What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Magnetic resonance imaging
- Algorithm
Michael Lustig mainly focuses on Compressed sensing, Artificial intelligence, Algorithm, Computer vision and Nuclear magnetic resonance.
His Compressed sensing research incorporates elements of Image quality, Imaging phantom, Magnetic resonance imaging, Parallel imaging and Undersampling.
Wavelet is the focus of his Artificial intelligence research.
He has researched Wavelet in several fields, including Sparse approximation and Aliasing.
His Algorithm research is multidisciplinary, incorporating perspectives in Cartesian coordinate system and Lasso.
He studies Computer vision, focusing on Iterative reconstruction in particular.
His most cited work include:
- Sparse MRI: The application of compressed sensing for rapid MR imaging. (4863 citations)
- An Interior-Point Method for Large-Scale $ll_1$ -Regularized Least Squares (1552 citations)
- Compressed Sensing MRI (1440 citations)
What are the main themes of his work throughout his whole career to date?
Michael Lustig mostly deals with Compressed sensing, Artificial intelligence, Computer vision, Algorithm and Magnetic resonance imaging.
His research in Compressed sensing intersects with topics in Regularization, Undersampling, Nuclear magnetic resonance, Wavelet and Iterative reconstruction.
Within one scientific family, Michael Lustig focuses on topics pertaining to Pattern recognition under Artificial intelligence, and may sometimes address concerns connected to Artificial neural network.
His Computer vision research integrates issues from Temporal resolution and Free breathing.
His work in Algorithm addresses subjects such as Cartesian coordinate system, which are connected to disciplines such as k-space and Computation.
His Magnetic resonance imaging research focuses on Image quality and how it relates to Noise.
He most often published in these fields:
- Compressed sensing (37.42%)
- Artificial intelligence (37.42%)
- Computer vision (34.19%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (37.42%)
- Computer vision (34.19%)
- Compressed sensing (37.42%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Computer vision, Compressed sensing, Algorithm and Iterative reconstruction.
His research investigates the connection between Artificial intelligence and topics such as Dynamic contrast-enhanced MRI that intersect with problems in Inference, Errors-in-variables models, Pattern recognition and Ground truth.
His Computer vision study incorporates themes from Temporal resolution and Free breathing.
His studies deal with areas such as Kernel, Magnetic resonance imaging and Inverse problem as well as Compressed sensing.
His Algorithm research incorporates themes from Calibration, Cartesian coordinate system, Parallel imaging and Sensitivity.
His studies in Iterative reconstruction integrate themes in fields like Nyquist rate, Sampling, Computational science, Image formation and Wavelet transform.
Between 2017 and 2021, his most popular works were:
- Motion robust high resolution 3D free-breathing pulmonary MRI using dynamic 3D image self-navigator. (25 citations)
- Motion robust high resolution 3D free-breathing pulmonary MRI using dynamic 3D image self-navigator. (25 citations)
- General phase regularized reconstruction using phase cycling. (20 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Magnetic resonance imaging
- Algorithm
His main research concerns Artificial intelligence, Computer vision, Algorithm, Image quality and Magnetic resonance imaging.
His Artificial intelligence study integrates concerns from other disciplines, such as Single shot and k-space.
His study in the field of Motion compensation also crosses realms of High resolution.
His Algorithm study also includes
- Parallel imaging and related Deconvolution, Nonlinear system, Nyquist frequency and Iterative reconstruction,
- Sensitivity which connect with Trajectory, Image resolution, Cartesian coordinate system, Rank and Imaging phantom.
Michael Lustig interconnects Acoustics, Transducer, Noise and Beam in the investigation of issues within Magnetic resonance imaging.
In his study, Reconstruction algorithm, Phase cycling, Invariant, Reconstruction method and Pattern recognition is inextricably linked to Inverse problem, which falls within the broad field of Compressed sensing.
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