Zobrazeno 1 - 10 of 27 pro vyhledávání: '"CM Scannell"'
2
Akademický článek
Deep Learning Synthesis of White-Blood From Dark-Blood Late Gadolinium Enhancement Cardiac Magnetic Resonance.
Autor: Jaspers TJM; From the Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (T.J.M.J., S.A., M.B., C.M.S.); School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (T.J.M.J., R.C., L.J., R.J.H., A.C., C.M.S.); Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (T.J.M.J.); Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands (B.M., R.J.H.); and Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, the Netherlands (B.M., R.J.H.)., Martens B, Crawley R, Jada L, Amirrajab S, Breeuwer M, Holtackers RJ, Chiribiri A, Scannell CM
Publikováno v: Investigative radiology [Invest Radiol] 2024 Nov 01; Vol. 59 (11), pp. 767-773. Date of Electronic Publication: 2024 May 01.
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3
Akademický článek
Qualitative American Heart Association plot of late gadolinium enhancement with mortality and ventricular arrhythmia prediction using artificial intelligence.
Autor: Alskaf E; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK., Scannell CM; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.; Eindhoven University of Technology, Eindhoven, The Netherlands., Suinesiaputra A; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK., Crawley R; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK., Masci P; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK., Young A; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK., Perera D; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK., Chiribiri A; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Publikováno v: Journal of medical artificial intelligence [J Med Artif Intell] 2025 Mar; Vol. 8, pp. 2.
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4
Akademický článek
Automated inversion time selection for late gadolinium-enhanced cardiac magnetic resonance imaging.
Autor: Xie C; Melbourne Bioinnovation Student Initiative (MBSI), Parkville, VIC, Australia.; Department of Radiology, Artificial Intelligence in Radiology Laboratory, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia., Zhang R; Melbourne Bioinnovation Student Initiative (MBSI), Parkville, VIC, Australia.; Department of Radiology, Artificial Intelligence in Radiology Laboratory, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia., Mensink S; Melbourne Bioinnovation Student Initiative (MBSI), Parkville, VIC, Australia.; Department of Radiology, Artificial Intelligence in Radiology Laboratory, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia., Gandharva R; Melbourne Bioinnovation Student Initiative (MBSI), Parkville, VIC, Australia.; Department of Radiology, Artificial Intelligence in Radiology Laboratory, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia., Awni M; Melbourne Bioinnovation Student Initiative (MBSI), Parkville, VIC, Australia.; Department of Radiology, Artificial Intelligence in Radiology Laboratory, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia., Lim H; Melbourne Bioinnovation Student Initiative (MBSI), Parkville, VIC, Australia.; Department of Radiology, Artificial Intelligence in Radiology Laboratory, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia., Kachel SE; Department of Radiology, Artificial Intelligence in Radiology Laboratory, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia.; Melbourne Medical School, The University of Melbourne, Parkville, VIC, Australia., Cheung E; Department of Radiology, Artificial Intelligence in Radiology Laboratory, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia., Crawley R; King's College London, Strand, London, UK., Churilov L; Melbourne Medical School, The University of Melbourne, Parkville, VIC, Australia., Bettencourt N; University of Porto, Porto, Portugal., Chiribiri A; King's College London, Strand, London, UK., Scannell CM; King's College London, Strand, London, UK.; Eindhoven University of Technology, Eindhoven, the Netherlands., Lim RP; Department of Radiology, Artificial Intelligence in Radiology Laboratory, Austin Health, 145 Studley Rd, Heidelberg, VIC, 3084, Australia. ruthplim74@gmail.com.; Melbourne Medical School, The University of Melbourne, Parkville, VIC, Australia. ruthplim74@gmail.com.
Publikováno v: European radiology [Eur Radiol] 2024 Sep; Vol. 34 (9), pp. 5816-5828. Date of Electronic Publication: 2024 Feb 10.
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5
Akademický článek
Automated cardiovascular MR myocardial scar quantification with unsupervised domain adaptation.
Autor: Crawley R; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK., Amirrajab S; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands., Lustermans D; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands., Holtackers RJ; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands.; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands., Plein S; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK., Veta M; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands., Breeuwer M; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands., Chiribiri A; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK., Scannell CM; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK. cian.scannell@kcl.ac.uk.; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands. cian.scannell@kcl.ac.uk.
