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| http://dx.doi.org/10.1148/rg.240042 | DOI Listing |
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Intraindividual Comparison of Image Quality Between Low-Dose and Ultra-Low-Dose Abdominal CT With Deep Learning Reconstruction and Standard-Dose Abdominal CT Using Dual-Split Scan.
Invest Radiol
January 2025
From the Department of Radiology, Ulsan University Hospital, Ulsan, Republic of Korea (T.Y.L.); Department of Radiology, University of Ulsan College of Medicine, Seoul, Republic of Korea (T.Y.L.); Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea (J.H.Y., H.K., J.M.L.); Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (J.H.Y., S.H.P., J.M.L.); Department of Radiology, Inje University Busan Paik Hospital, Busan, Republic of Korea (J.Y.P.); Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea (S.H.P.); Department of Radiology, Hanyang University College of Medicine, Seoul, Republic of Korea (C.L.); Division of Biostatistics, Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Republic of Korea (Y.C.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (J.M.L.).
Objective: The aim of this study was to intraindividually compare the conspicuity of focal liver lesions (FLLs) between low- and ultra-low-dose computed tomography (CT) with deep learning reconstruction (DLR) and standard-dose CT with model-based iterative reconstruction (MBIR) from a single CT using dual-split scan in patients with suspected liver metastasis via a noninferiority design.
Materials And Methods: This prospective study enrolled participants who met the eligibility criteria at 2 tertiary hospitals in South Korea from June 2022 to January 2023. The criteria included (a) being aged between 20 and 85 years and (b) having suspected or known liver metastases.
A comparison of CXR-CAD software to radiologists in identifying COVID-19 in individuals evaluated for Sars CoV-2 infection in Malawi and Zambia.
PLOS Digit Health
January 2025
FIND, Geneva, Switzerland.
AI based software, including computer aided detection software for chest radiographs (CXR-CAD), was developed during the pandemic to improve COVID-19 case finding and triage. In high burden TB countries, the use of highly portable CXR and computer aided detection software has been adopted more broadly to improve the screening and triage of individuals for TB, but there is little evidence in these settings regarding COVID-19 CAD performance. We performed a multicenter, retrospective cross-over study evaluating CXRs from individuals at risk for COVID-19.
View Article and Find Full Text PDFCross-Sectional Validation of an Automated Lesion Segmentation Software in Multiple Sclerosis: Comparison with Radiologist Assessments.
AJNR Am J Neuroradiol
January 2025
From the Department of Radiology, Medical University of South Carolina, Charleston, SC, USA (MVS, HRC, WD, JHC, JAC, MGM, STS, DRR), College of Medicine, Medical University of South Carolina, Charleston, SC, USA (HW, EY).
Background And Purpose: Magnetic Resonance Imaging is widely used to assess disease burden in multiple sclerosis (MS). This study aimed to evaluate the effectiveness of a commercially available k-nearest neighbors (k-NN) software in quantifying white matter lesion (WML) burden in MS. We compared the software's WML quantification to expert radiologists' assessments.
View Article and Find Full Text PDFCritical reflection on the indication for computed tomography: an interdisciplinary survey of risk and benefit management in patients with sepsis.
Insights Imaging
January 2025
Department of Radiology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt- Universität zu Berlin, Charitéplatz 1, Berlin, Germany.
Objectives: To survey physicians' views on the risks and benefits of computed tomography (CT) in the management of septic patients and indications for and contraindications to contrast media use in searching for septic foci.
Methods: A web-based questionnaire was administered to physicians at a large European university medical center in January 2022. A total of 371 questionnaires met the inclusion criteria and were analyzed with physicians' work experience, workplace, and medical specialty as independent variables.
Generation of high-resolution MPRAGE-like images from 3D head MRI localizer (AutoAlign Head) images using a deep learning-based model.
Jpn J Radiol
January 2025
Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan.
Purpose: Magnetization prepared rapid gradient echo (MPRAGE) is a useful three-dimensional (3D) T1-weighted sequence, but is not a priority in routine brain examinations. We hypothesized that converting 3D MRI localizer (AutoAlign Head) images to MPRAGE-like images with deep learning (DL) would be beneficial for diagnosing and researching dementia and neurodegenerative diseases. We aimed to establish and evaluate a DL-based model for generating MPRAGE-like images from MRI localizers.
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