What is he best known for?
The fields of study he is best known for:
- Magnetic resonance imaging
- Optics
- Artificial intelligence
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.
His most cited work include:
- Generalized image combinations in dual KVP digital radiography. (592 citations)
- Iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) : application with fast spin-echo imaging (551 citations)
- Multicoil Dixon chemical species separation with an iterative least-squares estimation method. (461 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Optics (29.45%)
- Detector (20.08%)
- Artificial intelligence (18.36%)
What were the highlights of his more recent work (between 2012-2021)?
- Detector (20.08%)
- Optics (29.45%)
- Photon counting (7.27%)
In recent papers he was focusing on the following fields of study:
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.
Between 2012 and 2021, his most popular works were:
- Photon-counting CT: Technical Principles and Clinical Prospects. (138 citations)
- The feasibility of a piecewise-linear dynamic bowtie filter (60 citations)
- Recent and Future Directions in CT Imaging (44 citations)
In his most recent research, the most cited papers focused on:
- Optics
- Magnetic resonance imaging
- 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.
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