Publikováno v: European radiology experimental [Eur Radiol Exp] 2024 Aug 14; Vol. 8 (1), pp. 93. Date of Electronic Publication: 2024 Aug 14.
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6
Editorial & Opinion
The intelligent imaging revolution: artificial intelligence in MRI and MRS acquisition and reconstruction.
Autor: Küstner T; Medical Image and Data Analysis (MIDAS.Lab), Diagnostic and Interventional Radiology, University Hospital of Tuebingen, 72076, Tuebingen, Germany. thomas.kuestner@med.uni-tuebingen.de., Qin C; Department of Electrical and Electronic Engineering, I-X Imperial College London, London, UK., Sun C; Department of Chemical and Biomedical Engineering, Department of Radiology, University of Missouri-Columbia, 65201, Columbia, USA., Ning L; Brigham and Women' s Hospital, 02215, Boston, USA., Scannell CM; Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
Publikováno v: Magma (New York, N.Y.) [MAGMA] 2024 Jul; Vol. 37 (3), pp. 329-333. Date of Electronic Publication: 2024 Jun 20.
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7
Akademický článek
High-resolution free-breathing automated quantitative myocardial perfusion by cardiovascular magnetic resonance for the detection of functionally significant coronary artery disease.
Autor: Crawley R; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK., Kunze KP; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK.; Magnetic Resonance Research Collaborations, Siemens Healthcare Limited, Camberley, UK., Milidonis X; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK.; DeepCamera MRG, CYENS Centre of Excellence, Nicosia, Cyprus., Highton J; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK.; Aival, London, UK., McElroy S; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK.; Magnetic Resonance Research Collaborations, Siemens Healthcare Limited, Camberley, UK., Frey SM; Department of Cardiology, University Hospital Basel, Basel, Switzerland., Hoefler D; Department of Radiotherapy, University of Erlangen, Erlangen, Germany., Karamanli C; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK., Wong NCK; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK., Backhaus SJ; Department of Cardiology, Campus Kerckhoff of the Justus-Liebig-University Giessen, Kerckhoff-Clinic, Bad Nauheim, Germany., Alskaf E; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK., Neji R; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK., Scannell CM; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK.; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands., Plein S; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK.; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK., Chiribiri A; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK.
Publikováno v: European heart journal. Cardiovascular Imaging [Eur Heart J Cardiovasc Imaging] 2024 Jun 28; Vol. 25 (7), pp. 914-925.
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8
Akademický článek
Hybrid artificial intelligence outcome prediction using features extraction from stress perfusion cardiac magnetic resonance images and electronic health records.
Autor: Alskaf E; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, UK., Crawley R; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, UK., Scannell CM; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands., Suinesiaputra A; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, UK., Young A; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, UK., Masci PG; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, UK., Perera D; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, UK., Chiribiri A; School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
Publikováno v: Journal of medical artificial intelligence [J Med Artif Intell] 2024 Mar 30; Vol. 7, pp. 3.
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9
Akademický článek
High-resolution quantification of stress perfusion defects by cardiac magnetic resonance.
Autor: Scannell CM; Department of Biomedical Engineering, Eindhoven University of Technology, Groene Loper 5, 5612 AE Eindhoven, The Netherlands.; School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK., Crawley R; School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK., Alskaf E; School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK., Breeuwer M; Department of Biomedical Engineering, Eindhoven University of Technology, Groene Loper 5, 5612 AE Eindhoven, The Netherlands., Plein S; School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, UK., Chiribiri A; School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK.
Publikováno v: European heart journal. Imaging methods and practice [Eur Heart J Imaging Methods Pract] 2024 Jan 09; Vol. 2 (1), pp. qyae001. Date of Electronic Publication: 2024 Jan 09 (Print Publication: 2024).
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10
Akademický článek
Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records.
Autor: Alskaf E; School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom., Frey SM; School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.; Department of Cardiology, University Hospital Basel, Basel, Switzerland., Scannell CM; School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands., Suinesiaputra A; School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom., Vilic D; King's College London, United Kingdom., Dinu V; King's College London, United Kingdom., Masci PG; School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom., Perera D; School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom., Young A; School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom., Chiribiri A; School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.
Publikováno v: Informatics in medicine unlocked [Inform Med Unlocked] 2024; Vol. 44, pp. 101418.
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