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Minerva Urol Nephrol
January 2025
Department of Urology, University of Verona, A.O.U.I. Verona, Italy.
Insights Imaging
January 2025
Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Objectives: To investigate the image quality and diagnostic performance with ultra-low dose dual-layer detector spectral CT (DLSCT) by various reconstruction techniques for evaluation of pulmonary nodules.
Materials And Methods: Between April 2023 and December 2023, patients with suspected pulmonary nodules were prospectively enrolled and underwent regular-dose chest CT (RDCT; 120 kVp/automatic tube current) and ultra-low dose CT (ULDCT; 100 kVp/10 mAs) on a DLSCT scanner. ULDCT was reconstructed with hybrid iterative reconstruction (HIR), electron density map (EDM), and virtual monoenergetic images at 40 keV and 70 keV.
Insights Imaging
January 2025
Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Objectives: To evaluate the value of contrast-enhanced CT in diagnosing ultrasonography-unspecified adnexal torsion (AT).
Methods: Surgically confirmed patients with painful pelvic masses (n = 165) were retrospectively collected from two institutes. Two senior radiologists independently reviewed the CT images and determined the Hounsfield unit difference between non-contrast vs portal venous phases (ΔHU) in both derivation and validation samples.
Insights Imaging
January 2025
Department of Radiology, Radiation Oncology and Medical Physics Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
Objective: To determine the feasibility, yield, and safety of fluoroscopic-guided aspiration of the acutely dislocated total hip arthroplasty (AD-THA).
Materials And Methods: IRB-approved, retrospective review of fluoroscopic-guided aspirations of AD-THA (January 2005-December 2023) was performed. Data from electronic charts and fluoroscopy images/reports were obtained.
Eur Radiol
January 2025
Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.
Objectives: The use of deep learning models for quantitative measurements on coronary computed tomography angiography (CCTA) may reduce inter-reader variability and increase efficiency in clinical reporting. This study aimed to investigate the diagnostic performance of a recently updated deep learning model (CorEx-2.0) for quantifying coronary stenosis, compared separately with two expert CCTA readers as references.
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