What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Magnetic resonance imaging
- Algorithm
Zhi-Pei Liang focuses on Algorithm, Artificial intelligence, Iterative reconstruction, Computer vision and Mathematical optimization.
His Algorithm research includes elements of Context and Rank.
His research on Artificial intelligence frequently links to adjacent areas such as Magnetic resonance imaging.
Zhi-Pei Liang has included themes like Fourier transform and Compressed sensing in his Iterative reconstruction study.
Zhi-Pei Liang has researched Computer vision in several fields, including Dimension, Matrix and Encoding.
His Mathematical optimization research integrates issues from Bloch equations, Topology, Flip angle and Signal processing.
His most cited work include:
- Prefrontal regions play a predominant role in imposing an attentional ‘set’: evidence from fMRI (295 citations)
- SPATIOTEMPORAL IMAGINGWITH PARTIALLY SEPARABLE FUNCTIONS (259 citations)
- Robust water/fat separation in the presence of large field inhomogeneities using a graph cut algorithm. (224 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Iterative reconstruction, Computer vision, Algorithm and Image resolution.
His Artificial intelligence research is multidisciplinary, relying on both Magnetic resonance imaging and Pattern recognition.
Zhi-Pei Liang interconnects Subspace topology, Encoding, Imaging phantom, Fourier transform and Signal in the investigation of issues within Iterative reconstruction.
His study in Computer vision is interdisciplinary in nature, drawing from both Cardiac imaging, Temporal resolution and Series.
His work investigates the relationship between Algorithm and topics such as Mathematical optimization that intersect with problems in Spectral density estimation.
His Image resolution research is multidisciplinary, incorporating elements of Sampling and Frame rate.
He most often published in these fields:
- Artificial intelligence (52.67%)
- Iterative reconstruction (41.22%)
- Computer vision (37.40%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (52.67%)
- Iterative reconstruction (41.22%)
- Data acquisition (12.98%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Iterative reconstruction, Data acquisition, Pattern recognition and Subspace topology.
His Artificial intelligence research includes themes of Magnetic resonance imaging and Computer vision.
His biological study spans a wide range of topics, including Low-rank approximation and Convolutional neural network.
His studies deal with areas such as Fourier series, Decoding methods and Fourier transform as well as Iterative reconstruction.
His Pattern recognition study combines topics in areas such as Voxel and Compressed sensing.
The study incorporates disciplines such as Mr spectroscopic imaging, Spice, Encoding and Algorithm in addition to Subspace topology.
Between 2014 and 2021, his most popular works were:
- Accelerated MR parameter mapping with low-rank and sparsity constraints. (107 citations)
- Accelerated High-Dimensional MR Imaging With Sparse Sampling Using Low-Rank Tensors (65 citations)
- High-resolution 1H-MRSI of the brain using SPICE: Data acquisition and image reconstruction (52 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Magnetic resonance imaging
- Algorithm
Data acquisition, Artificial intelligence, Subspace topology, Image resolution and Pattern recognition are his primary areas of study.
While the research belongs to areas of Artificial intelligence, Zhi-Pei Liang spends his time largely on the problem of Computer vision, intersecting his research to questions surrounding Low-rank approximation.
His Subspace topology research incorporates elements of Spice and Algorithm.
His Algorithm study combines topics from a wide range of disciplines, such as Magnetic resonance spectroscopic imaging and Mathematical optimization.
The concepts of his Image resolution study are interwoven with issues in Sampling, Nuclear magnetic resonance and Tensor.
His work carried out in the field of Pattern recognition brings together such families of science as Resolution and Partial separability.
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