What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Algorithm
Kawin Setsompop mostly deals with Nuclear magnetic resonance, Artificial intelligence, Iterative reconstruction, Computer vision and Algorithm.
His biological study spans a wide range of topics, including Radio frequency, Excitation, Scanner, Electromagnetic coil and Biomedical engineering.
His Artificial intelligence research includes elements of Machine learning, Relaxometry, Computation and Pattern recognition.
The Iterative reconstruction study combines topics in areas such as Echo-planar imaging, Mathematical optimization, Parallel imaging and Quantitative susceptibility mapping.
Kawin Setsompop has included themes like Phase, Signal, Aliasing and Connectome in his Computer vision study.
His research investigates the connection between Algorithm and topics such as Diffusion MRI that intersect with issues in Noise floor, Signal-to-noise ratio, Compressed sensing and Voxel.
His most cited work include:
- Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. (880 citations)
- Pushing the limits of in vivo diffusion MRI for the Human Connectome Project (319 citations)
- Ultra-fast MRI of the human brain with simultaneous multi-slice imaging. (297 citations)
What are the main themes of his work throughout his whole career to date?
Kawin Setsompop focuses on Artificial intelligence, Algorithm, Computer vision, Diffusion MRI and Encoding.
His work carried out in the field of Artificial intelligence brings together such families of science as Resolution and Pattern recognition.
His Algorithm research incorporates elements of Subspace topology, Phase and Quantitative susceptibility mapping.
As a member of one scientific family, he mostly works in the field of Phase, focusing on Aliasing and, on occasion, Nuclear magnetic resonance.
His Computer vision study combines topics in areas such as Simultaneous multislice, Parallel imaging and Echo-planar imaging.
The concepts of his Diffusion MRI study are interwoven with issues in Image resolution, Voxel, Noise reduction and Scanner.
He most often published in these fields:
- Artificial intelligence (53.22%)
- Algorithm (39.48%)
- Computer vision (34.76%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (53.22%)
- Diffusion MRI (22.75%)
- Computer vision (34.76%)
In recent papers he was focusing on the following fields of study:
Kawin Setsompop mainly investigates Artificial intelligence, Diffusion MRI, Computer vision, Image quality and Algorithm.
He combines subjects such as Resolution, Human brain and Pattern recognition with his study of Artificial intelligence.
His Diffusion MRI study integrates concerns from other disciplines, such as High fidelity, Spherical mean, White matter and Scanner.
The study incorporates disciplines such as Robustness, Echo planar, Bold fmri and Flip angle in addition to Computer vision.
His Algorithm study incorporates themes from Subspace topology, Phase, Encoding, Distortion and Undersampling.
In Encoding, Kawin Setsompop works on issues like Voxel, which are connected to Structural imaging.
Between 2019 and 2021, his most popular works were:
- High-fidelity, high-isotropic-resolution diffusion imaging through gSlider acquisition with B 1 + and T 1 corrections and integrated ΔB 0 /Rx shim array. (18 citations)
- Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM) (17 citations)
- Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM) (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Algorithm
Artificial intelligence, Deep learning, Diffusion MRI, Algorithm and Pattern recognition are his primary areas of study.
He performs integrative Artificial intelligence and Data acquisition research in his work.
Kawin Setsompop studied Deep learning and Image quality that intersect with Leverage, Gradient descent and Quantitative susceptibility mapping.
His Diffusion MRI study also includes fields such as
- Noise reduction together with Tractography, Diffusion Tractography and Spatial analysis,
- Scanner, which have a strong connection to Image resolution, Voxel and Shim.
His studies deal with areas such as Signal and Encoding as well as Algorithm.
His work deals with themes such as Subspace topology, Phase, Reduction, Distortion and Undersampling, which intersect with Encoding.
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