RATIONALE AND OBJECTIVES: Nasal polyps (NP) and inverted papilloma (IP) are benign tumors within the nasal cavity, each necessitating distinct treatment approaches. Herein, we investigate the utility of a deep learning (DL) model for distinguishing b...
PURPOSE: To evaluate the diagnostic ability and methodological quality of ML models in detecting Pancreatic Ductal Adenocarcinoma (PDAC) in Contrast CT images.
BACKGROUND AND AIMS: The significance of left ventricular mass and chamber volumes from non-contrast computed tomography (CT) for predicting major adverse cardiovascular events (MACE) has not been studied. Our objective was to evaluate the role of ar...
OBJECTIVE: To assess whether CT style conversion between different CT vendors using a routable generative adversarial network (RouteGAN) could minimize variation in ILD quantification, resulting in improved functional correlation of quantitative CT (...
OBJECTIVES: To assess the impact of artificial intelligence iterative reconstruction algorithms (AIIR) on image quality with phantom and clinical studies.
OBJECTIVES: To develop and validate deep learning (DL)-models that denoise late iodine enhancement (LIE) images and enable accurate extracellular volume (ECV) quantification.
INTRODUCTION: A chest X-ray (CXR) is the most common imaging investigation performed worldwide. Advances in machine learning and computer vision technologies have led to the development of several artificial intelligence (AI) tools to detect abnormal...
OBJECTIVE: To construct a predictive model using deep-learning radiomics and clinical risk factors for assessing the preoperative histopathological grade of bladder cancer according to computed tomography (CT) images.
Journal of X-ray science and technology
Dec 18, 2024
BACKGROUND: Computed tomography angiography (CTA) provides significant information on image quality in vascular imaging, thus offering high-resolution images despite having the disadvantages of increased radiation doses and contrast agent-related sid...
OBJECTIVES: We evaluated the noise reduction effects of deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) in brain computed tomography (CT).
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