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On the Effect of the Patient Table on Attenuation in Myocardial Perfusion Imaging SPECT.
EJNMMI Phys
January 2025
Institute of Radiology, Nuclear Medicine and Molecular Imaging, Heart and Diabetes Center North Rhine-Westphalia, University Hospital (Ruhr University Bochum), Medical Faculty OWL (Bielefeld University), Bad Oeynhausen, Germany.
Background: The topic of the effect of the patient table on attenuation in myocardial perfusion imaging (MPI) SPECT is gaining new relevance due to deep learning methods. Existing studies on this effect are old, rare and only consider phantom measurements, not patient studies. This study investigates the effect of the patient table on attenuation based on the difference between reconstructions of phantom scans and polar maps of patient studies.
View Article and Find Full Text PDFVolumetry of Selected Brain Regions-Can We Compare MRI Examinations of Different Manufacturers and Field Strengths?
Clin Neuroradiol
January 2025
Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Arnold-Heller-Str. 3, Hs D (Neurozentrum), 24105, Kiel, Germany.
Purpose: Magnetic Resonance Imaging based brain segmentation and volumetry has become an important tool in clinical routine and research. However the impact of the used hardware is only barely investigated. This study aims to assess the influence of scanner manufacturer, field strength and head-coil on volumetry results.
View Article and Find Full Text PDFAI-based automatic patient positioning in a digital-BGO PET/CT scanner: efficacy and impact.
EJNMMI Phys
January 2025
Department of Nuclear Medicine, Rambam Health Care Campus, P.O.B. 9602, 3109601, Haifa, Israel.
Background: A recently released digital solid-state positron emission tomography/x-ray CT (PET/CT) scanner with bismuth germanate (BGO) scintillators provides an artificial intelligence (AI) based system for automatic patient positioning. The efficacy of this digital-BGO system in patient placement at the isocenter and its impact on image quality and radiation exposure was evaluated.
Method: The digital-BGO PET/CT with AI-based auto-positioning was compared (χ, Mann-Whitney tests) to a solid-state lutetium-yttrium oxyorthosilicate (digital-LYSO) PET/CT with manual patient positioning (n = 432 and 343 studies each, respectively), with results split into groups before and after the date of a recalibration of the digital-BGO auto-positioning camera.
AntiBinder: utilizing bidirectional attention and hybrid encoding for precise antibody-antigen interaction prediction.
Brief Bioinform
November 2024
Research Center for Social Intelligence, Fudan University, Handan Street, Shanghai 200433, China.
Antibodies play a key role in medical diagnostics and therapeutics. Accurately predicting antibody-antigen binding is essential for developing effective treatments. Traditional protein-protein interaction prediction methods often fall short because they do not account for the unique structural and dynamic properties of antibodies and antigens.
View Article and Find Full Text PDFAutomatic segmentation of MRI images for brain radiotherapy planning using deep ensemble learning.
Biomed Phys Eng Express
January 2025
Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA, United States of America.
This study aimed to develop and evaluate an efficient method to automatically segment T1- and T2-weighted brain magnetic resonance imaging (MRI) images. We specifically compared the segmentation performance of individual convolutional neural network (CNN) models against an ensemble approach to advance the accuracy of MRI-guided radiotherapy (RT) planning..
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