named "R Gilkeson"

Background: Evaluation of cardiothoracic pathologies is a common indication for computed tomography (CT) in infants. However, CT is fraught with challenges specific to the patient population, such as increased sensitivity to radiation and inability to remain stationary during imaging.

Objective: This study investigates potential advantages of a high-pitch helical CT protocol for infants with cardiothoracic pathologies.

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Although many advancements have been made in imaging modalities that can be used to diagnose pulmonary embolism (PE), computed tomography pulmonary angiography (CTPA) is still the preferred gold standard for promptly diagnosing pulmonary embolism by looking for filling defects caused by the embolus lodged within the main pulmonary artery or its respective branches. The diagnosis is made by the radiologists in emergency settings where quick detection of a PE on CTPA helps the Pulmonary Embolism Response Team (PERT) in quick management. Thus, utmost care is needed to follow standard image acquisition protocols and optimal contrast administration techniques to achieve a contrast opacification of at least 210 Hounsfield units for the radiologists to easily pinpoint an embolus within the pulmonary arteries.

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Article Synopsis
  • Researchers aimed to find a screening method using computed tomography calcium scoring (CTCS) to assess the risk of heart failure (HF) in patients, particularly focusing on those with type 2 diabetes.
  • They analyzed CTCS scans from nearly 2,000 patients and applied deep learning to create models that predict HF risk based on radiomic features of epicardial adipose tissue (EAT) and calcifications.
  • The study found that CTCS-based models, especially those using fat-omics for non-diabetic patients and calcium-omics for diabetic patients, significantly outperformed traditional clinical prediction methods in forecasting incident HF.
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Severe COVID-19 can lead to extensive lung disease causing lung architectural distortion. In this study we employed machine learning and statistical atlas-based approaches to explore possible changes in lung shape among COVID-19 patients and evaluated whether the extent of these changes was associated with COVID-19 severity. On a large multi-institutional dataset (N = 3443), three different populations were defined; a) healthy (no COVID-19), b) mild COVID-19 (no ventilator required), c) severe COVID-19 (ventilator required), and the presence of lung shape differences between them were explored using baseline chest CT.

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Whole-heart coronary calcium Agatston score is a well-established predictor of major adverse cardiovascular events (MACE), but it does not account for individual calcification features related to the pathophysiology of the disease (e.g., multiple-vessel disease, spread of the disease along the vessel, stable calcifications, numbers of lesions, and density).

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