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Deep Learning Superresolution for Simultaneous Multislice Parallel Imaging-Accelerated Knee MRI Using Arthroscopy Validation.
Radiology
January 2025
From the Department of Radiology, Division of Musculoskeletal Radiology, NYU Grossman School of Medicine, 660 1st Ave, 3rd Fl, Rm 313, New York, NY 10016 (S.S.W., J.V., R.K., E.H.P., J.F.); Department for Diagnostic and Interventional Radiology, Eberhard Karls University Tübingen, University Hospital Tübingen, Tübingen, Germany (S.S.W.); Department of Radiology, University Hospital Basel, Basel, Switzerland (J.V.); Department of Radiology, Hospital do Coraçao, São Paulo, Brazil (T.C.R.); Academic Surgical Unit, South West London Elective Orthopaedic Centre (SWLEOC), London, United Kingdom (D.D.); Department of Radiology, Balgrist University Hospital, Zurich, Switzerland (B.F.); Department of Radiology, Jeonbuk National University Hospital, Jeonju, Republic of Korea (E.H.P.); Research Institute of Clinical Medicine of Jeonbuk National University, Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea (E.H.P.); Medscanlagos Radiology, Cabo Frio, Brazil (A.S.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Siemens Healthineers AG, Erlangen, Germany (I.B.); and Siemens Medical Solutions USA, Malvern, Pa (G.K.).
Background Deep learning (DL) methods can improve accelerated MRI but require validation against an independent reference standard to ensure robustness and accuracy. Purpose To validate the diagnostic performance of twofold-simultaneous-multislice (SMSx2) twofold-parallel-imaging (PIx2)-accelerated DL superresolution MRI in the knee against conventional SMSx2-PIx2-accelerated MRI using arthroscopy as the reference standard. Materials and Methods Adults with painful knee conditions were prospectively enrolled from December 2021 to October 2022.
View Article and Find Full Text PDFCorrection: Kuklane et al. A Database of Static Thermal Insulation and Evaporative Resistance Values of Dutch Firefighter Clothing Items and Ensembles. 2022, , 1813.
Biology (Basel)
January 2025
Team Fire Service Science, Netherlands Academy of Crisis Management and Fire Service Science, Netherlands Institute for Public Safety, Zilverstraat 91, 2718 RP Zoetermeer, The Netherlands.
The original publication contained an erroneous data line in Appendix A, "Table A1 [...
View Article and Find Full Text PDFEvaluation of MRX PT DOAC as a new screening method for detecting interferences in thrombophilia analyses.
Scand J Clin Lab Invest
January 2025
Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.
Background: Direct oral anticoagulants (DOACs) can interfere with coagulation analyses, causing erroneous results such as false-positive lupus anticoagulant and false-normal antithrombin, threatening patient safety when overlooked. A test using a prothrombin time quotient method to detect DOAC presence in plasma samples is now commercially available, the MRX PT DOAC, with the result expressed as Clot Time Ratio (CTR).
Objectives: Evaluate the ability of MRX PT DOAC to identify interfering apixaban or rivaroxaban concentrations, identify non-interfering or interfering patient samples, and detect whether a patient is on DOAC treatment.
Geometric Feature Characterization of Apple Trees from 3D LiDAR Point Cloud Data.
J Imaging
December 2024
Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea.
The geometric feature characterization of fruit trees plays a role in effective management in orchards. LiDAR (light detection and ranging) technology for object detection enables the rapid and precise evaluation of geometric features. This study aimed to quantify the height, canopy volume, tree spacing, and row spacing in an apple orchard using a three-dimensional (3D) LiDAR sensor.
View Article and Find Full Text PDFAutomated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AI.
Bioengineering (Basel)
January 2025
Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea.
Recent advancements in deep learning have significantly improved medical image segmentation. However, the generalization performance and potential risks of data-driven models remain insufficiently validated. Specifically, unrealistic segmentation predictions deviating from actual anatomical structures, known as a Seg-Hallucination, often occur in deep learning-based models.
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