4 Works
Multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy CT, diffusion-weighted MRI, and radiomics
Vitali Koch, Nils Weitzer, Daniel Pinto Dos Santos, Leon D. Gruenewald, Scherwin Mahmoudi, Simon S. Martin, Katrin Eichler, Simon Bernatz, Tatjana Gruber-Rouh, Christian Booz, Renate M. Hammerstingl, Teodora Biciusca, Nicolas Rosbach, Aynur Gökduman, Tommaso D’Angelo, Fabian Finkelmeier, Ibrahim Yel, Leona S. Alizadeh, Christof M. Sommer, Duygu Cengiz, Thomas J. Vogl & Moritz H. Albrecht
Abstract Background The advent of next-generation computed tomography (CT)- and magnetic resonance imaging (MRI) opened many new perspectives in the evaluation of tumor characteristics. An increasing body of evidence suggests the incorporation of quantitative imaging biomarkers into clinical decision-making to provide mineable tissue information. The present study sought to evaluate the diagnostic and predictive value of a multiparametric approach involving radiomics texture analysis, dual-energy CT-derived iodine concentration (DECT-IC), and diffusion-weighted MRI (DWI) in participants with...
Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment
Marco Penso, Mario Babbaro, Sara Moccia, Marco Guglielmo, Maria Ludovica Carerj, Carlo Maria Giacari, Mattia Chiesa, Riccardo Maragna, Mark G. Rabbat, Andrea Barison, Nicola Martini, Mauro Pepi, Enrico G. Caiani & Gianluca Pontone
Abstract Background Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted...
Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment
Marco Penso, Mario Babbaro, Sara Moccia, Marco Guglielmo, Maria Ludovica Carerj, Carlo Maria Giacari, Mattia Chiesa, Riccardo Maragna, Mark G. Rabbat, Andrea Barison, Nicola Martini, Mauro Pepi, Enrico G. Caiani & Gianluca Pontone
Abstract Background Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted...
Multiparametric detection and outcome prediction of pancreatic cancer involving dual-energy CT, diffusion-weighted MRI, and radiomics
Vitali Koch, Nils Weitzer, Daniel Pinto Dos Santos, Leon D. Gruenewald, Scherwin Mahmoudi, Simon S. Martin, Katrin Eichler, Simon Bernatz, Tatjana Gruber-Rouh, Christian Booz, Renate M. Hammerstingl, Teodora Biciusca, Nicolas Rosbach, Aynur Gökduman, Tommaso D’Angelo, Fabian Finkelmeier, Ibrahim Yel, Leona S. Alizadeh, Christof M. Sommer, Duygu Cengiz, Thomas J. Vogl & Moritz H. Albrecht
Abstract Background The advent of next-generation computed tomography (CT)- and magnetic resonance imaging (MRI) opened many new perspectives in the evaluation of tumor characteristics. An increasing body of evidence suggests the incorporation of quantitative imaging biomarkers into clinical decision-making to provide mineable tissue information. The present study sought to evaluate the diagnostic and predictive value of a multiparametric approach involving radiomics texture analysis, dual-energy CT-derived iodine concentration (DECT-IC), and diffusion-weighted MRI (DWI) in participants with...
Affiliations
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Azienda Ospedaliera Universitaria Policlinico "G. Martino"4
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Centro Cardiologico Monzino2
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Koç University2
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University Hospital Heidelberg2
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Politecnico di Milano2
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Edward Hines, Jr. VA Hospital2
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Sant'Anna School of Advanced Studies2
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Institute of Molecular Science and Technologies2
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University Hospital Frankfurt2
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Loyola University Chicago2