J
J.H.C. Reiber
Researcher at Leiden University Medical Center
Publications - 132
Citations - 3488
J.H.C. Reiber is an academic researcher from Leiden University Medical Center. The author has contributed to research in topics: Image segmentation & Coronary artery disease. The author has an hindex of 33, co-authored 132 publications receiving 3395 citations.
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Journal ArticleDOI
Magnetic resonance imaging during dobutamine stress for detection and localization of coronary artery disease. Quantitative wall motion analysis using a modification of the centerline method.
F. P. Van Rugge,E. E. van der Wall,S. J. Spanjersberg,A. de Roos,Niels A. A. Matheijssen,A. H. Zwinderman,P. R. M. Van Dijkman,J.H.C. Reiber,A. V. G. Bruschke +8 more
TL;DR: Dobutamine MRI is an accurate method for detection and localization of myocardial ischemia and may emerge as a new noninvasive approach for evaluation of patients with known or suspected coronary artery disease.
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Diagnostic accuracy of 320-row multidetector computed tomography coronary angiography in the non-invasive evaluation of significant coronary artery disease
Fleur R. de Graaf,Joanne D. Schuijf,Joëlla E. van Velzen,Lucia J.M. Kroft,Albert de Roos,J.H.C. Reiber,Eric Boersma,Martin J. Schalij,Fabrizio Spanó,J. Wouter Jukema,Ernst E. van der Wall,Jeroen J. Bax +11 more
TL;DR: The current study shows that 320-row CTA allows accurate non-invasive assessment of significant CAD and sensitivity, specificity, and positive and negative predictive values to detect > or =50% luminal narrowing on a patient basis were 100, 81, 88, and 100%, respectively.
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Does the quantitative assessment of coronary artery dimensions predict the physiologic significance of a coronary stenosis
TL;DR: In coronary artery disease the consequent reduction in coronary flow reserve can be predicted with reasonable accuracy by quantitative assessment of coronary artery dimensions, based on a theoretical pressure-flow relationship inferred.
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A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering
TL;DR: An unsupervised image segmentation technique is presented, which combines pyramidal images segmentation with the fuzzy c-means clustering algorithm, which shows good performance in detecting LV lumen in MR images.
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Head-to-head comparison of coronary plaque evaluation between multislice computed tomography and intravascular ultrasound radiofrequency data analysis.
Gabija Pundziute,Joanne D. Schuijf,J. Wouter Jukema,Isabel Decramer,Giovanna Sarno,Piet K. Vanhoenacker,J.H.C. Reiber,Martin J. Schalij,William Wijns,Jeroen J. Bax +9 more
TL;DR: A head-to-head comparison of plaque observations with multislice computed tomography to virtual histology intravascular ultrasound (VH IVUS) found a good correlation was observed between calcium quantification on MSCT and VH IV US.