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http://dx.doi.org/10.21037/qims-2020-27 | DOI Listing |
J Formos Med Assoc
January 2025
Department of Radiology, National Taiwan University Hospital, Taipei, Taiwan.
Contrast media are essential agents that enhance the diagnostic capabilities of imaging studies, such as computed tomography and magnetic resonance imaging. However, concerns regarding the risk of adverse events have led to cautious use in patients with chronic kidney disease. A multidisciplinary review by nephrologists, cardiologists, and radiologists at National Taiwan University Hospital examined evidence linking iodinated contrast media and gadolinium-based contrast agents with acute kidney injury and nephrogenic systemic fibrosis.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Radiation Oncology, Henry Ford Health, Detroit, MI, USA.
Automatic segmentation of angiographic structures can aid in assessing vascular disease. While recent deep learning models promise automation, they lack validation on interventional angiographic data. This study investigates the feasibility of angiographic segmentation using in-context learning with the UniverSeg model, which is a cross-learning segmentation model that lacks inherent angiographic training.
View Article and Find Full Text PDFJ Med Imaging (Bellingham)
January 2025
Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States.
Purpose: We investigated the feasibility and advantages of using non-contrast CT calcium score (CTCS) images to assess pericoronary adipose tissue (PCAT) and its association with major adverse cardiovascular events (MACE). PCAT features from coronary computed tomography angiography (CCTA) have been shown to be associated with cardiovascular risk but are potentially confounded by iodine. If PCAT in CTCS images can be similarly analyzed, it would avoid this issue and enable its inclusion in formal risk assessment from readily available, low-cost CTCS images.
View Article and Find Full Text PDFJ Med Imaging (Bellingham)
January 2025
U.S. Food and Drug Administration, Office of Science and Engineering Labs, Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, Maryland, United States.
Purpose: We evaluate the impact of charge summing correction on a cadmium telluride (CdTe)-based photon-counting detector in breast computed tomography (CT).
Approach: We employ a custom-built laboratory benchtop system using the X-THOR FX30 0.75-mm CdTe detector (Varex Imaging, Salt Lake City, Utah, United States) with a pixel pitch of 0.
Med Phys
January 2025
Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, Hokkaido, Japan.
Background: The use of iodinated contrast-enhancing agents in computed tomography (CT) improves the visualization of relevant structures for radiotherapy treatment planning (RTP). However, it can lead to dose calculation errors by incorrectly converting a CT number to electron density.
Purpose: This study aimed to propose an algorithm for deriving virtual non-contrast (VNC) electron density from dual-energy CT (DECT) data.
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