What is he best known for?
The fields of study he is best known for:
- Magnetic resonance imaging
- Radiology
- Internal medicine
Krishna S. Nayak mostly deals with Magnetic resonance imaging, Artificial intelligence, Computer vision, Nuclear magnetic resonance and Adipose tissue.
His research integrates issues of Medical physics and Pathology in his study of Magnetic resonance imaging.
Krishna S. Nayak combines subjects such as Algorithm, Noise and Microphone with his study of Artificial intelligence.
He has researched Computer vision in several fields, including Perspective and Speech production.
The concepts of his Nuclear magnetic resonance study are interwoven with issues in Imaging phantom, Cardiac imaging and Signal.
His work on White adipose tissue as part of general Adipose tissue research is often related to Fat content, thus linking different fields of science.
His most cited work include:
- k-t FOCUSS: a general compressed sensing framework for high resolution dynamic MRI. (557 citations)
- Saturated double-angle method for rapid B1+ mapping. (275 citations)
- An approach to real-time magnetic resonance imaging for speech production. (249 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Magnetic resonance imaging, Computer vision, Nuclear magnetic resonance and Internal medicine.
His work deals with themes such as Real-time MRI and Speech production, which intersect with Artificial intelligence.
His Magnetic resonance imaging research incorporates elements of Myocardial perfusion imaging, Nuclear medicine, Pathology, Adipose tissue and Biomedical engineering.
The study incorporates disciplines such as Temporal resolution and Vocal tract in addition to Computer vision.
His Nuclear magnetic resonance study combines topics from a wide range of disciplines, such as Steady-state free precession imaging, Imaging phantom, Image resolution and Signal.
His Internal medicine research is multidisciplinary, incorporating perspectives in Diabetes mellitus, Endocrinology and Cardiology.
He most often published in these fields:
- Artificial intelligence (25.83%)
- Magnetic resonance imaging (25.42%)
- Computer vision (23.33%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (25.83%)
- Computer vision (23.33%)
- Real-time MRI (13.33%)
In recent papers he was focusing on the following fields of study:
Krishna S. Nayak mainly investigates Artificial intelligence, Computer vision, Real-time MRI, Spiral and Magnetic resonance imaging.
He frequently studies issues relating to Pattern recognition and Artificial intelligence.
In his articles, he combines various disciplines, including Computer vision and Rank.
His research in Real-time MRI intersects with topics in Speech production, Vocal tract and Artifact.
His Magnetic resonance imaging research includes themes of Imaging phantom, Ischemia and Pathology.
While the research belongs to areas of Imaging phantom, Krishna S. Nayak spends his time largely on the problem of Luminance, intersecting his research to questions surrounding Nuclear magnetic resonance.
Between 2018 and 2021, his most popular works were:
- Dynamic off-resonance correction for spiral real-time MRI of speech. (15 citations)
- 3D dynamic MRI of the vocal tract during natural speech. (13 citations)
- Improved velocity-selective labeling pulses for myocardial ASL. (6 citations)
In his most recent research, the most cited papers focused on:
- Magnetic resonance imaging
- Internal medicine
- Artificial intelligence
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Real-time MRI, Perfusion and Speech production.
His research on Artificial intelligence frequently links to adjacent areas such as Field.
Krishna S. Nayak performs integrative Computer vision and Spiral research in his work.
His studies in Real-time MRI integrate themes in fields like Image quality, Context and Metric.
His work carried out in the field of Perfusion brings together such families of science as Communication noise, Pulse wave and Arterial blood.
His Speech production research incorporates themes from Phase sensitive, Cued speech, Phase, American English and Contrast-to-noise ratio.
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