ID: 394
THREE-DIMENSIONAL OSTEOCYTE LACUNO-CANALICULAR NETWORK AT THE BONE IMPLANT INTERFACE
K. Abouzaid1, T. Reiss2, H. Albini-Lomami1, G. Haïat1, E. Vennat2, S. Le Cann1
1CNRS, MSME UMR 8208, France; 2Université Paris-Saclay, CentraleSupélec, ENS Paris-Saclay, CNRS, LMPS, France
ID: 663
A CONTRAST-ENHANCED X-RAY IMAGING APPROACH FOR CHARACTERIZING ARTICULAR CARTILAGE
S. Fantoni1, M. Berni2, M. Assenza2, P. Cardarelli3, A. Taibi4, C. Trapella5, V. Cristofori5, F. Baruffaldi2, N. F. Lopomo6, M. Baleani2
1Department of Industrial Engineering, University of Bologna, Italy; 2Medical Technology Laboratory, IRCCS Istituto Ortopedico Rizzoli, Italy; 3Istituto Nazionale di Fisica Nucleare (INFN), Division of Ferrara, Italy; 4Department of Physics and Earth Sciences, University of Ferrara, Italy; 5Department of Chemical, Pharmaceutical and Agricultural Sciences, University of Ferrara, Italy; 6Department of Information Engineering, University of Brescia, Italy
ID: 940
4D CT AS A TOOL TO MEASURE SCAPHOLUNATE DISTANCE: AN INTRA-AND INTEROBSERVER EVALUATION
S. Goeminne1, E. Salaets1, W. Coudyzer1, D. Shah2, I. Degreef1, L. Scheys1
1KULeuven, Belgium; 2ITT, India
ID: 692
MUSCLE DIFFUSION TENSOR IMAGING: INFLUENCE OF SEGMENTATION ON THE DETERMINATION OF MUSCLE ARCHITECTURE
S. Vetter1, H.-P. Köhler1, M. Witt1, J. Henkelmann2, C. Roth2
1Leipzig University, Germany; 2Universitätsklinikum Leipzig, Germany
ID: 438
NEURAL RADIANCE FIELDS FOR VESSEL RECONSTRUCTION FROM 2D X-RAY CORONARY ANGIOGRAPHY PROJECTIONS – PROOF OF CONCEPT
A. J. E. Vermeer1,2, K. W. H. Maas3, P.-J.J. Vlaar2, F. N. Vosse1, M. van 't Veer1,2
1Department of Biomedical Engineering, Eindhoven University of Technology; 2Department of Cardiology, Catharina Hospital Eindhoven; 3Department of Mathematics and Computer Science, Eindhoven University of Technology
ID: 824
CORONARY ARTERY SEGMENTATION IN HYPEREMIA CONDITIONS FOR COMPUTED FFR ANALYSIS
J. Festas1,2, L. C. Sousa1,2, C. C. António1,2, S. Silva3, S. I. Pinto1,2
1Engineering Faculty, University of Porto, Porto, Portugal; 2Institute of Science and Innovation in Mechanical and Industrial Engineering (LAETA-INEGI), Porto, Portugal; 3Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro, Aveiro, Portugal
ID: 860
DEEP LEARNING THORACIC AORTA SEGMENTATION FOR FEATURE EXTRACTION AND HEMODYNAMIC ANALYSIS FROM 3D PC-MRI
S. Garzia1,2, M. A. Scarpolini1,3, K. Capellini1, V. Positano1, F. Cademartiri4, S. Celi1
1BioCardioLab - Fondazione Toscana Gabriele Monasterio, Italy; 2Department of Information Engineering, University of Pisa, Italy; 3Department of Industrial Engineering, University of Rome "Tor Vergata", Roma, Italy; 4Clinical Imaging Department, Fondazione Toscana Gabriele Monasterio, Italy
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