What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Computer vision
Jinyi Qi mainly investigates Iterative reconstruction, Algorithm, Artificial intelligence, Positron emission tomography and Image quality.
His Iterative reconstruction research incorporates elements of Image resolution, Inverse problem, Pixel, Maximum a posteriori estimation and Likelihood function.
Jinyi Qi has researched Image resolution in several fields, including Detector and Scanner.
He interconnects Mathematical optimization, Tomography, Direct methods and Parametric statistics in the investigation of issues within Algorithm.
His studies deal with areas such as Smoothing and Computer vision as well as Artificial intelligence.
Jinyi Qi focuses mostly in the field of Image quality, narrowing it down to topics relating to Pattern recognition and, in certain cases, Artificial neural network.
His most cited work include:
- High-resolution 3D Bayesian image reconstruction using the microPET small-animal scanner (568 citations)
- Resolution and noise properties of MAP reconstruction for fully 3-D PET (344 citations)
- Simultaneous Acquisition of Multislice PET and MR Images: Initial Results with a MR-Compatible PET Scanner (319 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Iterative reconstruction, Artificial intelligence, Computer vision, Algorithm and Detector.
His Iterative reconstruction research is multidisciplinary, relying on both Image resolution, Maximum a posteriori estimation, Positron emission tomography, Tomography and Monte Carlo method.
His studies examine the connections between Artificial intelligence and genetics, as well as such issues in Pattern recognition, with regards to Artificial neural network, Noise reduction, Inverse problem and Noise.
As a part of the same scientific family, Jinyi Qi mostly works in the field of Computer vision, focusing on Regularization and, on occasion, Penalty method.
His biological study spans a wide range of topics, including Mathematical optimization, Parametric statistics, Reconstruction algorithm and Smoothing.
The various areas that he examines in his Detector study include Positron emission mammography, Scanner and Point source.
He most often published in these fields:
- Iterative reconstruction (57.50%)
- Artificial intelligence (46.25%)
- Computer vision (34.58%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (46.25%)
- Iterative reconstruction (57.50%)
- Image quality (20.83%)
In recent papers he was focusing on the following fields of study:
Jinyi Qi mainly focuses on Artificial intelligence, Iterative reconstruction, Image quality, Computer vision and Scanner.
Jinyi Qi works mostly in the field of Artificial intelligence, limiting it down to concerns involving Pattern recognition and, occasionally, Medical imaging, Noise reduction, Noise and Parametric statistics.
He has included themes like Positron emission tomography, Regularization, Algorithm and Standard deviation in his Iterative reconstruction study.
Jinyi Qi has researched Image quality in several fields, including Feature extraction and PET-CT, Computed tomography.
The concepts of his Scanner study are interwoven with issues in Imaging phantom, Nuclear medicine, Detector and Biomedical engineering.
His Detector research incorporates themes from Image resolution, Monte Carlo method and Sensitivity.
Between 2015 and 2021, his most popular works were:
- Total-Body PET: Maximizing Sensitivity to Create New Opportunities for Clinical Research and Patient Care. (213 citations)
- Iterative PET Image Reconstruction Using Convolutional Neural Network Representation (103 citations)
- PET Image Denoising Using a Deep Neural Network Through Fine Tuning (67 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer vision
Jinyi Qi mostly deals with Artificial intelligence, Iterative reconstruction, Image quality, Positron emission tomography and Pattern recognition.
His work focuses on many connections between Artificial intelligence and other disciplines, such as Computer vision, that overlap with his field of interest in Point source.
His Iterative reconstruction study deals with Noise reduction intersecting with Inverse problem.
Jinyi Qi focuses mostly in the field of Image quality, narrowing it down to matters related to Scanner and, in some cases, Detector, Biomedical engineering, Whole body imaging and Monte Carlo method.
His study on Scintillator is often connected to Materials science as part of broader study in Detector.
His study connects Image resolution and Positron emission tomography.
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