What is he best known for?
The fields of study he is best known for:
- Internal medicine
- Magnetic resonance imaging
- Quantum mechanics
His primary areas of investigation include Quantitative susceptibility mapping, Magnetic resonance imaging, Nuclear magnetic resonance, Imaging phantom and Pathology.
His Quantitative susceptibility mapping research integrates issues from Magnetic susceptibility, Magnetic field, Dipole and Algorithm.
His study in Algorithm is interdisciplinary in nature, drawing from both Artificial intelligence and Computer vision.
His Magnetic resonance imaging study integrates concerns from other disciplines, such as Image enhancement, Neuroscience and Nuclear medicine.
His study looks at the relationship between Nuclear magnetic resonance and fields such as Magnetic dipole, as well as how they intersect with chemical problems.
The various areas that he examines in his Pathology study include Multiple sclerosis and Pancreas.
His most cited work include:
- Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: validation and application to brain imaging. (464 citations)
- Respiratory motion of the heart: kinematics and the implications for the spatial resolution in coronary imaging (441 citations)
- Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker. (425 citations)
What are the main themes of his work throughout his whole career to date?
Yi Wang mainly focuses on Quantitative susceptibility mapping, Magnetic resonance imaging, Nuclear medicine, Nuclear magnetic resonance and Artificial intelligence.
His research in Quantitative susceptibility mapping intersects with topics in Lesion, Pathology, Gradient echo, Imaging phantom and Multiple sclerosis.
His Magnetic resonance imaging study deals with the bigger picture of Radiology.
His studies deal with areas such as Proton therapy, Digital subtraction angiography, Angiography, Steady-state free precession imaging and Receiver operating characteristic as well as Nuclear medicine.
As part of his studies on Nuclear magnetic resonance, Yi Wang often connects relevant subjects like Magnetic susceptibility.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Computer vision and Pattern recognition.
He most often published in these fields:
- Quantitative susceptibility mapping (33.21%)
- Magnetic resonance imaging (25.99%)
- Nuclear medicine (21.12%)
What were the highlights of his more recent work (between 2018-2021)?
- Quantitative susceptibility mapping (33.21%)
- Artificial intelligence (13.36%)
- Pattern recognition (5.05%)
In recent papers he was focusing on the following fields of study:
His main research concerns Quantitative susceptibility mapping, Artificial intelligence, Pattern recognition, Magnetic resonance imaging and Multiple sclerosis.
His Quantitative susceptibility mapping study combines topics in areas such as Nuclear medicine, Pathology, Oxygen extraction, Internal medicine and Nuclear magnetic resonance.
The study incorporates disciplines such as Healthy volunteers, Area under the curve and Receiver operating characteristic in addition to Nuclear medicine.
The concepts of his Nuclear magnetic resonance study are interwoven with issues in Substantia nigra, Magnetic susceptibility and Gradient echo.
To a larger extent, Yi Wang studies Radiology with the aim of understanding Magnetic resonance imaging.
His Multiple sclerosis research integrates issues from Lesion, Diffusion MRI and White matter.
Between 2018 and 2021, his most popular works were:
- Quantitative Susceptibility Mapping Identifies Inflammation in a Subset of Chronic Multiple Sclerosis Lesions (40 citations)
- Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction (22 citations)
- 3D texture analyses within the substantia nigra of Parkinson's disease patients on quantitative susceptibility maps and R2∗ maps. (22 citations)
In his most recent research, the most cited papers focused on:
- Internal medicine
- Magnetic resonance imaging
- Quantum mechanics
His primary scientific interests are in Quantitative susceptibility mapping, Magnetic resonance imaging, Artificial intelligence, Nuclear medicine and Multiple sclerosis.
His Quantitative susceptibility mapping research incorporates themes from Mr imaging, Pathology, Region of interest, Reproducibility and Nuclear magnetic resonance.
His Magnetic resonance imaging research is multidisciplinary, incorporating perspectives in Context, Medical physics and Neuroradiology.
He has included themes like Computer vision and Pattern recognition in his Artificial intelligence study.
In general Nuclear medicine study, his work on Diagnostic quality often relates to the realm of High resolution, thereby connecting several areas of interest.
His research investigates the link between Multiple sclerosis and topics such as Lesion that cross with problems in Translocator protein, Brain lesions, Attention network and Convolutional neural network.
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