Objective: The objective of our study was to assess the utility of dual-energy CT for characterizing renal masses using iodine overlay techniques and virtual unenhanced images and to measure the potential radiation dose reduction for two-phase kidney CT compared with a standard three-phase protocol.
Materials And Methods: Sixty patients with suspected renal masses underwent dual-energy CT including true unenhanced, dual-energy corticomedullary, and dual-energy late nephrographic phase imaging. Iodine overlay and virtual unenhanced images were derived from the corticomedullary and late nephrographic phases, respectively. The CT numbers of renal masses were calculated using the iodine overlay images superimposed on the virtual unenhanced images. The overall imaging quality of the true unenhanced images and of the virtual unenhanced images was also evaluated. The effective radiation doses for dual-energy CT and for true unenhanced imaging were calculated.
Results: For overlay or enhancement values on iodine overlay images, 36 simple cysts and 10 hemorrhagic cysts had an attenuation value of less than 20 HU, whereas 21 renal cell carcinomas showed an attenuation value of 20 HU or greater. Eleven angiomyolipomas contained macroscopic fat tissue. All renal masses were accurately classified on the basis of dual-energy CT. The imaging quality of the virtual unenhanced images from the corticomedullary and late nephrographic phases was inferior to the image quality of the true unenhanced images (p < 0.01). The mean effective doses for the three-phase protocol and for true unenhanced images were 12.6 and 2.4 mSv, respectively.
Conclusion: Our results show that dual-energy CT using iodine overlay techniques and virtual unenhanced images may be useful for characterizing renal masses.
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http://dx.doi.org/10.2214/AJR.11.6922 | DOI Listing |
Insights Imaging
January 2025
Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
Introduction: A large number of middle-aged and elderly patients have an insufficient understanding of osteoporosis and its harm. This study aimed to establish and validate a convolutional neural network (CNN) model based on unenhanced chest computed tomography (CT) images of the vertebral body and skeletal muscle for opportunistic screening in patients with osteoporosis.
Materials And Methods: Our team retrospectively collected clinical information from participants who underwent unenhanced chest CT and dual-energy X-ray absorptiometry (DXA) examinations between January 1, 2022, and December 31, 2022, at four hospitals.
Purpose: With the widespread introduction of dual energy computed tomography (DECT), applications utilizing the spectral information to perform material decomposition became available. Among these, a popular application is to decompose contrast-enhanced CT images into virtual non-contrast (VNC) or virtual non-iodine images and into iodine maps. In 2021, photon-counting CT (PCCT) was introduced, which is another spectral CT modality.
View Article and Find Full Text PDFObjectives The primary objective of this study is to describe and evaluate the diagnostic performance of the hyperdense right hemidiaphragm sign (HRHS) as a novel radiological indicator for diffuse fatty infiltration of the liver on non-enhanced CT (NECT) scans. This includes assessing its sensitivity, specificity, positive predictive value, and negative predictive value, and comparing these metrics with other established NECT signs. Methods This cross-sectional multicenter retrospective study included all patients over 12 years of age who underwent both abdominal MRI and NECT scans of the abdomen within a period not exceeding six months at two tertiary hospitals (The Royal Hospital and Armed Forces Hospital, Muscat, Sultanate of Oman) between January 2010 and December 2022.
View Article and Find Full Text PDFAesthetic Plast Surg
January 2025
Division of Plastic Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, 110 Francis Street Suite 5A, Boston, MA, 02215, USA.
Background: Artificial intelligence (AI) technologies use a three-part strategy for facial visual enhancement: (1) Facial Detection, (2) Facial Landmark Detection, and (3) Filter Application (Chen in Arch Fac Plast Surg 21:361-367, 2019). In the context of the surgical patient population, open-source AI algorithms are capable of modifying or simulating images to present potential results of plastic surgery procedures. Our primary aim was to understand whether AI filter use may influence individuals' perceptions and expectations of post-surgical outcomes.
View Article and Find Full Text PDFJ Comput Assist Tomogr
November 2024
From the Department of Radiology and Radiological Science, Divisions of Cardiovascular and Thoracic Imaging, Medical University of South Carolina. Charleston, SC.
Background: The latest generation of computed tomography (CT) systems based on photon-counting detector promises significant improvements in several clinical applications, including chest imaging.
Purpose: The aim of the study is to evaluate the image quality of ultra-high-resolution (UHR) photon-counting detector CT (PCD-CT) of the lung using four sharp reconstruction kernels.
Material And Methods: This retrospective study included 25 patients (11 women and 14 men; median age, 71 years) who underwent unenhanced chest CT from April to May 2023.
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