2020 - Fellow, National Academy of Inventors
2017 - SPIE Fellow
2012 - Member of the National Academy of Engineering For development of algorithms and technologies for MRI, CT, and hybrid X-ray/MRI imaging.
2007 - Fellow of the Indian National Academy of Engineering (INAE)
Norbert J. Pelc mostly deals with Magnetic resonance imaging, Blood flow, Nuclear magnetic resonance, Optics and Artificial intelligence. His Magnetic resonance imaging research incorporates themes from Plane, Contrast, Spiral and Cardiac cycle. His Blood flow research integrates issues from Flow, Magnetic resonance angiography, Nuclear medicine, Aortic valve and Hemodynamics.
His study in Nuclear magnetic resonance is interdisciplinary in nature, drawing from both Steady-state free precession imaging, Imaging phantom, Distortion and Pulsatile flow. The Optics study combines topics in areas such as Orientation and Noise. His studies in Artificial intelligence integrate themes in fields like Periodic function, Simulation, Temporal resolution and Computer vision.
Optics, Detector, Artificial intelligence, Computer vision and Magnetic resonance imaging are his primary areas of study. His Artificial intelligence study combines topics from a wide range of disciplines, such as Fourier transform and Temporal resolution. Norbert J. Pelc interconnects Nuclear medicine, Nuclear magnetic resonance, Biomedical engineering and Cardiac cycle in the investigation of issues within Magnetic resonance imaging.
His work carried out in the field of Nuclear medicine brings together such families of science as Hemodynamics and Blood flow, Radiology. His work focuses on many connections between Nuclear magnetic resonance and other disciplines, such as Signal, that overlap with his field of interest in Phase. His research in Iterative reconstruction tackles topics such as Algorithm which are related to areas like Noise.
Norbert J. Pelc mainly focuses on Detector, Optics, Photon counting, Energy and Imaging phantom. His research in Detector intersects with topics in Pixel, Monte Carlo method, Detective quantum efficiency and Photon. Norbert J. Pelc studied Optics and Piecewise linear function that intersect with Scanner.
His Imaging phantom research includes elements of Field of view, Filter, Perfusion scanning and Rotation. While the research belongs to areas of Image resolution, Norbert J. Pelc spends his time largely on the problem of Tomography, intersecting his research to questions surrounding Nuclear medicine and Image processing. His Image quality research is under the purview of Artificial intelligence.
His main research concerns Optics, Detector, Attenuator, Photon counting and Monte Carlo method. His Imaging phantom and Dynamic range study in the realm of Optics interacts with subjects such as Collimated light. His Detector study combines topics in areas such as Image resolution, Piecewise linear function, Beam and Projection.
His Image resolution study integrates concerns from other disciplines, such as Image processing, Tomography and Noise. His study with Image processing involves better knowledge in Artificial intelligence. His Attenuator research is multidisciplinary, incorporating perspectives in Image quality and Algorithm.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Generalized image combinations in dual KVP digital radiography.
L. A. Lehmann;R. E. Alvarez;A. Macovski;W. R. Brody.
Medical Physics (1981)
Phase contrast cine magnetic resonance imaging.
N J Pelc;R J Herfkens;A Shimakawa;D R Enzmann.
Magnetic resonance quarterly (1991)
Iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) : application with fast spin-echo imaging
Scott B. Reeder;Angel R. Pineda;Zhifei Wen;Ann Shimakawa.
Magnetic Resonance in Medicine (2005)
Multicoil Dixon chemical species separation with an iterative least-squares estimation method.
Scott B. Reeder;Zhifei Wen;Huanzhou Yu;Angel R. Pineda.
Magnetic Resonance in Medicine (2004)
Concomitant gradient terms in phase contrast MR: Analysis and correction
Matt A. Bernstein;Xiaohong Joe Zhou;Jason A. Polzin;Kevin F. King.
Magnetic Resonance in Medicine (1998)
Unaliasing by fourier-encoding the overlaps using the temporal dimension (UNFOLD), applied to cardiac imaging and fMRI.
Bruno Madore;Gary H. Glover;Norbert J. Pelc.
Magnetic Resonance in Medicine (1999)
Encoding strategies for three‐direction phase‐contrast MR imaging of flow
Norbert J. Pelc;Matt A. Bernstein;Ann Shimakawa;Gary H. Glover.
Journal of Magnetic Resonance Imaging (1991)
Time-resolved three-dimensional phase-contrast MRI.
Michael Markl;Frandics P. Chan;Marcus T. Alley;Kris L. Wedding.
Journal of Magnetic Resonance Imaging (2003)
Normal flow patterns of intracranial and spinal cerebrospinal fluid defined with phase-contrast cine MR imaging.
D R Enzmann;N J Pelc.
The Anatomy of the Posterior Communicating Artery as a Risk Factor for Ischemic Cerebral Infarction
Don F. Schomer;Michael P. Marks;Gary K. Steinberg;Iain M. Johnstone.
The New England Journal of Medicine (1994)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: