What is he best known for?
The fields of study he is best known for:
- Magnetic resonance imaging
- Artificial intelligence
- Quantum mechanics
Mark A. Griswold mainly focuses on Artificial intelligence, Nuclear magnetic resonance, Computer vision, Magnetic resonance imaging and Parallel imaging.
His study on Iterative reconstruction is often connected to Data acquisition as part of broader study in Artificial intelligence.
His work carried out in the field of Nuclear magnetic resonance brings together such families of science as Sensitivity, Imaging phantom, Radiofrequency coil, Signal and Precession.
His biological study spans a wide range of topics, including Acceleration, Calibration, Electromagnetic coil and Aliasing.
His study in Magnetic resonance imaging is interdisciplinary in nature, drawing from both Scan time, Extended phase, Pulse sequence and Robustness.
His Parallel imaging study combines topics from a wide range of disciplines, such as Phantom studies, Undersampling and Image acquisition.
His most cited work include:
- Generalized autocalibrating partially parallel acquisitions (GRAPPA). (3879 citations)
- Magnetic resonance fingerprinting (744 citations)
- Magnetic resonance fingerprinting (744 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Magnetic resonance imaging, Nuclear magnetic resonance, Artificial intelligence, Computer vision and Signal.
His Magnetic resonance imaging research integrates issues from Imaging phantom, Nuclear medicine, Pulse sequence, Algorithm and Biomedical engineering.
His studies in Nuclear magnetic resonance integrate themes in fields like Image resolution, Optics, Sample, Phase and Electromagnetic coil.
His work in the fields of Iterative reconstruction overlaps with other areas such as Data acquisition and Radial trajectory.
In his study, Calibration and Spiral is inextricably linked to Parallel imaging, which falls within the broad field of Computer vision.
His study in the field of Sampling also crosses realms of Set.
He most often published in these fields:
- Magnetic resonance imaging (36.83%)
- Nuclear magnetic resonance (30.18%)
- Artificial intelligence (29.67%)
What were the highlights of his more recent work (between 2018-2021)?
- Magnetic resonance imaging (36.83%)
- Imaging phantom (14.32%)
- Artificial intelligence (29.67%)
In recent papers he was focusing on the following fields of study:
Mark A. Griswold spends much of his time researching Magnetic resonance imaging, Imaging phantom, Artificial intelligence, Nuclear medicine and Pattern recognition.
His work deals with themes such as Acoustics, Biological system, Nuclear magnetic resonance and Image resolution, which intersect with Magnetic resonance imaging.
His Imaging phantom study incorporates themes from Phase, Single scan, Estimation theory, Biomedical engineering and Relaxation.
In his work, Catheter is strongly intertwined with Computer vision, which is a subfield of Artificial intelligence.
His studies deal with areas such as Stage, Relaxometry, Normal breast and Receiver operating characteristic as well as Nuclear medicine.
In general Pattern recognition study, his work on Pattern recognition often relates to the realm of Quantitative imaging and Data dictionary, thereby connecting several areas of interest.
Between 2018 and 2021, his most popular works were:
- Optimal Experiment Design for Magnetic Resonance Fingerprinting: Cramér-Rao Bound Meets Spin Dynamics (39 citations)
- Magnetic resonance fingerprinting: a technical review. (39 citations)
- Magnetic resonance fingerprinting: a technical review. (39 citations)
In his most recent research, the most cited papers focused on:
- Magnetic resonance imaging
- Quantum mechanics
- Artificial intelligence
Imaging phantom, Magnetic resonance imaging, Nuclear medicine, Artificial intelligence and Pattern recognition are his primary areas of study.
The concepts of his Imaging phantom study are interwoven with issues in Phase, MEDLINE, Estimation theory, Medical physics and Signal.
His Magnetic resonance imaging study frequently draws connections to other fields, such as Precession.
His Nuclear medicine research incorporates elements of Stage, Relaxometry and Normal breast.
He incorporates Artificial intelligence and Implementation in his studies.
His Pattern recognition study, which is part of a larger body of work in Pattern recognition, is frequently linked to Quantitative imaging, bridging the gap between disciplines.
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